1
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Singh S, Kaur N, Gehlot A. Application of artificial intelligence in drug design: A review. Comput Biol Med 2024; 179:108810. [PMID: 38991316 DOI: 10.1016/j.compbiomed.2024.108810] [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: 03/18/2024] [Revised: 05/31/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.
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Affiliation(s)
- Simrandeep Singh
- Department of Electronics & Communication Engineering, UCRD, Chandigarh University, Gharuan, Punjab, India.
| | - Navjot Kaur
- Department of Pharmacognosy, Amar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy, Bela, Ropar, India
| | - Anita Gehlot
- Uttaranchal Institute of technology, Uttaranchal University, Dehradun, India
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2
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Chandraghatgi R, Ji HF, Rosen GL, Sokhansanj BA. Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening. J Chem Inf Model 2024; 64:3826-3840. [PMID: 38696451 PMCID: PMC11197033 DOI: 10.1021/acs.jcim.4c00234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 05/04/2024]
Abstract
Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD). FDSL-DD uses in silico screening to identify ligands from a vast library, fragmenting them while attaching specific attributes based on predicted binding affinity and interaction with the target subdomain. In this paper, we further propose a two-stage optimization method that utilizes the information from prescreening to optimize computational ligand synthesis. We hypothesize that using prescreening information for optimization shrinks the search space and focuses on promising regions, thereby improving the optimization for candidate ligands. The first optimization stage assembles these fragments into larger compounds using genetic algorithms, followed by a second stage of iterative refinement to produce compounds with enhanced bioactivity. To demonstrate broad applicability, the methodology is demonstrated on three diverse protein targets found in human solid cancers, bacterial antimicrobial resistance, and the SARS-CoV-2 virus. Combined, the proposed FDSL-DD and a two-stage optimization approach yield high-affinity ligand candidates more efficiently than other state-of-the-art computational FBDD methods. We further show that a multiobjective optimization method accounting for drug-likeness can still produce potential candidate ligands with a high binding affinity. Overall, the results demonstrate that integrating detailed chemical information with a constrained search framework can markedly optimize the initial drug discovery process, offering a more precise and efficient route to developing new therapeutics.
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Affiliation(s)
- Rohan Chandraghatgi
- Department
of Biology, Drexel University, Philadelphia, Pennsylvania 19104, United States
| | - Hai-Feng Ji
- Department
of Chemistry, Drexel University, Philadelphia, Pennsylvania 19104, United States
| | - Gail L. Rosen
- Department
of Electrical & Computer Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States
| | - Bahrad A. Sokhansanj
- Department
of Electrical & Computer Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States
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3
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Huang CH, Lin ST. MARS Plus: An Improved Molecular Design Tool for Complex Compounds Involving Ionic, Stereo, and Cis-Trans Isomeric Structures. J Chem Inf Model 2023; 63:7711-7728. [PMID: 38100117 DOI: 10.1021/acs.jcim.3c01745] [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: 12/26/2023]
Abstract
MARS (Molecular Assembling and Representation Suite) (Hsu et al. J. Chem. Inf. Model. 2019, 59, 3703-3713) is a toolbox for the molecular design of organic molecules. MARS uses integer arrays to represent the elements and connectivity between elements of a molecule. It provides a collection of operations to manipulate the elemental composition and connectivity of a molecule (or a pair of molecules), enabling the creation of novel chemical compounds. In this work, the original MARS is extended to handle complex molecular structures, including geometric (cis-trans) isomers, stereo isomers, cyclic compounds, and ionic species. The extended version of MARS, referred to as MARS+, has a more comprehensive coverage of the chemical space and therefore can explore molecules with a greater chemical and physical diversity. Compared to other molecular design tools, MARS+ is designed to perform all possible manipulations on a given molecule or a pair of molecules. Molecular structure manipulation can be conducted in either a controlled or a random fashion. Furthermore, every structure manipulation has a counterpart so that the operation can be reversed. Nearly any possible chemical structure can be generated with MARS+ via a combination of molecular operations. The capabilities of MARS+ are examined by the design of new ionic liquids (ILs). The results show that MARS+ is a useful tool for computer-aided molecular design (CAMD) and molecular structure enumeration.
