1
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Tom G, Schmid SP, Baird SG, Cao Y, Darvish K, Hao H, Lo S, Pablo-García S, Rajaonson EM, Skreta M, Yoshikawa N, Corapi S, Akkoc GD, Strieth-Kalthoff F, Seifrid M, Aspuru-Guzik A. Self-Driving Laboratories for Chemistry and Materials Science. Chem Rev 2024; 124:9633-9732. [PMID: 39137296 PMCID: PMC11363023 DOI: 10.1021/acs.chemrev.4c00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.
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Affiliation(s)
- Gary Tom
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Stefan P. Schmid
- Department
of Chemistry and Applied Biosciences, ETH
Zurich, Vladimir-Prelog-Weg 1, CH-8093 Zurich, Switzerland
| | - Sterling G. Baird
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Yang Cao
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Kourosh Darvish
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Han Hao
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Stanley Lo
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Sergio Pablo-García
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
| | - Ella M. Rajaonson
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Marta Skreta
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Naruki Yoshikawa
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Samantha Corapi
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Gun Deniz Akkoc
- Forschungszentrum
Jülich GmbH, Helmholtz Institute
for Renewable Energy Erlangen-Nürnberg, Cauerstr. 1, 91058 Erlangen, Germany
- Department
of Chemical and Biological Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Egerlandstr. 3, 91058 Erlangen, Germany
| | - Felix Strieth-Kalthoff
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- School of
Mathematics and Natural Sciences, University
of Wuppertal, Gaußstraße
20, 42119 Wuppertal, Germany
| | - Martin Seifrid
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Department
of Materials Science and Engineering, North
Carolina State University, Raleigh, North Carolina 27695, United States of America
| | - Alán Aspuru-Guzik
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
- Department
of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
- Department
of Materials Science & Engineering, University of Toronto, Toronto, Ontario M5S 3E4, Canada
- Lebovic
Fellow, Canadian Institute for Advanced
Research (CIFAR), 661
University Ave, Toronto, Ontario M5G 1M1, Canada
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2
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Mervin L, Voronov A, Kabeshov M, Engkvist O. QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design. J Chem Inf Model 2024; 64:5365-5374. [PMID: 38950185 DOI: 10.1021/acs.jcim.4c00457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Machine-learning (ML) and deep-learning (DL) approaches to predict the molecular properties of small molecules are increasingly deployed within the design-make-test-analyze (DMTA) drug design cycle to predict molecular properties of interest. Despite this uptake, there are only a few automated packages to aid their development and deployment that also support uncertainty estimation, model explainability, and other key aspects of model usage. This represents a key unmet need within the field, and the large number of molecular representations and algorithms (and associated parameters) means it is nontrivial to robustly optimize, evaluate, reproduce, and deploy models. Here, we present QSARtuna, a molecule property prediction modeling pipeline, written in Python and utilizing the Optuna, Scikit-learn, RDKit, and ChemProp packages, which enables the efficient and automated comparison between molecular representations and machine learning models. The platform was developed by considering the increasingly important aspect of model uncertainty quantification and explainability by design. We provide details for our framework and provide illustrative examples to demonstrate the capability of the software when applied to simple molecular property, reaction/reactivity prediction, and DNA encoded library enrichment classification. We hope that the release of QSARtuna will further spur innovation in automatic ML modeling and provide a platform for education of best practices in molecular property modeling. The code for the QSARtuna framework is made freely available via GitHub.
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Affiliation(s)
- Lewis Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge CB2 0AA, United Kingdom
| | - Alexey Voronov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
| | - Mikhail Kabeshov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
- Department of Computer Science and Engineering, University of Gothenburg, Chalmers University of Technology, Gothenburg 412 96, Sweden
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3
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Özçelik R, de Ruiter S, Criscuolo E, Grisoni F. Chemical language modeling with structured state space sequence models. Nat Commun 2024; 15:6176. [PMID: 39039051 PMCID: PMC11263548 DOI: 10.1038/s41467-024-50469-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 07/05/2024] [Indexed: 07/24/2024] Open
Abstract
Generative deep learning is reshaping drug design. Chemical language models (CLMs) - which generate molecules in the form of molecular strings - bear particular promise for this endeavor. Here, we introduce a recent deep learning architecture, termed Structured State Space Sequence (S4) model, into de novo drug design. In addition to its unprecedented performance in various fields, S4 has shown remarkable capabilities to learn the global properties of sequences. This aspect is intriguing in chemical language modeling, where complex molecular properties like bioactivity can 'emerge' from separated portions in the molecular string. This observation gives rise to the following question: Can S4 advance chemical language modeling for de novo design? To provide an answer, we systematically benchmark S4 with state-of-the-art CLMs on an array of drug discovery tasks, such as the identification of bioactive compounds, and the design of drug-like molecules and natural products. S4 shows a superior capacity to learn complex molecular properties, while at the same time exploring diverse scaffolds. Finally, when applied prospectively to kinase inhibition, S4 designs eight of out ten molecules that are predicted as highly active by molecular dynamics simulations. Taken together, these findings advocate for the introduction of S4 into chemical language modeling - uncovering its untapped potential in the molecular sciences.
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Affiliation(s)
- Rıza Özçelik
- Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Utrecht, The Netherlands
| | - Sarah de Ruiter
- Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Emanuele Criscuolo
- Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Francesca Grisoni
- Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
- Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Utrecht, The Netherlands.
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4
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Catacutan DB, Alexander J, Arnold A, Stokes JM. Machine learning in preclinical drug discovery. Nat Chem Biol 2024:10.1038/s41589-024-01679-1. [PMID: 39030362 DOI: 10.1038/s41589-024-01679-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 06/13/2024] [Indexed: 07/21/2024]
Abstract
Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of more than 90%. Machine learning (ML) presents an opportunity to improve the drug-discovery process. Indeed, with the growing abundance of public and private large-scale biological and chemical datasets, ML techniques are becoming well positioned as useful tools that can augment the traditional drug-development process. In this Perspective, we discuss the integration of algorithmic methods throughout the preclinical phases of drug discovery. Specifically, we highlight an array of ML-based efforts, across diverse disease areas, to accelerate initial hit discovery, mechanism-of-action (MOA) elucidation and chemical property optimization. With advances in the application of ML across diverse therapeutic areas, we posit that fully ML-integrated drug-discovery pipelines will define the future of drug-development programs.
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Affiliation(s)
- Denise B Catacutan
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada
| | - Jeremie Alexander
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada
| | - Autumn Arnold
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada
| | - Jonathan M Stokes
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada.
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada.
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5
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Atz K, Nippa DF, Müller AT, Jost V, Anelli A, Reutlinger M, Kramer C, Martin RE, Grether U, Schneider G, Wuitschik G. Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry. RSC Med Chem 2024; 15:2310-2321. [PMID: 39026644 PMCID: PMC11253849 DOI: 10.1039/d4md00196f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 05/25/2024] [Indexed: 07/20/2024] Open
Abstract
Suzuki cross-coupling reactions are considered a valuable tool for constructing carbon-carbon bonds in small molecule drug discovery. However, the synthesis of chemical matter often represents a time-consuming and labour-intensive bottleneck. We demonstrate how machine learning methods trained on high-throughput experimentation (HTE) data can be leveraged to enable fast reaction condition selection for novel coupling partners. We show that the trained models support chemists in determining suitable catalyst-solvent-base combinations for individual transformations including an evaluation of the need for HTE screening. We introduce an algorithm for designing 96-well plates optimized towards reaction yields and discuss the model performance of zero- and few-shot machine learning. The best-performing machine learning model achieved a three-category classification accuracy of 76.3% (±0.2%) and an F 1-score for a binary classification of 79.1% (±0.9%). Validation on eight reactions revealed a receiver operating characteristic (ROC) curve (AUC) value of 0.82 (±0.07) for few-shot machine learning. On the other hand, zero-shot machine learning models achieved a mean ROC-AUC value of 0.63 (±0.16). This study positively advocates the application of few-shot machine learning-guided reaction condition selection for HTE campaigns in medicinal chemistry and highlights practical applications as well as challenges associated with zero-shot machine learning.
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Affiliation(s)
- Kenneth Atz
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - David F Nippa
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Alex T Müller
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Vera Jost
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Andrea Anelli
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Michael Reutlinger
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Christian Kramer
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Rainer E Martin
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Uwe Grether
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich Vladimir-Prelog-Weg 4 8093 Zurich Switzerland
| | - Georg Wuitschik
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
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6
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Thomas M, Ahmad M, Tresadern G, de Fabritiis G. PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models. J Cheminform 2024; 16:77. [PMID: 38965600 PMCID: PMC11225391 DOI: 10.1186/s13321-024-00866-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 06/04/2024] [Indexed: 07/06/2024] Open
Abstract
SMILES-based generative models are amongst the most robust and successful recent methods used to augment drug design. They are typically used for complete de novo generation, however, scaffold decoration and fragment linking applications are sometimes desirable which requires a different grammar, architecture, training dataset and therefore, re-training of a new model. In this work, we describe a simple procedure to conduct constrained molecule generation with a SMILES-based generative model to extend applicability to scaffold decoration and fragment linking by providing SMILES prompts, without the need for re-training. In combination with reinforcement learning, we show that pre-trained, decoder-only models adapt to these applications quickly and can further optimize molecule generation towards a specified objective. We compare the performance of this approach to a variety of orthogonal approaches and show that performance is comparable or better. For convenience, we provide an easy-to-use python package to facilitate model sampling which can be found on GitHub and the Python Package Index.Scientific contributionThis novel method extends an autoregressive chemical language model to scaffold decoration and fragment linking scenarios. This doesn't require re-training, the use of a bespoke grammar, or curation of a custom dataset, as commonly required by other approaches.
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Affiliation(s)
- Morgan Thomas
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aguiader 88, 08003, Barcelona, Spain.
| | - Mazen Ahmad
- In Silico Discovery, Janssen Pharmaceutica N. V., Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Gary Tresadern
- In Silico Discovery, Janssen Pharmaceutica N. V., Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Gianni de Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aguiader 88, 08003, Barcelona, Spain.
- Acellera Labs, C Dr. Trueta 183, 08005, Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010, Barcelona, Spain.
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7
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Hao Y, Wang H, Liu X, Gai W, Hu S, Liu W, Miao Z, Gan Y, Yu X, Shi R, Tan Y, Kang T, Hai A, Zhao Y, Fu Y, Tang Y, Ye L, Liu J, Liang X, Ke B. Deep simulated annealing for the discovery of novel dental anesthetics with local anesthesia and anti-inflammatory properties. Acta Pharm Sin B 2024; 14:3086-3109. [PMID: 39027234 PMCID: PMC11252475 DOI: 10.1016/j.apsb.2024.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/04/2024] [Accepted: 01/22/2024] [Indexed: 07/20/2024] Open
Abstract
Multifunctional therapeutics have emerged as a solution to the constraints imposed by drugs with singular or insufficient therapeutic effects. The primary challenge is to integrate diverse pharmacophores within a single-molecule framework. To address this, we introduced DeepSA, a novel edit-based generative framework that utilizes deep simulated annealing for the modification of articaine, a well-known local anesthetic. DeepSA integrates deep neural networks into metaheuristics, effectively constraining molecular space during compound generation. This framework employs a sophisticated objective function that accounts for scaffold preservation, anti-inflammatory properties, and covalent constraints. Through a sequence of local editing to navigate the molecular space, DeepSA successfully identified AT-17, a derivative exhibiting potent analgesic properties and significant anti-inflammatory activity in various animal models. Mechanistic insights into AT-17 revealed its dual mode of action: selective inhibition of NaV1.7 and 1.8 channels, contributing to its prolonged local anesthetic effects, and suppression of inflammatory mediators via modulation of the NLRP3 inflammasome pathway. These findings not only highlight the efficacy of AT-17 as a multifunctional drug candidate but also highlight the potential of DeepSA in facilitating AI-enhanced drug discovery, particularly within stringent chemical constraints.
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Affiliation(s)
- Yihang Hao
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Haofan Wang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Xianggen Liu
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Wenrui Gai
- Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Shilong Hu
- Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Wencheng Liu
- Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Zhuang Miao
- Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yu Gan
- Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xianghua Yu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Rongjia Shi
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Yongzhen Tan
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Ting Kang
- Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Ao Hai
- Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yi Zhao
- Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yihang Fu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Yaling Tang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Ling Ye
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Jin Liu
- Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xinhua Liang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Bowen Ke
- Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
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8
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Jiang X, Lu L, Li J, Jiang J, Zhang J, Zhou S, Wen H, Cai H, Luo X, Li Z, Wang J, Ju B, Bai R. Synthetically Feasible De Novo Molecular Design of Leads Based on a Reinforcement Learning Model: AI-Assisted Discovery of an Anti-IBD Lead Targeting CXCR4. J Med Chem 2024; 67:10057-10075. [PMID: 38863440 DOI: 10.1021/acs.jmedchem.4c00184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2024]
Abstract
Artificial intelligence (AI) de novo molecular generation provides leads with novel structures for drug discovery. However, the target affinity and synthesizability of the generated molecules present critical challenges for the successful application of AI technology. Therefore, we developed an advanced reinforcement learning model to bridge the gap between the theory of de novo molecular generation and the practical aspects of drug discovery. This model utilizes chemical reaction templates and commercially available building blocks as a starting point and employs forward reaction prediction to generate molecules, while real-time docking and drug-likeness predictions are conducted to ensure synthesizability and drug-likeness. We applied this model to design active molecules targeting the inflammation-related receptor CXCR4 and successfully prepared them according to the AI-proposed synthetic routes. Several molecules exhibited potent anti-CXCR4 and anti-inflammatory activity in subsequent in vitro and in vivo assays. The top-performing compound XVI alleviated symptoms related to inflammatory bowel disease and showed reasonable pharmacokinetic properties.
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Affiliation(s)
- Xiaoying Jiang
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, PR China
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines; Engineering Laboratory of Development and Application of Traditional Chinese Medicines; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, PR China
| | - Liuxin Lu
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, PR China
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines; Engineering Laboratory of Development and Application of Traditional Chinese Medicines; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, PR China
| | - Junjie Li
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, PR China
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines; Engineering Laboratory of Development and Application of Traditional Chinese Medicines; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, PR China
| | - Jing Jiang
- SanOmics AI Co. Ltd., Hangzhou 311103, PR China
| | - Jiapeng Zhang
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, PR China
| | - Shengbin Zhou
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, PR China
| | - Hao Wen
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, PR China
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines; Engineering Laboratory of Development and Application of Traditional Chinese Medicines; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, PR China
| | - Hong Cai
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, PR China
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines; Engineering Laboratory of Development and Application of Traditional Chinese Medicines; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, PR China
| | - Xinyu Luo
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, PR China
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines; Engineering Laboratory of Development and Application of Traditional Chinese Medicines; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, PR China
| | - Zhen Li
- SanOmics AI Co. Ltd., Hangzhou 311103, PR China
| | - Jiahui Wang
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, PR China
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines; Engineering Laboratory of Development and Application of Traditional Chinese Medicines; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, PR China
| | - Bin Ju
- SanOmics AI Co. Ltd., Hangzhou 311103, PR China
| | - Renren Bai
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, PR China
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines; Engineering Laboratory of Development and Application of Traditional Chinese Medicines; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, PR China
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9
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Guo J, Schwaller P. Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning. JACS AU 2024; 4:2160-2172. [PMID: 38938817 PMCID: PMC11200228 DOI: 10.1021/jacsau.4c00066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 06/29/2024]
Abstract
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparent when using oracles that can provide increased predictive accuracy but impose significant computational cost. Consequently, designing molecules that are optimized for such oracles cannot be achieved under a practical computational budget. Molecular generative models based on simplified molecular-input line-entry system (SMILES) have shown remarkable sample efficiency when coupled with reinforcement learning, as demonstrated in the practical molecular optimization (PMO) benchmark. Here, we first show that experience replay drastically improves the performance of multiple previously proposed algorithms. Next, we propose a novel algorithm called Augmented Memory that combines data augmentation with experience replay. We show that scores obtained from oracle calls can be reused to update the model multiple times. We compare Augmented Memory to previously proposed algorithms and show significantly enhanced sample efficiency in an exploitation task, a drug discovery case study requiring both exploration and exploitation, and a materials design case study optimizing explicitly for quantum-mechanical properties. Our method achieves a new state-of-the-art in sample-efficient de novo molecular design, outperforming all of the previously reported methods. The code is available at https://github.com/schwallergroup/augmented_memory.
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Affiliation(s)
- Jeff Guo
- Laboratory
of Artificial Chemical Intelligence (LIAC), Institut des Sciences
et Ingénierie Chimiques, Ecole Polytechnique
Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
- National
Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Philippe Schwaller
- Laboratory
of Artificial Chemical Intelligence (LIAC), Institut des Sciences
et Ingénierie Chimiques, Ecole Polytechnique
Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
- National
Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
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10
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Gangwal A, Lavecchia A. Unleashing the power of generative AI in drug discovery. Drug Discov Today 2024; 29:103992. [PMID: 38663579 DOI: 10.1016/j.drudis.2024.103992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/22/2024] [Accepted: 04/18/2024] [Indexed: 05/04/2024]
Abstract
Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy, Dhule 424001, Maharashtra, India
| | - Antonio Lavecchia
- "Drug Discovery" Laboratory, Department of Pharmacy, University of Naples Federico II, I-80131 Naples, Italy.
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11
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Thribhuvan Reddy D, Grewal I, García Pinzon LF, Latchireddy B, Goraya S, Ali Alansari B, Gadwal A. The Role of Artificial Intelligence in Healthcare: Enhancing Coronary Computed Tomography Angiography for Coronary Artery Disease Management. Cureus 2024; 16:e61523. [PMID: 38957241 PMCID: PMC11218716 DOI: 10.7759/cureus.61523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/02/2024] [Indexed: 07/04/2024] Open
Abstract
This review aims to explore the potential of artificial intelligence (AI) in coronary CT angiography (CCTA), a key tool for diagnosing coronary artery disease (CAD). Because CAD is still a major cause of death worldwide, effective and accurate diagnostic methods are required to identify and manage the condition. CCTA certainly is a noninvasive alternative for diagnosing CAD, but it requires a large amount of data as input. We intend to discuss the idea of incorporating AI into CCTA, which enhances its diagnostic accuracy and operational efficiency. Using such AI technologies as machine learning (ML) and deep learning (DL) tools, CCTA images are automated to perfection and the analysis is significantly refined. It enables the characterization of a plaque, assesses the severity of the stenosis, and makes more accurate risk stratifications than traditional methods, with pinpoint accuracy. Automating routine tasks through AI-driven CCTA will reduce the radiologists' workload considerably, which is a standard benefit of such technologies. More importantly, it would enable radiologists to allocate more time and expertise to complex cases, thereby improving overall patient care. However, the field of AI in CCTA is not without its challenges, which include data protection, algorithm transparency, as well as criteria for standardization encoding. Despite such obstacles, it appears that the integration of AI technology into CCTA in the future holds great promise for keeping CAD itself in check, thereby aiding the fight against this disease and begetting better clinical outcomes and more optimized modes of healthcare. Future research on AI algorithms for CCTA, making ethical use of AI, and thereby overcoming the technical and clinical barriers to widespread adoption of this new tool, will hopefully pave the way for profound AI-driven transformations in healthcare.