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Affiliation(s)
- Chen-Hsuan Huang
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Shiang-Tai Lin
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
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4
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Kurkin DV, Morkovin EI, Bakulin DA, Gorbunova YV, Kolosov YA, Dzhavakhyan MA, Makarenko IE, Drai RV, Zaborovsky AV, Shatalova OV, Strygin AV, Petrov VI, Pleten AP, Prokopov AA, Tatarenko-Kozmina TY. Overview of drugs approved by the FDA in 2022. PHARMACY & PHARMACOLOGY 2023; 11:193-210. [DOI: 10.19163/2307-9266-2023-11-3-193-210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2025]
Abstract
The aim of the work is to conduct a review of medications approved by the FDA in 2022.Materials and methods. In searching for the materials to write this review article, bibliographic databases including PubMed, Google Scholar and e-library.ru were utilized. The search was conducted for the publications spanning the period from 2008 to 2023. Herewith, the following keywords and word combinations were used: new drug approval, NDA, drug authorization, approval package, breakthrough medicine.Results. The discovery and development of medications are among the most crucial scientific processes in healthcare. Developing a new drug is a highly intricate, expensive, and time-consuming process. Nowadays, the problem of costs reduction and the process of expedited discovering of new medications are particularly pertinent. To optimize the search for active compounds, virtual and high-throughput screenings, machine learning, artificial intelligence, cryo-electron microscopy, and drug repurposing are employed. Simultaneously, the search for original molecules to serve as the basis for innovative drugs continues. This article presents a review of medications approved by the FDA in 2022 for the treatment of various pathologies.Conclusion. A drug development is a complex and resource-intensive process, with only a small fraction of candidates advancing to clinical trials. A drug design evolves in tandem with societal needs, and this review highlights some of the medications approved by the FDA in 2022. Technological advancements are expected to expedite drug development, potentially reducing the time to the market. Biotechnology, including cell therapy, holds significant prospects, and achievements in genetic mapping and chip technologies will enhance the accessibility of personalized pharmacology.
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Affiliation(s)
- D. V. Kurkin
- 1.Yevdokimov Moscow State University of Medicine and Dentistry.
2.Volgograd State Medical University
| | | | | | | | - Yu. A. Kolosov
- Yevdokimov Moscow State University of Medicine and Dentistry
| | - M. A. Dzhavakhyan
- 1.Yevdokimov Moscow State University of Medicine and Dentistry.
2.All-Russian Scientific Research Institute of Medicinal and Aromatic Plants
| | - I. E. Makarenko
- 1.Yevdokimov Moscow State University of Medicine and Dentistry.
2.Farm-Holding
| | | | | | | | | | | | - A. P. Pleten
- Yevdokimov Moscow State University of Medicine and Dentistry
| | - A. A. Prokopov
- Yevdokimov Moscow State University of Medicine and Dentistry
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5
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Vangala SR, Krishnan SR, Bung N, Srinivasan R, Roy A. pBRICS: A Novel Fragmentation Method for Explainable Property Prediction of Drug-Like Small Molecules. J Chem Inf Model 2023; 63:5066-5076. [PMID: 37585609 DOI: 10.1021/acs.jcim.3c00689] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Generative artificial intelligence algorithms have shown to be successful in exploring large chemical spaces and designing novel and diverse molecules. There has been considerable interest in developing predictive models using artificial intelligence for drug-like properties, which can potentially reduce the late-stage attrition of drug candidates or predict the properties of novel AI-designed molecules. Concurrently, it is important to understand the contribution of functional groups toward these properties and modify them to obtain property-optimized lead compounds. As a result, there is an increasing interest in the development of explainable property prediction models. However, current explainable approaches are mostly atom-based, where, often, only a fraction of a fragment is shown to be significant. To address the above challenges, we have developed a novel domain-aware molecular fragmentation approach termed post-processing of BRICS (pBRICS), which can fragment small molecules into their functional groups. Multitask models were developed to predict various properties, including the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The fragment importance was explained using the gradient-weighted class activation mapping (Grad-CAM) approach. The method was validated on data sets of experimentally available matched molecular pairs (MMPs). The explanations from the model can be useful for medicinal chemists to identify the fragments responsible for poor drug-like properties and optimize the molecule. The explainability approach was also used to identify the reason behind false positive and false negative MMP predictions. Based on evidence from the existing literature and our analysis, some of these mispredictions were justified. We propose that the quantity, quality, and diversity of the training data will improve the accuracy of property prediction algorithms for novel molecules.