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Affiliation(s)
| | - Inayat Grewal
- Department of Medicine, Government Medical College and Hospital, Chandigarh, IND
| | | | | | - Simran Goraya
- Department of Medicine, Kharkiv National Medical University, Kharkiv, UKR
| | | | - Aishwarya Gadwal
- Department of Radiodiagnosis, St. John's Medical College and Hospital, Bengaluru, IND
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12
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Thomas M, O'Boyle NM, Bender A, De Graaf C. MolScore: a scoring, evaluation and benchmarking framework for generative models in de novo drug design. J Cheminform 2024; 16:64. [PMID: 38816825 PMCID: PMC11141043 DOI: 10.1186/s13321-024-00861-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Generative models are undergoing rapid research and application to de novo drug design. To facilitate their application and evaluation, we present MolScore. MolScore already contains many drug-design-relevant scoring functions commonly used in benchmarks such as, molecular similarity, molecular docking, predictive models, synthesizability, and more. In addition, providing performance metrics to evaluate generative model performance based on the chemistry generated. With this unification of functionality, MolScore re-implements commonly used benchmarks in the field (such as GuacaMol, MOSES, and MolOpt). Moreover, new benchmarks can be created trivially. We demonstrate this by testing a chemical language model with reinforcement learning on three new tasks of increasing complexity related to the design of 5-HT2a ligands that utilise either molecular descriptors, 266 pre-trained QSAR models, or dual molecular docking. Lastly, MolScore can be integrated into an existing Python script with just three lines of code. This framework is a step towards unifying generative model application and evaluation as applied to drug design for both practitioners and researchers. The framework can be found on GitHub and downloaded directly from the Python Package Index.Scientific ContributionMolScore is an open-source platform to facilitate generative molecular design and evaluation thereof for application in drug design. This platform takes important steps towards unifying existing benchmarks, providing a platform to share new benchmarks, and improves customisation, flexibility and usability for practitioners over existing solutions.
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Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Noel M O'Boyle
- Computational Chemistry, Nxera Pharma, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Chris De Graaf
- Computational Chemistry, Nxera Pharma, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
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13
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Atz K, Cotos L, Isert C, Håkansson M, Focht D, Hilleke M, Nippa DF, Iff M, Ledergerber J, Schiebroek CCG, Romeo V, Hiss JA, Merk D, Schneider P, Kuhn B, Grether U, Schneider G. Prospective de novo drug design with deep interactome learning. Nat Commun 2024; 15:3408. [PMID: 38649351 PMCID: PMC11035696 DOI: 10.1038/s41467-024-47613-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the "zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.
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Affiliation(s)
- Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Leandro Cotos
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Clemens Isert
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Maria Håkansson
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Dorota Focht
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Mattis Hilleke
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - David F Nippa
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Michael Iff
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Jann Ledergerber
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Carl C G Schiebroek
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Valentina Romeo
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Jan A Hiss
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Daniel Merk
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Petra Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Bernd Kuhn
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Uwe Grether
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
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14
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Ghiandoni GM, Flanagan SR, Bodkin MJ, Nizi MG, Galera-Prat A, Brai A, Chen B, Wallace JEA, Hristozov D, Webster J, Manfroni G, Lehtiö L, Tabarrini O, Gillet VJ. Synthetically accessible de novo design using reaction vectors: Application to PARP1 inhibitors. Mol Inform 2024; 43:e202300183. [PMID: 38258328 DOI: 10.1002/minf.202300183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 01/24/2024]
Abstract
De novo design has been a hotly pursued topic for many years. Most recent developments have involved the use of deep learning methods for generative molecular design. Despite increasing levels of algorithmic sophistication, the design of molecules that are synthetically accessible remains a major challenge. Reaction-based de novo design takes a conceptually simpler approach and aims to address synthesisability directly by mimicking synthetic chemistry and driving structural transformations by known reactions that are applied in a stepwise manner. However, the use of a small number of hand-coded transformations restricts the chemical space that can be accessed and there are few examples in the literature where molecules and their synthetic routes have been designed and executed successfully. Here we describe the application of reaction-based de novo design to the design of synthetically accessible and biologically active compounds as proof-of-concept of our reaction vector-based software. Reaction vectors are derived automatically from known reactions and allow access to a wide region of synthetically accessible chemical space. The design was aimed at producing molecules that are active against PARP1 and which have improved brain penetration properties compared to existing PARP1 inhibitors. We synthesised a selection of the designed molecules according to the provided synthetic routes and tested them experimentally. The results demonstrate that reaction vectors can be applied to the design of novel molecules of biological relevance that are also synthetically accessible.
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Affiliation(s)
- Gian Marco Ghiandoni
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | - Stuart R Flanagan
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - Michael J Bodkin
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - Maria Giulia Nizi
- Department of Pharmaceutical Sciences, University of Perugia, 06123, Perugia, Italy
| | - Albert Galera-Prat
- Faculty of Biochemistry and Molecular Medicine & Biocenter Oulu, University of Oulu, Oulu, FI-90014, Finland
| | - Annalaura Brai
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, I-53100, Siena, Italy
| | - Beining Chen
- Department of Chemistry, University of Sheffield, Dainton Building, Brook Hill, Sheffield, S3 7HF, UK
| | - James E A Wallace
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - Dimitar Hristozov
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - James Webster
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | - Giuseppe Manfroni
- Department of Pharmaceutical Sciences, University of Perugia, 06123, Perugia, Italy
| | - Lari Lehtiö
- Faculty of Biochemistry and Molecular Medicine & Biocenter Oulu, University of Oulu, Oulu, FI-90014, Finland
| | - Oriana Tabarrini
- Department of Pharmaceutical Sciences, University of Perugia, 06123, Perugia, Italy
| | - Valerie J Gillet
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
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15
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Ghiandoni GM, Evertsson E, Riley DJ, Tyrchan C, Rathi PC. Augmenting DMTA using predictive AI modelling at AstraZeneca. Drug Discov Today 2024; 29:103945. [PMID: 38460568 DOI: 10.1016/j.drudis.2024.103945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 03/11/2024]
Abstract
Design-Make-Test-Analyse (DMTA) is the discovery cycle through which molecules are designed, synthesised, and assayed to produce data that in turn are analysed to inform the next iteration. The process is repeated until viable drug candidates are identified, often requiring many cycles before reaching a sweet spot. The advent of artificial intelligence (AI) and cloud computing presents an opportunity to innovate drug discovery to reduce the number of cycles needed to yield a candidate. Here, we present the Predictive Insight Platform (PIP), a cloud-native modelling platform developed at AstraZeneca. The impact of PIP in each step of DMTA, as well as its architecture, integration, and usage, are discussed and used to provide insights into the future of drug discovery.
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Affiliation(s)
- Gian Marco Ghiandoni
- Augmented DMTA Platform, R&D IT, AstraZeneca, The Discovery Centre (DISC), Francis Crick Avenue, Cambridge CB2 0AA, UK.
| | - Emma Evertsson
- Research and Early Development, Respiratory and Immunology (R&I), Biopharmaceuticals R&D, AstraZeneca, Pepparedsleden, Mölndal, SE 43183, Sweden
| | - David J Riley
- Augmented DMTA Platform, R&D IT, AstraZeneca, The Discovery Centre (DISC), Francis Crick Avenue, Cambridge CB2 0AA, UK
| | - Christian Tyrchan
- Research and Early Development, Respiratory and Immunology (R&I), Biopharmaceuticals R&D, AstraZeneca, Pepparedsleden, Mölndal, SE 43183, Sweden
| | - Prakash Chandra Rathi
- Augmented DMTA Platform, R&D IT, AstraZeneca, The Discovery Centre (DISC), Francis Crick Avenue, Cambridge CB2 0AA, UK
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16
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Bai L, Xia Z, Triffitt JT, Su J. Generation artificial intelligence (GenAI) and Biomaterials Translational: steering innovation without misdirection. BIOMATERIALS TRANSLATIONAL 2024; 5:1-2. [PMID: 39220662 PMCID: PMC11362347 DOI: 10.12336/biomatertransl.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Affiliation(s)
- Long Bai
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, China
- Wenzhou Institute of Shanghai University, Wenzhou, Zhejiang Province, China
| | - Zhidao Xia
- Centre for Nanohealth, ILS2, Medical School, Swansea University, Swansea, UK
| | - James T. Triffitt
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, The Oxford University Institute of Musculoskeletal Sciences, The Botnar Research Centre, Nuffield Orthopaedic Centre, Oxford, UK
| | - Jiacan Su
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, China
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, China
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17
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He D, Liu Q, Mi Y, Meng Q, Xu L, Hou C, Wang J, Li N, Liu Y, Chai H, Yang Y, Liu J, Wang L, Hou Y. De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307245. [PMID: 38204214 PMCID: PMC10962488 DOI: 10.1002/advs.202307245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/05/2023] [Indexed: 01/12/2024]
Abstract
One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning-based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning-based Strategy) by using dataset of disease-direct-related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery.
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Affiliation(s)
- Dakuo He
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Qing Liu
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Yan Mi
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Qingqi Meng
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Libin Xu
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Chunyu Hou
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Jinpeng Wang
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Ning Li
- School of Traditional Chinese Materia MedicaKey Laboratory for TCM Material Basis Study and Innovative Drug Development of Shenyang CityShenyang Pharmaceutical UniversityShenyang110016China
| | - Yang Liu
- Key Laboratory of Structure‐Based Drug Design & Discovery of Ministry of EducationShenyang Pharmaceutical UniversityShenyang110016China
| | - Huifang Chai
- School of PharmacyGuizhou University of Traditional Chinese MedicineGuiyang550025China
| | - Yanqiu Yang
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Jingyu Liu
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Lihui Wang
- Department of PharmacologyShenyang Pharmaceutical UniversityShenyang110016China
| | - Yue Hou
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
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18
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Mock M, Langmead CJ, Grandsard P, Edavettal S, Russell A. Recent advances in generative biology for biotherapeutic discovery. Trends Pharmacol Sci 2024; 45:255-267. [PMID: 38378385 DOI: 10.1016/j.tips.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/22/2023] [Accepted: 01/05/2024] [Indexed: 02/22/2024]
Abstract
Generative biology combines artificial intelligence (AI), advanced life sciences technologies, and automation to revolutionize the process of designing novel biomolecules with prescribed properties, giving drug discoverers the ability to escape the limitations of biology during the design of next-generation protein therapeutics. Significant hurdles remain, namely: (i) the inherently complex nature of drug discovery, (ii) the bewildering number of promising computational and experimental techniques that have emerged in the past several years, and (iii) the limited availability of relevant protein sequence-function data for drug-like molecules. There is a need to focus on computational methods that will be most practically effective for protein drug discovery and on building experimental platforms to generate the data most appropriate for these methods. Here, we discuss recent advances in computational and experimental life sciences that are most crucial for impacting the pace and success of protein drug discovery.
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Affiliation(s)
- Marissa Mock
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | | | - Peter Grandsard
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Suzanne Edavettal
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Alan Russell
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA.
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19
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Zhou J, Dong J, Hou H, Huang L, Li J. High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications. LAB ON A CHIP 2024; 24:1307-1326. [PMID: 38247405 DOI: 10.1039/d3lc01012k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.
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Affiliation(s)
- Jianhua Zhou
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jianpei Dong
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Hongwei Hou
- Beijing Life Science Academy, Beijing 102209, China
| | - Lu Huang
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jinghong Li
- Department of Chemistry, Center for BioAnalytical Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Tsinghua University, Beijing 100084, China.
- New Cornerstone Science Laboratory, Shenzhen 518054, China
- Beijing Life Science Academy, Beijing 102209, China
- Center for BioAnalytical Chemistry, Hefei National Laboratory of Physical Science at Microscale, University of Science and Technology of China, Hefei 230026, China
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20
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Dong H, Lin J, Tao Y, Jia Y, Sun L, Li WJ, Sun H. AI-enhanced biomedical micro/nanorobots in microfluidics. LAB ON A CHIP 2024; 24:1419-1440. [PMID: 38174821 DOI: 10.1039/d3lc00909b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad spectrum of microfluidic systems that adopt fundamental roles in orchestrating physiological mechanisms. In vitro recapitulation of human microenvironments based on lab-on-a-chip technology represents a critical paradigm to better understand the intricate mechanisms. Moreover, the advent of micro/nanorobotics provides brand new perspectives and dynamic tools for elucidating the complex process in microfluidics. Currently, artificial intelligence (AI) has endowed micro/nanorobots (MNRs) with unprecedented benefits, such as material synthesis, optimal design, fabrication, and swarm behavior. Using advanced AI algorithms, the motion control, environment perception, and swarm intelligence of MNRs in microfluidics are significantly enhanced. This emerging interdisciplinary research trend holds great potential to propel biomedical research to the forefront and make valuable contributions to human health. Herein, we initially introduce the AI algorithms integral to the development of MNRs. We briefly revisit the components, designs, and fabrication techniques adopted by robots in microfluidics with an emphasis on the application of AI. Then, we review the latest research pertinent to AI-enhanced MNRs, focusing on their motion control, sensing abilities, and intricate collective behavior in microfluidics. Furthermore, we spotlight biomedical domains that are already witnessing or will undergo game-changing evolution based on AI-enhanced MNRs. Finally, we identify the current challenges that hinder the practical use of the pioneering interdisciplinary technology.
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Affiliation(s)
- Hui Dong
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
| | - Jiawen Lin
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
| | - Yihui Tao
- Department of Automation Control and System Engineering, University of Sheffield, Sheffield, UK
| | - Yuan Jia
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China
| | - Lining Sun
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
| | - Wen Jung Li
- Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China
| | - Hao Sun
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- Research Center of Aerospace Mechanism and Control, Harbin Institute of Technology, Harbin, China
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21
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Wang M, Wu Z, Wang J, Weng G, Kang Y, Pan P, Li D, Deng Y, Yao X, Bing Z, Hsieh CY, Hou T. Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space. J Chem Inf Model 2024; 64:1213-1228. [PMID: 38302422 DOI: 10.1021/acs.jcim.3c01964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Deep learning-based de novo molecular design has recently gained significant attention. While numerous DL-based generative models have been successfully developed for designing novel compounds, the majority of the generated molecules lack sufficiently novel scaffolds or high drug-like profiles. The aforementioned issues may not be fully captured by commonly used metrics for the assessment of molecular generative models, such as novelty, diversity, and quantitative estimation of the drug-likeness score. To address these limitations, we proposed a genetic algorithm-guided generative model called GARel (genetic algorithm-based receptor-ligand interaction generator), a novel framework for training a DL-based generative model to produce drug-like molecules with novel scaffolds. To efficiently train the GARel model, we utilized dense net to update the parameters based on molecules with novel scaffolds and drug-like features. To demonstrate the capability of the GARel model, we used it to design inhibitors for three targets: AA2AR, EGFR, and SARS-Cov2. The results indicate that GARel-generated molecules feature more diverse and novel scaffolds and possess more desirable physicochemical properties and favorable docking scores. Compared with other generative models, GARel makes significant progress in balancing novelty and drug-likeness, providing a promising direction for the further development of DL-based de novo design methodology with potential impacts on drug discovery.
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Affiliation(s)
- Mingyang Wang
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang ,China
| | - Zhengjian Wu
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
- School of Computer Science, Wuhan University, Wuhan 430072, Hubei ,China
| | - Jike Wang
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang ,China
| | - Gaoqi Weng
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Yu Kang
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Peichen Pan
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Dan Li
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang ,China
| | - Xiaojun Yao
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery Macau Institute for Applied Research in Medicine and Health State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China
| | - Zhitong Bing
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Tingjun Hou
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
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22
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Gangwal A, Ansari A, Ahmad I, Azad AK, Kumarasamy V, Subramaniyan V, Wong LS. Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Front Pharmacol 2024; 15:1331062. [PMID: 38384298 PMCID: PMC10879372 DOI: 10.3389/fphar.2024.1331062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/17/2024] [Indexed: 02/23/2024] Open
Abstract
There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Azim Ansari
- Computer Aided Drug Design Center Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, Prof. Ravindra Nikam College of Pharmacy, Dhule, India
| | - Abul Kalam Azad
- Faculty of Pharmacy, University College of MAIWP International, Batu Caves, Malaysia
| | - Vinoth Kumarasamy
- Department of Parasitology and Medical Entomology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Malaysia
| | - Vetriselvan Subramaniyan
- Pharmacology Unit, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Selangor, Malaysia
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India
| | - Ling Shing Wong
- Faculty of Health and Life Sciences, INTI International University, Nilai, Malaysia
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23
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Singh S, Singh PK, Sachan K, Kumar M, Bhardwaj P. Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope. Curr Comput Aided Drug Des 2024; 20:723-735. [PMID: 37807412 DOI: 10.2174/0115734099260187230921073932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 08/05/2023] [Accepted: 08/18/2023] [Indexed: 10/10/2023]
Abstract
The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.
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Affiliation(s)
- Smita Singh
- Department of Pharmaceutics, SRM Modinagar College of Pharmacy, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad, India
| | - Pranjal Kumar Singh
- Department of Pharmaceutics, SRM Modinagar College of Pharmacy, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad, India
| | - Kapil Sachan
- KIET School of Pharmacy, KIET Group of Institutions, Ghaziabad, India
| | - Mukesh Kumar
- IIMT College of Medical Sciences, IIMT University, Ganga Nagar, Meerut, India
| | - Poonam Bhardwaj
- NKBR College of Pharmacy and Research Center, Phaphunda, Meerut, India
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24
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Ochiai T, Inukai T, Akiyama M, Furui K, Ohue M, Matsumori N, Inuki S, Uesugi M, Sunazuka T, Kikuchi K, Kakeya H, Sakakibara Y. Variational autoencoder-based chemical latent space for large molecular structures with 3D complexity. Commun Chem 2023; 6:249. [PMID: 37973971 PMCID: PMC10654724 DOI: 10.1038/s42004-023-01054-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023] Open
Abstract
The structural diversity of chemical libraries, which are systematic collections of compounds that have potential to bind to biomolecules, can be represented by chemical latent space. A chemical latent space is a projection of a compound structure into a mathematical space based on several molecular features, and it can express structural diversity within a compound library in order to explore a broader chemical space and generate novel compound structures for drug candidates. In this study, we developed a deep-learning method, called NP-VAE (Natural Product-oriented Variational Autoencoder), based on variational autoencoder for managing hard-to-analyze datasets from DrugBank and large molecular structures such as natural compounds with chirality, an essential factor in the 3D complexity of compounds. NP-VAE was successful in constructing the chemical latent space from large-sized compounds that were unable to be handled in existing methods, achieving higher reconstruction accuracy, and demonstrating stable performance as a generative model across various indices. Furthermore, by exploring the acquired latent space, we succeeded in comprehensively analyzing a compound library containing natural compounds and generating novel compound structures with optimized functions.
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Grants
- 22H04901 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- 17H06410 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- 23H04885 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- 23H04880 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- 23H04881 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- 23H04887 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
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Affiliation(s)
- Toshiki Ochiai
- Department of Biosciences and Informatics, Keio University, Yokohama, Kanagawa, 223-8522, Japan
| | - Tensei Inukai
- Department of Biosciences and Informatics, Keio University, Yokohama, Kanagawa, 223-8522, Japan
| | - Manato Akiyama
- Department of Biosciences and Informatics, Keio University, Yokohama, Kanagawa, 223-8522, Japan
| | - Kairi Furui
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Yokohama, Kanagawa, 226-8501, Japan
| | - Masahito Ohue
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Yokohama, Kanagawa, 226-8501, Japan
| | - Nobuaki Matsumori
- Department of Chemistry, Graduate School of Science, Kyushu University, Fukuoka, Fukuoka, 819-0395, Japan
| | - Shinsuke Inuki
- Division of Medicinal Frontier Sciences, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Kyoto, 606-8501, Japan
| | - Motonari Uesugi
- Institute for Chemical Research and WPI-iCeMS, Kyoto University, Uji, Kyoto, 611-0011, Japan
| | - Toshiaki Sunazuka
- Omura Satoshi Memorial Institute and Graduate School of Infection Control Sciences, Kitasato University, Minato-ku, Tokyo, 108-8641, Japan
| | - Kazuya Kikuchi
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University, Suita, Osaka, 565-0871, Japan
- Immunology Frontier Research Centre, Osaka University, Suita, Osaka, 565-0871, Japan
| | - Hideaki Kakeya
- Division of Medicinal Frontier Sciences, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Kyoto, 606-8501, Japan
| | - Yasubumi Sakakibara
- Department of Biosciences and Informatics, Keio University, Yokohama, Kanagawa, 223-8522, Japan.