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Affiliation(s)
- Sarveswara Rao Vangala
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | | | - Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Rajgopal Srinivasan
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
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6
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Fromer JC, Coley CW. Computer-aided multi-objective optimization in small molecule discovery. PATTERNS (NEW YORK, N.Y.) 2023; 4:100678. [PMID: 36873904 PMCID: PMC9982302 DOI: 10.1016/j.patter.2023.100678] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Molecular discovery is a multi-objective optimization problem that requires identifying a molecule or set of molecules that balance multiple, often competing, properties. Multi-objective molecular design is commonly addressed by combining properties of interest into a single objective function using scalarization, which imposes assumptions about relative importance and uncovers little about the trade-offs between objectives. In contrast to scalarization, Pareto optimization does not require knowledge of relative importance and reveals the trade-offs between objectives. However, it introduces additional considerations in algorithm design. In this review, we describe pool-based and de novo generative approaches to multi-objective molecular discovery with a focus on Pareto optimization algorithms. We show how pool-based molecular discovery is a relatively direct extension of multi-objective Bayesian optimization and how the plethora of different generative models extend from single-objective to multi-objective optimization in similar ways using non-dominated sorting in the reward function (reinforcement learning) or to select molecules for retraining (distribution learning) or propagation (genetic algorithms). Finally, we discuss some remaining challenges and opportunities in the field, emphasizing the opportunity to adopt Bayesian optimization techniques into multi-objective de novo design.
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Affiliation(s)
- Jenna C Fromer
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
| | - Connor W Coley
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA.,Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
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7
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Diao Y, Hu F, Shen Z, Li H. MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities. Bioinformatics 2023; 39:6986129. [PMID: 36637187 PMCID: PMC9872447 DOI: 10.1093/bioinformatics/btad012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 01/03/2023] [Accepted: 01/11/2023] [Indexed: 01/14/2023] Open
Abstract
SUMMARY Construction of high-quality fragment libraries by segmenting organic compounds is an important part of the drug discovery paradigm. This article presents a new method, MacFrag, for efficient molecule fragmentation. MacFrag utilized a modified version of BRICS rules to break chemical bonds and introduced an efficient subgraphs extraction algorithm for rapid enumeration of the fragment space. The evaluation results with ChEMBL dataset exhibited that MacFrag was overall faster than BRICS implemented in RDKit and modified molBLOCKS. Meanwhile, the fragments acquired through MacFrag were more compliant with the 'Rule of Three'. AVAILABILITY AND IMPLEMENTATION https://github.com/yydiao1025/MacFrag. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yanyan Diao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Feng Hu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zihao Shen
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Honglin Li
- To whom correspondence should be addressed.
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8
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Bhattamisra SK, Banerjee P, Gupta P, Mayuren J, Patra S, Candasamy M. Artificial Intelligence in Pharmaceutical and Healthcare Research. BIG DATA AND COGNITIVE COMPUTING 2023; 7:10. [DOI: 10.3390/bdcc7010010] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
Abstract
Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public.
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Affiliation(s)
- Subrat Kumar Bhattamisra
- Department of Pharmacology, GITAM School of Pharmacy, GITAM (Deemed to Be University), Visakhapatnam 530045, Andhra Pradesh, India
| | - Priyanka Banerjee
- Department of Pharmaceutical Technology, School of Medical Sciences, Adamas University, Kolkata 700126, West Bengal, India
| | - Pratibha Gupta
- Department of Pharmaceutical Technology, School of Medical Sciences, Adamas University, Kolkata 700126, West Bengal, India
| | - Jayashree Mayuren
- Department of Pharmaceutical Technology, School of Pharmacy, International Medical University, Kuala Lumpur 57000, Malaysia
| | - Susmita Patra
- Department of Pharmaceutical Technology, School of Medical Sciences, Adamas University, Kolkata 700126, West Bengal, India
| | - Mayuren Candasamy
- Department of Life Sciences, School of Pharmacy, International Medical University, Kuala Lumpur 57000, Malaysia
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9
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Rai A, Shah K, Dewangan HK. Review on the Artificial Intelligence-based Nanorobotics Targeted Drug Delivery System for Brain-specific Targeting. Curr Pharm Des 2023; 29:3519-3531. [PMID: 38111114 DOI: 10.2174/0113816128279248231210172053] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 11/07/2023] [Indexed: 12/20/2023]
Abstract
Contemporary medical research increasingly focuses on the blood-brain barrier (BBB) to maintain homeostasis in healthy individuals and provide solutions for neurological disorders, including brain cancer. Specialized in vitro modules replicate the BBB's complex structure and signalling using micro-engineered perfusion devices and advanced 3D cell cultures, thus advancing the understanding of neuropharmacology. This research explores nanoparticle-based biomolecular engineering for precise control, targeting, and transport of theranostic payloads across the BBB using nanorobots. The review summarizes case studies on delivering therapeutics for brain tumors and neurological disorders, such as Alzheimer's, Parkinson's, and multiple sclerosis. It also examines the advantages and disadvantages of nano-robotics. In conclusion, integrating machine learning and AI with robotics aims to develop safe nanorobots capable of interacting with the BBB without adverse effects. This comprehensive review is valuable for extensive analysis and is of great significance to healthcare professionals, engineers specializing in robotics, chemists, and bioengineers involved in pharmaceutical development and neurological research, emphasizing transdisciplinary approaches.