- Department of Data Science, Kitasato University School of Frontier Engineering, Sagamihara, Kanagawa, 252-0373, Japan.
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25
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Stanley M, Segler M. Fake it until you make it? Generative de novo design and virtual screening of synthesizable molecules. Curr Opin Struct Biol 2023; 82:102658. [PMID: 37473637 DOI: 10.1016/j.sbi.2023.102658] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/22/2023]
Abstract
Computational techniques, including virtual screening, de novo design, and generative models, play an increasing role in expediting DMTA cycles for modern molecular discovery. However, computationally proposed molecules must be synthetically feasible for laboratory testing. In this perspective, we offer a succinct introduction to the subject, and showcase typical workflows to integrate synthesis planning, synthesizability scoring, and molecule generation. Finally, we address limitations and opportunities for future research.
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Affiliation(s)
- Megan Stanley
- Microsoft Research AI4Science, UK. https://twitter.com/@megjanestanley
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26
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Muroga S, Miki Y, Hata K. A Comprehensive and Versatile Multimodal Deep-Learning Approach for Predicting Diverse Properties of Advanced Materials. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302508. [PMID: 37357977 PMCID: PMC10460884 DOI: 10.1002/advs.202302508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/08/2023] [Indexed: 06/27/2023]
Abstract
A multimodal deep-learning (MDL) framework is presented for predicting physical properties of a ten-dimensional acrylic polymer composite material by merging physical attributes and chemical data. The MDL model comprises four modules, including three generative deep-learning models for material structure characterization and a fourth model for property prediction. The approach handles an 18-dimensional complexity, with ten compositional inputs and eight property outputs, successfully predicting 913 680 property data points across 114 210 composition conditions. This level of complexity is unprecedented in computational materials science, particularly for materials with undefined structures. A framework is proposed to analyze the high-dimensional information space for inverse material design, demonstrating flexibility and adaptability to various materials and scales, provided sufficient data are available. This study advances future research on different materials and the development of more sophisticated models, drawing the authors closer to the ultimate goal of predicting all properties of all materials.
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Affiliation(s)
- Shun Muroga
- Nano Carbon Device Research CenterNational Institute of Advanced Industrial Science and TechnologyTsukuba Central 5, 1‐1‐1, HigashiTsukubaIbaraki305‐8565Japan
| | - Yasuaki Miki
- Nano Carbon Device Research CenterNational Institute of Advanced Industrial Science and TechnologyTsukuba Central 5, 1‐1‐1, HigashiTsukubaIbaraki305‐8565Japan
| | - Kenji Hata
- Nano Carbon Device Research CenterNational Institute of Advanced Industrial Science and TechnologyTsukuba Central 5, 1‐1‐1, HigashiTsukubaIbaraki305‐8565Japan
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27
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Floresta G, Zagni C, Patamia V, Rescifina A. How can artificial intelligence be utilized for de novo drug design against COVID-19 (SARS-CoV-2)? Expert Opin Drug Discov 2023; 18:1061-1064. [PMID: 37458097 DOI: 10.1080/17460441.2023.2236930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 07/12/2023] [Indexed: 07/18/2023]
Affiliation(s)
- Giuseppe Floresta
- Dipartimento di Scienze Del Farmaco E della Salute, Università di Catania, Catania, Italy
| | - Chiara Zagni
- Dipartimento di Scienze Del Farmaco E della Salute, Università di Catania, Catania, Italy
| | - Vincenzo Patamia
- Dipartimento di Scienze Del Farmaco E della Salute, Università di Catania, Catania, Italy
| | - Antonio Rescifina
- Dipartimento di Scienze Del Farmaco E della Salute, Università di Catania, Catania, Italy
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28
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Chenthamarakshan V, Hoffman SC, Owen CD, Lukacik P, Strain-Damerell C, Fearon D, Malla TR, Tumber A, Schofield CJ, Duyvesteyn HM, Dejnirattisai W, Carrique L, Walter TS, Screaton GR, Matviiuk T, Mojsilovic A, Crain J, Walsh MA, Stuart DI, Das P. Accelerating drug target inhibitor discovery with a deep generative foundation model. SCIENCE ADVANCES 2023; 9:eadg7865. [PMID: 37343087 PMCID: PMC10284550 DOI: 10.1126/sciadv.adg7865] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023]
Abstract
Inhibitor discovery for emerging drug-target proteins is challenging, especially when target structure or active molecules are unknown. Here, we experimentally validate the broad utility of a deep generative framework trained at-scale on protein sequences, small molecules, and their mutual interactions-unbiased toward any specific target. We performed a protein sequence-conditioned sampling on the generative foundation model to design small-molecule inhibitors for two dissimilar targets: the spike protein receptor-binding domain (RBD) and the main protease from SARS-CoV-2. Despite using only the target sequence information during the model inference, micromolar-level inhibition was observed in vitro for two candidates out of four synthesized for each target. The most potent spike RBD inhibitor exhibited activity against several variants in live virus neutralization assays. These results establish that a single, broadly deployable generative foundation model for accelerated inhibitor discovery is effective and efficient, even in the absence of target structure or binder information.
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Affiliation(s)
| | - Samuel C. Hoffman
- IBM Research, Thomas J. Watson Research Center, Yorktown Heights, New York, NY, USA
| | - C. David Owen
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, OX11 0DE Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA Didcot, UK
| | - Petra Lukacik
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, OX11 0DE Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA Didcot, UK
| | - Claire Strain-Damerell
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, OX11 0DE Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA Didcot, UK
| | - Daren Fearon
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, OX11 0DE Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA Didcot, UK
| | - Tika R. Malla
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, University of Oxford, 12 Mansfield Road, OX1 3TA Oxford, UK
| | - Anthony Tumber
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, University of Oxford, 12 Mansfield Road, OX1 3TA Oxford, UK
| | - Christopher J. Schofield
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, University of Oxford, 12 Mansfield Road, OX1 3TA Oxford, UK
| | - Helen M.E. Duyvesteyn
- Division of Structural Biology, University of Oxford, The Wellcome Centre for Human Genetics, Headington, Oxford, UK
| | - Wanwisa Dejnirattisai
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Loic Carrique
- Division of Structural Biology, University of Oxford, The Wellcome Centre for Human Genetics, Headington, Oxford, UK
| | - Thomas S. Walter
- Division of Structural Biology, University of Oxford, The Wellcome Centre for Human Genetics, Headington, Oxford, UK
| | - Gavin R. Screaton
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | | | | | - Jason Crain
- IBM Research Europe, Hartree Centre, Daresbury WA4 4AD, UK
- Department of Biochemistry, University of Oxford, Oxford OX1 3QU, UK
| | - Martin A. Walsh
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, OX11 0DE Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA Didcot, UK
| | - David I. Stuart
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, OX11 0DE Didcot, UK
- Division of Structural Biology, University of Oxford, The Wellcome Centre for Human Genetics, Headington, Oxford, UK
| | - Payel Das
- IBM Research, Thomas J. Watson Research Center, Yorktown Heights, New York, NY, USA
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29
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 PMCID: PMC10141994 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan;
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan;
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan;
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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30
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Grisoni F. Chemical language models for de novo drug design: Challenges and opportunities. Curr Opin Struct Biol 2023; 79:102527. [PMID: 36738564 DOI: 10.1016/j.sbi.2023.102527] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/07/2022] [Accepted: 12/20/2022] [Indexed: 02/05/2023]
Abstract
Generative deep learning is accelerating de novo drug design, by allowing the generation of molecules with desired properties on demand. Chemical language models - which generate new molecules in the form of strings using deep learning - have been particularly successful in this endeavour. Thanks to advances in natural language processing methods and interdisciplinary collaborations, chemical language models are expected to become increasingly relevant in drug discovery. This minireview provides an overview of the current state-of-the-art of chemical language models for de novo design, and analyses current limitations, challenges, and advantages. Finally, a perspective on future opportunities is provided.
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Affiliation(s)
- Francesca Grisoni
- Eindhoven University of Technology, Institute for Complex Molecular Systems and Dept. Biomedical Engineering, Eindhoven, Netherlands; Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Netherlands.
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31
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Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023; 616:673-685. [PMID: 37100941 DOI: 10.1038/s41586-023-05905-z] [Citation(s) in RCA: 184] [Impact Index Per Article: 184.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 03/01/2023] [Indexed: 04/28/2023]
Abstract
Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.
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Affiliation(s)
- Anastasiia V Sadybekov
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA
| | - Vsevolod Katritch
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA.
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32
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Isert C, Atz K, Schneider G. Structure-based drug design with geometric deep learning. Curr Opin Struct Biol 2023; 79:102548. [PMID: 36842415 DOI: 10.1016/j.sbi.2023.102548] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 01/16/2023] [Accepted: 01/24/2023] [Indexed: 02/26/2023]
Abstract
Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has been applied to macromolecular structures. This review provides an overview of the recent applications of geometric deep learning in bioorganic and medicinal chemistry, highlighting its potential for structure-based drug discovery and design. Emphasis is placed on molecular property prediction, ligand binding site and pose prediction, and structure-based de novo molecular design. The current challenges and opportunities are highlighted, and a forecast of the future of geometric deep learning for drug discovery is presented.
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Affiliation(s)
- Clemens Isert
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, Zurich, 8093, Switzerland
| | - Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, Zurich, 8093, Switzerland
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, Zurich, 8093, Switzerland; ETH Singapore SEC Ltd, 1 CREATE Way, #06-01 CREATE Tower, Singapore, 8093, Singapore.
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33
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Duran-Frigola M, Cigler M, Winter GE. Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence. J Am Chem Soc 2023; 145:2711-2732. [PMID: 36706315 PMCID: PMC9912273 DOI: 10.1021/jacs.2c11098] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Indexed: 01/28/2023]
Abstract
Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.
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Affiliation(s)
- Miquel Duran-Frigola
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria
- Ersilia
Open Source Initiative, 28 Belgrave Road, CB1 3DE, Cambridge, United Kingdom
| | - Marko Cigler
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria
| | - Georg E. Winter
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria
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34
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Danel T, Łęski J, Podlewska S, Podolak IT. Docking-based generative approaches in the search for new drug candidates. Drug Discov Today 2023; 28:103439. [PMID: 36372330 DOI: 10.1016/j.drudis.2022.103439] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/08/2022] [Accepted: 11/08/2022] [Indexed: 11/13/2022]
Abstract
Despite the popularity of virtual screening (VS) of existing compound libraries, the search for new potential drug candidates also takes advantage of generative protocols, where new compound suggestions are enumerated using various algorithms. To increase the activity potency of generative approaches, they have recently been coupled with molecular docking, a leading methodology of structure-based drug design (SBDD). In this review, we summarize progress since docking-based generative models emerged. We propose a new taxonomy for these methods and discuss their importance for the field of computer-aided drug design (CADD). In addition, we discuss the most promising directions for the further development of generative protocols coupled with docking.
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Affiliation(s)
- Tomasz Danel
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland.
| | - Jan Łęski
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, Department of Medicinal Chemistry, 31-343 Kraków, Smętna Street 12, Poland
| | - Igor T Podolak
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
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35
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Moret M, Pachon Angona I, Cotos L, Yan S, Atz K, Brunner C, Baumgartner M, Grisoni F, Schneider G. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nat Commun 2023; 14:114. [PMID: 36611029 PMCID: PMC9825622 DOI: 10.1038/s41467-022-35692-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 12/19/2022] [Indexed: 01/09/2023] Open
Abstract
Generative chemical language models (CLMs) can be used for de novo molecular structure generation by learning from a textual representation of molecules. Here, we show that hybrid CLMs can additionally leverage the bioactivity information available for the training compounds. To computationally design ligands of phosphoinositide 3-kinase gamma (PI3Kγ), a collection of virtual molecules was created with a generative CLM. This virtual compound library was refined using a CLM-based classifier for bioactivity prediction. This second hybrid CLM was pretrained with patented molecular structures and fine-tuned with known PI3Kγ ligands. Several of the computer-generated molecular designs were commercially available, enabling fast prescreening and preliminary experimental validation. A new PI3Kγ ligand with sub-micromolar activity was identified, highlighting the method's scaffold-hopping potential. Chemical synthesis and biochemical testing of two of the top-ranked de novo designed molecules and their derivatives corroborated the model's ability to generate PI3Kγ ligands with medium to low nanomolar activity for hit-to-lead expansion. The most potent compounds led to pronounced inhibition of PI3K-dependent Akt phosphorylation in a medulloblastoma cell model, demonstrating efficacy of PI3Kγ ligands in PI3K/Akt pathway repression in human tumor cells. The results positively advocate hybrid CLMs for virtual compound screening and activity-focused molecular design.
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Affiliation(s)
- Michael Moret
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Irene Pachon Angona
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Leandro Cotos
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Shen Yan
- University of Zurich, University Children's Hospital, Children's Research Center, Pediatric Molecular Neuro-Oncology Research, Lengghalde 5, 8008, Zurich, Switzerland
| | - Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Cyrill Brunner
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Martin Baumgartner
- University of Zurich, University Children's Hospital, Children's Research Center, Pediatric Molecular Neuro-Oncology Research, Lengghalde 5, 8008, Zurich, Switzerland
| | - Francesca Grisoni
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
- Eindhoven University of Technology, Institute for Complex Molecular Systems and Eindhoven Artificial Intelligence Systems Institute, Department of Biomedical Engineering, Groene Loper 7, 5612AZ, Eindhoven, The Netherlands.
- Center for 393 Living Technologies, Alliance TU/e, WUR, UU, UMC 394 Utrecht, Utrecht, 3584 CB, The Netherlands.
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
- ETH Singapore SEC Ltd, 1 CREATE Way, #06-01 CREATE Tower, Singapore, 138602, Singapore.
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36
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Atz K, Guba W, Grether U, Schneider G. Machine Learning and Computational Chemistry for the Endocannabinoid System. Methods Mol Biol 2023; 2576:477-493. [PMID: 36152211 DOI: 10.1007/978-1-0716-2728-0_39] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure-activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.
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Affiliation(s)
- Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland
| | - Wolfgang Guba
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Uwe Grether
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland
- ETH Singapore SEC Ltd, Singapore, Singapore
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37
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Li Y, Zhang L, Wang Y, Zou J, Yang R, Luo X, Wu C, Yang W, Tian C, Xu H, Wang F, Yang X, Li L, Yang S. Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor. Nat Commun 2022; 13:6891. [PMID: 36371441 PMCID: PMC9653409 DOI: 10.1038/s41467-022-34692-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 11/03/2022] [Indexed: 11/13/2022] Open
Abstract
The retrieval of hit/lead compounds with novel scaffolds during early drug development is an important but challenging task. Various generative models have been proposed to create drug-like molecules. However, the capacity of these generative models to design wet-lab-validated and target-specific molecules with novel scaffolds has hardly been verified. We herein propose a generative deep learning (GDL) model, a distribution-learning conditional recurrent neural network (cRNN), to generate tailor-made virtual compound libraries for given biological targets. The GDL model is then applied to RIPK1. Virtual screening against the generated tailor-made compound library and subsequent bioactivity evaluation lead to the discovery of a potent and selective RIPK1 inhibitor with a previously unreported scaffold, RI-962. This compound displays potent in vitro activity in protecting cells from necroptosis, and good in vivo efficacy in two inflammatory models. Collectively, the findings prove the capacity of our GDL model in generating hit/lead compounds with unreported scaffolds, highlighting a great potential of deep learning in drug discovery.
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Affiliation(s)
- Yueshan Li
- grid.13291.380000 0001 0807 1581State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan China
| | - Liting Zhang
- grid.13291.380000 0001 0807 1581State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan China
| | - Yifei Wang
- grid.13291.380000 0001 0807 1581State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan China
| | - Jun Zou
- grid.13291.380000 0001 0807 1581State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan China
| | - Ruicheng Yang
- grid.13291.380000 0001 0807 1581State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan China
| | - Xinling Luo
- grid.13291.380000 0001 0807 1581Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, 610041 Chengdu, Sichuan China
| | - Chengyong Wu
- grid.13291.380000 0001 0807 1581State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan China
| | - Wei Yang
- grid.13291.380000 0001 0807 1581State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan China
| | - Chenyu Tian
- grid.13291.380000 0001 0807 1581State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan China
| | - Haixing Xu
- grid.13291.380000 0001 0807 1581State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan China
| | - Falu Wang
- grid.13291.380000 0001 0807 1581State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan China
| | - Xin Yang
- grid.13291.380000 0001 0807 1581State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan China
| | - Linli Li
- grid.13291.380000 0001 0807 1581Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, 610041 Chengdu, Sichuan China
| | - Shengyong Yang
- grid.13291.380000 0001 0807 1581State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan China
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38
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Prentis LE, Singleton CD, Bickel JD, Allen WJ, Rizzo RC. A molecular evolution algorithm for ligand design in DOCK. J Comput Chem 2022; 43:1942-1963. [PMID: 36073674 PMCID: PMC9623574 DOI: 10.1002/jcc.26993] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 06/13/2022] [Accepted: 08/03/2022] [Indexed: 01/11/2023]
Abstract
As a complement to virtual screening, de novo design of small molecules is an alternative approach for identifying potential drug candidates. Here, we present a new 3D genetic algorithm to evolve molecules through breeding, mutation, fitness pressure, and selection. The method, termed DOCK_GA, builds upon and leverages powerful sampling, scoring, and searching routines previously implemented into DOCK6. Three primary experiments were used during development: Single-molecule evolution evaluated three selection methods (elitism, tournament, and roulette), in four clinically relevant systems, in terms of mutation type and crossover success, chemical properties, ensemble diversity, and fitness convergence, among others. Large scale benchmarking assessed performance across 651 different protein-ligand systems. Ensemble-based evolution demonstrated using multiple inhibitors simultaneously to seed growth in a SARS-CoV-2 target. Key takeaways include: (1) The algorithm is robust as demonstrated by the successful evolution of molecules across a large diverse dataset. (2) Users have flexibility with regards to parent input, selection method, fitness function, and molecular descriptors. (3) The program is straightforward to run and only requires a single executable and input file at run-time. (4) The elitism selection method yields more tightly clustered molecules in terms of 2D/3D similarity, with more favorable fitness, followed by tournament and roulette.
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Affiliation(s)
- Lauren E. Prentis
- Department of Biochemistry & Cell BiologyStony Brook UniversityStony BrookNew YorkUSA
| | | | - John D. Bickel
- Department of ChemistryStony Brook UniversityStony BrookNew YorkUSA
| | - William J. Allen
- Department of Applied Mathematics & StatisticsStony Brook UniversityStony BrookNew YorkUSA
| | - Robert C. Rizzo
- Department of Applied Mathematics & StatisticsStony Brook UniversityStony BrookNew YorkUSA
- Institute of Chemical Biology & Drug DiscoveryStony Brook UniversityStony BrookNew YorkUSA
- Laufer Center for Physical & Quantitative BiologyStony Brook UniversityStony BrookNew YorkUSA
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39
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Thomas M, O’Boyle NM, Bender A, de Graaf C. Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation. J Cheminform 2022; 14:68. [PMID: 36192789 PMCID: PMC9531503 DOI: 10.1186/s13321-022-00646-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 09/23/2022] [Indexed: 11/10/2022] Open
Abstract
A plethora of AI-based techniques now exists to conduct de novo molecule generation that can devise molecules conditioned towards a particular endpoint in the context of drug design. One popular approach is using reinforcement learning to update a recurrent neural network or language-based de novo molecule generator. However, reinforcement learning can be inefficient, sometimes requiring up to 105 molecules to be sampled to optimize more complex objectives, which poses a limitation when using computationally expensive scoring functions like docking or computer-aided synthesis planning models. In this work, we propose a reinforcement learning strategy called Augmented Hill-Climb based on a simple, hypothesis-driven hybrid between REINVENT and Hill-Climb that improves sample-efficiency by addressing the limitations of both currently used strategies. We compare its ability to optimize several docking tasks with REINVENT and benchmark this strategy against other commonly used reinforcement learning strategies including REINFORCE, REINVENT (version 1 and 2), Hill-Climb and best agent reminder. We find that optimization ability is improved ~ 1.5-fold and sample-efficiency is improved ~ 45-fold compared to REINVENT while still delivering appealing chemistry as output. Diversity filters were used, and their parameters were tuned to overcome observed failure modes that take advantage of certain diversity filter configurations. We find that Augmented Hill-Climb outperforms the other reinforcement learning strategies used on six tasks, especially in the early stages of training or for more difficult objectives. Lastly, we show improved performance not only on recurrent neural networks but also on a reinforcement learning stabilized transformer architecture. Overall, we show that Augmented Hill-Climb improves sample-efficiency for language-based de novo molecule generation conditioning via reinforcement learning, compared to the current state-of-the-art. This makes more computationally expensive scoring functions, such as docking, more accessible on a relevant timescale.
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Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW UK
| | - Noel M. O’Boyle
- Computational Chemistry, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG UK
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW UK
| | - Chris de Graaf
- Computational Chemistry, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG UK
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40
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Chen S, Zhang L, Chen Y, Fu L. Inhibiting Sodium Taurocholate Cotransporting Polypeptide in HBV-Related Diseases: From Biological Function to Therapeutic Potential. J Med Chem 2022; 65:12546-12561. [DOI: 10.1021/acs.jmedchem.2c01097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Siwei Chen
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Lan Zhang
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Yi Chen
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Leilei Fu
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China
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41
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Flam-Shepherd D, Zhu K, Aspuru-Guzik A. Language models can learn complex molecular distributions. Nat Commun 2022; 13:3293. [PMID: 35672310 PMCID: PMC9174447 DOI: 10.1038/s41467-022-30839-x] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 05/16/2022] [Indexed: 11/09/2022] Open
Abstract
Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space. The downstream utility of generative models for the inverse design of novel functional compounds, depends on their ability to learn a training distribution of molecules. The most simple example is a language model that takes the form of a recurrent neural network and generates molecules using a string representation. Since their initial use, subsequent work has shown that language models are very capable, in particular, recent research has demonstrated their utility in the low data regime. In this work, we investigate the capacity of simple language models to learn more complex distributions of molecules. For this purpose, we introduce several challenging generative modeling tasks by compiling larger, more complex distributions of molecules and we evaluate the ability of language models on each task. The results demonstrate that language models are powerful generative models, capable of adeptly learning complex molecular distributions. Language models can accurately generate: distributions of the highest scoring penalized LogP molecules in ZINC15, multi-modal molecular distributions as well as the largest molecules in PubChem. The results highlight the limitations of some of the most popular and recent graph generative models- many of which cannot scale to these molecular distributions.
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Affiliation(s)
- Daniel Flam-Shepherd
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4, Canada.
- Vector Institute for Artificial Intelligence, Toronto, ON, M5S 1M1, Canada.
| | - Kevin Zhu
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4, Canada
| | - Alán Aspuru-Guzik
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4, Canada.
- Vector Institute for Artificial Intelligence, Toronto, ON, M5S 1M1, Canada.
- Department of Chemistry, University of Toronto, Toronto, ON, M5G 1Z8, Canada.
- Canadian Institute for Advanced Research, Toronto, ON, M5G 1Z8, Canada.
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42
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Goralczyk A, Mayoussi F, Sanjaya M, Corredor SF, Bhagwat S, Song Q, Schwenteck S, Warmbold A, Pezeshkpour P, Rapp BE. On‐Chip Chemical Synthesis Using One‐Step 3D Printed Polyperfluoropolyether. CHEM-ING-TECH 2022; 94:975-982. [PMID: 35915768 PMCID: PMC9322562 DOI: 10.1002/cite.202200013] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 03/31/2022] [Accepted: 04/07/2022] [Indexed: 11/28/2022]
Abstract
Three‐dimensional (3D) printing has already shown its high relevance for the fabrication of microfluidic devices in terms of precision manufacturing cycles and a wider range of materials. 3D‐printable transparent fluoropolymers are highly sought after due to their high chemical and thermal resistance. Here, we present a simple one‐step fabrication process via stereolithography of perfluoropolyether dimethacrylate. We demonstrate successfully printed microfluidic mixers with 800 µm circular channels for chemistry‐on‐chip applications. The printed chips show chemical, mechanical, and thermal resistance up to 200 °C, as well as high optical transparency. Aqueous and organic reactions are presented to demonstrate the wide potential of perfluoropolyether dimethacrylate for chemical synthesis.
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Affiliation(s)
- Andreas Goralczyk
- University of Freiburg Laboratory of Process Technology, NeptunLab Department of Microsystems Engineering (IMTEK) Georges-Köhler-Allee 103 79110 Freiburg im Breisgau Germany
| | - Fadoua Mayoussi
- University of Freiburg Laboratory of Process Technology, NeptunLab Department of Microsystems Engineering (IMTEK) Georges-Köhler-Allee 103 79110 Freiburg im Breisgau Germany
| | - Mario Sanjaya
- University of Freiburg Laboratory of Process Technology, NeptunLab Department of Microsystems Engineering (IMTEK) Georges-Köhler-Allee 103 79110 Freiburg im Breisgau Germany
| | - Santiago Franco Corredor
- University of Freiburg Laboratory of Process Technology, NeptunLab Department of Microsystems Engineering (IMTEK) Georges-Köhler-Allee 103 79110 Freiburg im Breisgau Germany
| | - Sagar Bhagwat
- University of Freiburg Laboratory of Process Technology, NeptunLab Department of Microsystems Engineering (IMTEK) Georges-Köhler-Allee 103 79110 Freiburg im Breisgau Germany
| | - Qingchuan Song
- University of Freiburg Laboratory of Process Technology, NeptunLab Department of Microsystems Engineering (IMTEK) Georges-Köhler-Allee 103 79110 Freiburg im Breisgau Germany
| | - Sarah Schwenteck
- University of Freiburg Laboratory of Process Technology, NeptunLab Department of Microsystems Engineering (IMTEK) Georges-Köhler-Allee 103 79110 Freiburg im Breisgau Germany
| | - Andreas Warmbold
- University of Freiburg Freiburg Materials Research Center (FMF) Stefan-Meier-Straße 21 79104 Freiburg im Breisgau Germany
| | - Pegah Pezeshkpour
- University of Freiburg Laboratory of Process Technology, NeptunLab Department of Microsystems Engineering (IMTEK) Georges-Köhler-Allee 103 79110 Freiburg im Breisgau Germany
| | - Bastian E. Rapp
- University of Freiburg Laboratory of Process Technology, NeptunLab Department of Microsystems Engineering (IMTEK) Georges-Köhler-Allee 103 79110 Freiburg im Breisgau Germany
- University of Freiburg Freiburg Materials Research Center (FMF) Stefan-Meier-Straße 21 79104 Freiburg im Breisgau Germany
- University of Freiburg FIT Freiburg Center of Interactive Materials and Bioinspired Technologies Georges-Köhler-Allee 105 79110 Freiburg im Breisgau Germany
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43
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Bos PH, Houang EM, Ranalli F, Leffler AE, Boyles NA, Eyrich VA, Luria Y, Katz D, Tang H, Abel R, Bhat S. AutoDesigner, a De Novo Design Algorithm for Rapidly Exploring Large Chemical Space for Lead Optimization: Application to the Design and Synthesis of d-Amino Acid Oxidase Inhibitors. J Chem Inf Model 2022; 62:1905-1915. [PMID: 35417149 DOI: 10.1021/acs.jcim.2c00072] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The lead optimization stage of a drug discovery program generally involves the design, synthesis, and assaying of hundreds to thousands of compounds. The design phase is usually carried out via traditional medicinal chemistry approaches and/or structure-based drug design (SBDD) when suitable structural information is available. Two of the major limitations of this approach are (1) difficulty in rapidly designing potent molecules that adhere to myriad project criteria, or the multiparameter optimization (MPO) problem, and (2) the relatively small number of molecules explored compared to the vast size of chemical space. To address these limitations, we have developed AutoDesigner, a de novo design algorithm. AutoDesigner employs a cloud-native, multistage search algorithm to carry out successive rounds of chemical space exploration and filtering. Millions to billions of virtual molecules are explored and optimized while adhering to a customizable set of project criteria such as physicochemical properties and potency. Additionally, the algorithm only requires a single ligand with measurable affinity and a putative binding model as a starting point, making it amenable to the early stages of an SBDD project where limited data are available. To assess the effectiveness of AutoDesigner, we applied it to the design of novel inhibitors of d-amino acid oxidase (DAO), a target for the treatment of schizophrenia. AutoDesigner was able to generate and efficiently explore over 1 billion molecules to successfully address a variety of project goals. The compounds generated by AutoDesigner that were synthesized and assayed (1) simultaneously met not only physicochemical criteria, clearance, and central nervous system (CNS) penetration (Kp,uu) cutoffs but also potency thresholds and (2) fully utilize structural data to discover and explore novel interactions and a previously unexplored subpocket in the DAO active site. The reported data demonstrate that AutoDesigner can play a key role in accelerating the discovery of novel, potent chemical matter within the constraints of a given drug discovery lead optimization campaign.
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Affiliation(s)
- Pieter H Bos
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Evelyne M Houang
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Fabio Ranalli
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Abba E Leffler
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Nicholas A Boyles
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Volker A Eyrich
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Yuval Luria
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Dana Katz
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Haifeng Tang
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Robert Abel
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Sathesh Bhat
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
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44
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Creanza TM, Lamanna G, Delre P, Contino M, Corriero N, Saviano M, Mangiatordi GF, Ancona N. DeLA-Drug: A Deep Learning Algorithm for Automated Design of Druglike Analogues. J Chem Inf Model 2022; 62:1411-1424. [PMID: 35294184 DOI: 10.1021/acs.jcim.2c00205] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In this paper, we present a deep learning algorithm for automated design of druglike analogues (DeLA-Drug), a recurrent neural network (RNN) model composed of two long short-term memory (LSTM) layers and conceived for data-driven generation of similar-to-bioactive compounds. DeLA-Drug captures the syntax of SMILES strings of more than 1 million compounds belonging to the ChEMBL28 database and, by employing a new strategy called sampling with substitutions (SWS), generates molecules starting from a single user-defined query compound. Remarkably, the algorithm preserves druglikeness and synthetic accessibility of the known bioactive compounds present in the ChEMBL28 repository. The absence of any time-demanding fine-tuning procedure enables DeLA-Drug to perform a fast generation of focused libraries for further high-throughput screening and makes it a suitable tool for performing de novo design even in low-data regimes. To provide a concrete idea of its applicability, DeLA-Drug was applied to the cannabinoid receptor subtype 2 (CB2R), a known target involved in different pathological conditions such as cancer and neurodegeneration. DeLA-Drug, available as a free web platform (http://www.ba.ic.cnr.it/softwareic/deladrugportal/), can help medicinal chemists interested in generating analogues of compounds already available in their laboratories and, for this reason, good candidates for an easy and low-cost synthesis.
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Affiliation(s)
- Teresa Maria Creanza
- CNR─Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Amendola 122/o, 70126 Bari, Italy
| | - Giuseppe Lamanna
- Chemistry Department, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125 Bari, Italy.,CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | - Pietro Delre
- Chemistry Department, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125 Bari, Italy.,CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | - Marialessandra Contino
- Department of Pharmacy─Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125 Bari, Italy
| | - Nicola Corriero
- CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | - Michele Saviano
- CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | | | - Nicola Ancona
- CNR─Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Amendola 122/o, 70126 Bari, Italy
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45
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Moret M, Grisoni F, Katzberger P, Schneider G. Perplexity-Based Molecule Ranking and Bias Estimation of Chemical Language Models. J Chem Inf Model 2022; 62:1199-1206. [PMID: 35191696 PMCID: PMC8924923 DOI: 10.1021/acs.jcim.2c00079] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Indexed: 02/07/2023]
Abstract
Chemical language models (CLMs) can be employed to design molecules with desired properties. CLMs generate new chemical structures in the form of textual representations, such as the simplified molecular input line entry system (SMILES) strings. However, the quality of these de novo generated molecules is difficult to assess a priori. In this study, we apply the perplexity metric to determine the degree to which the molecules generated by a CLM match the desired design objectives. This model-intrinsic score allows identifying and ranking the most promising molecular designs based on the probabilities learned by the CLM. Using perplexity to compare "greedy" (beam search) with "explorative" (multinomial sampling) methods for SMILES generation, certain advantages of multinomial sampling become apparent. Additionally, perplexity scoring is performed to identify undesired model biases introduced during model training and allows the development of a new ranking system to remove those undesired biases.
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Affiliation(s)
- Michael Moret
- Department
of Chemistry and Applied Biosciences, ETH
Zurich, RETHINK, Vladimir-Prelog-Weg 4, Zurich 8093, Switzerland
| | - Francesca Grisoni
- Institute
for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 7, Eindhoven 5612AZ, Netherlands
- Center
for Living Technologies, Alliance TU/e,
WUR, UU, UMC Utrecht, Princetonlaan 6, Utrecht 3584 CB, The Netherlands
| | - Paul Katzberger
- Department
of Chemistry and Applied Biosciences, ETH
Zurich, RETHINK, Vladimir-Prelog-Weg 4, Zurich 8093, Switzerland
| | - Gisbert Schneider
- Department
of Chemistry and Applied Biosciences, ETH
Zurich, RETHINK, Vladimir-Prelog-Weg 4, Zurich 8093, Switzerland
- ETH
Singapore SEC Ltd., 1
CREATE Way, #06-01 CREATE Tower, Singapore 138602, Singapore
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46
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Tian F, Cai L, Liu C, Sun J. Microfluidic technologies for nanoparticle formation. LAB ON A CHIP 2022; 22:512-529. [PMID: 35048096 DOI: 10.1039/d1lc00812a] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Functional nanoparticles (NPs) hold immense promise in diverse fields due to their unique biological, chemical, and physical properties associated with size or morphology. Microfluidic technologies featuring precise fluid manipulation have become versatile toolkits for manufacturing NPs in a highly controlled manner with low batch-to-batch variability. In this review, we present the fundamentals of microfluidic fabrication strategies, including mixing-, droplet-, and multiple field-based microfluidic methods. We highlight the formation of functional NPs using these microfluidic reactors, with an emphasis on lipid NPs, polymer NPs, lipid-polymer hybrid NPs, supramolecular NPs, metal and metal-oxide NPs, metal-organic framework NPs, covalent organic framework NPs, quantum dots, perovskite nanocrystals, biomimetic NPs, etc. we discuss future directions in microfluidic fabrication for accelerated development of functional NPs, such as device parallelization for large-scale NP production, highly efficient optimization of NP formulations, and AI-guided design of multi-step microfluidic reactors.
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Affiliation(s)
- Fei Tian
- Beijing Engineering Research Center for BioNanotechnology, CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, China.
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lili Cai
- Department of Laboratory Medicine, The Second Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Chao Liu
- Beijing Engineering Research Center for BioNanotechnology, CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, China.
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiashu Sun
- Beijing Engineering Research Center for BioNanotechnology, CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, China.
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
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47
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Zhang Y, Zhou A, Chen S, Lum GZ, Zhang X. A perspective on magnetic microfluidics: Towards an intelligent future. BIOMICROFLUIDICS 2022; 16:011301. [PMID: 35069962 PMCID: PMC8769766 DOI: 10.1063/5.0079464] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 01/02/2022] [Indexed: 05/09/2023]
Abstract
Magnetic microfluidics has been gradually recognized as an area of its own. Both conventional microfluidic platforms have incorporated magnetic actuation for microfluidic operation and microscale object manipulation. Nonetheless, there is still much room for improvement after decades of development. In this Perspective, we first provide a quick review of existing magnetic microfluidic platforms with a focus on the magnetic tools and actuation mechanisms. Next, we discuss several emerging technologies, including magnetic microrobots, additive manufacture, and artificial intelligence, and their potential application in the future development of magnetic microfluidics. We believe that these technologies can eventually inspire highly functional magnetic tools for microfluidic manipulation and coordinated microfluidic control at the system level, which eventually drives magnetic microfluidics into an intelligent system for automated experimentation.
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Affiliation(s)
- Yi Zhang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China Chengdu, China
- Authors to whom correspondence should be addressed:; ;
and
| | - Aiwu Zhou
- Singapore Centre for 3D Printing, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | - Songlin Chen
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | - Guo Zhan Lum
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
- Authors to whom correspondence should be addressed:; ;
and
| | - Xiaosheng Zhang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China Chengdu, China
- Authors to whom correspondence should be addressed:; ;
and
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48
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49
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Miljković F, Rodríguez-Pérez R, Bajorath J. Impact of Artificial Intelligence on Compound Discovery, Design, and Synthesis. ACS OMEGA 2021; 6:33293-33299. [PMID: 34926881 PMCID: PMC8674916 DOI: 10.1021/acsomega.1c05512] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 11/18/2021] [Indexed: 05/17/2023]
Abstract
As in other areas, artificial intelligence (AI) is heavily promoted in different scientific fields, including chemistry. Although chemistry traditionally tends to be a conservative field and slower than others to adapt new concepts, AI is increasingly being investigated across chemical disciplines. In medicinal chemistry, supported by computer-aided drug design and cheminformatics, computational methods have long been employed to aid in the search for and optimization of active compounds. We are currently witnessing a multitude of AI-related publications in the medicinal-chemistry-relevant literature and anticipate that the numbers will further increase. Often, advances through AI promoted in such reports are difficult to reconcile or remain questionable, which hampers the acceptance of computational work in interdisciplinary environments. Herein we attempt to highlight selected investigations in which AI has shown promise to impact medicinal chemistry in areas such as compound design and synthesis.
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Affiliation(s)
- Filip Miljković
- Department
of Life Science Informatics and Data Science, B-IT, LIMES Program
Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany
- Data
Science and AI, Imaging and Data Analytics, Clinical Pharmacology
& Safety Sciences, R&D, AstraZeneca, SE-431 83 Gothenburg, Sweden
| | - Raquel Rodríguez-Pérez
- Department
of Life Science Informatics and Data Science, B-IT, LIMES Program
Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany
- Novartis
Institutes for Biomedical Research, Novartis
Campus, CH-4002 Basel, Switzerland
| | - Jürgen Bajorath
- Department
of Life Science Informatics and Data Science, B-IT, LIMES Program
Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany
- Phone: 49-228-7369-100.
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50
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Liu L, Bi M, Wang Y, Liu J, Jiang X, Xu Z, Zhang X. Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis. NANOSCALE 2021; 13:19352-19366. [PMID: 34812823 DOI: 10.1039/d1nr06195j] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Artificial intelligence (AI) is an emerging technology with great potential, and its robust calculation and analysis capabilities are unmatched by traditional calculation tools. With the promotion of deep learning and open-source platforms, the threshold of AI has also become lower. Combining artificial intelligence with traditional fields to create new fields of high research and application value has become a trend. AI has been involved in many disciplines, such as medicine, materials, energy, and economics. The development of AI requires the support of many kinds of data, and microfluidic systems can often mine object data on a large scale to support AI. Due to the excellent synergy between the two technologies, excellent research results have emerged in many fields. In this review, we briefly review AI and microfluidics and introduce some applications of their combination, mainly in nanomedicine and material synthesis. Finally, we discuss the development trend of the combination of the two technologies.
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Affiliation(s)
- Linbo Liu
- John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA 02138, USA
| | - Mingcheng Bi
- Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, P.R. China
| | - Yunhua Wang
- John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA 02138, USA
| | - Junfeng Liu
- Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, P.R. China
| | - Xiwen Jiang
- College of Biological Science and Engineering, Fuzhou university, Fuzhou 350108, P.R. China
| | - Zhongbin Xu
- Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, P.R. China
| | - Xingcai Zhang
- John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA 02138, USA
- School of Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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