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Affiliation(s)
- Akriti Rai
- School of Pharmacy, Lingayas Vidyapeeth, Nachauli, Jasana Road, Faridabad, Haryana 121002, India
| | - Kamal Shah
- Institute of Pharmaceutical Research (IPR), GLA University Mathura, NH-2 Delhi Mathura Road, Po Chaumuhan, Mathura, Uttar Pradesh 281406, India
| | - Hitesh Kumar Dewangan
- University Institute of Pharma Sciences (UIPS), Chandigarh University, NH-95, Chandigarh Ludhiana Highway, Mohali, Punjab, India
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10
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Yoshizawa T, Ishida S, Sato T, Ohta M, Honma T, Terayama K. Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search. J Chem Inf Model 2022; 62:5351-5360. [PMID: 36334094 DOI: 10.1021/acs.jcim.2c00787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Tatsuya Yoshizawa
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama230-0045, Japan
| | - Shoichi Ishida
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama230-0045, Japan
| | - Tomohiro Sato
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama230-0045, Japan
| | - Masateru Ohta
- HPC- and AI-driven Drug Development Platform Division, Center for Computational Science, RIKEN, Yokohama230-0045, Japan
| | - Teruki Honma
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama230-0045, Japan
| | - Kei Terayama
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama230-0045, Japan
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11
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Perron Q, Mirguet O, Tajmouati H, Skiredj A, Rojas A, Gohier A, Ducrot P, Bourguignon MP, Sansilvestri-Morel P, Do Huu N, Gellibert F, Gaston-Mathé Y. Deep generative models for ligand-based de novo design applied to multi-parametric optimization. J Comput Chem 2022; 43:692-703. [PMID: 35218219 DOI: 10.1002/jcc.26826] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 01/26/2022] [Accepted: 01/27/2022] [Indexed: 11/08/2022]
Abstract
Multi-parameter optimization (MPO) is a major challenge in new chemical entity (NCE) drug discovery. Recently, promising results were reported for deep learning generative models applied to de novo molecular design, but, to our knowledge, until now no report was made of the value of this new technology for addressing MPO in an actual drug discovery project. In this study, we demonstrate the benefit of applying AI technology in a real drug discovery project. We evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the obtention of lead compounds meeting 11 different biological activity objectives simultaneously. Using the initial dataset of the project, we built QSAR models for all the 11 objectives, with moderate to high performance (precision between 0.67 and 1.0 on an independent test set). Our DL-based AI de novo design algorithm, combined with the QSAR models, generated 150 virtual compounds predicted as active on all objectives. Eleven were synthetized and tested. The AI-designed compounds met 9.5 objectives on average (i.e., 86% success rate) versus 6.4 (i.e., 58% success rate) for the initial molecules measured on all objectives. One of the AI-designed molecules was active on all 11 measured objectives, and two were active on 10 objectives while being in the error margin of the assay for the last one. The AI algorithm designed compounds with functional groups, which, although being rare or absent in the initial dataset, turned out to be highly beneficial for the MPO.
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Affiliation(s)
| | - Olivier Mirguet
- Institut De Recherches Servier, Suresnes, France.,Institut De Recherches Servier, Croissy, France
| | | | | | - Anne Rojas
- Institut De Recherches Servier, Suresnes, France.,Institut De Recherches Servier, Croissy, France
| | - Arnaud Gohier
- Institut De Recherches Servier, Suresnes, France.,Institut De Recherches Servier, Croissy, France
| | - Pierre Ducrot
- Institut De Recherches Servier, Suresnes, France.,Institut De Recherches Servier, Croissy, France
| | - Marie-Pierre Bourguignon
- Institut De Recherches Servier, Suresnes, France.,Institut De Recherches Servier, Croissy, France
| | | | | | - Françoise Gellibert
- Institut De Recherches Servier, Suresnes, France.,Institut De Recherches Servier, Croissy, France
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12
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Cincilla G, Masoni S, Blobel J. Individual and collective human intelligence in drug design: evaluating the search strategy. J Cheminform 2021; 13:80. [PMID: 34635158 PMCID: PMC8507178 DOI: 10.1186/s13321-021-00556-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 09/18/2021] [Indexed: 11/10/2022] Open
Abstract
In recent years, individual and collective human intelligence, defined as the knowledge, skills, reasoning and intuition of individuals and groups, have been used in combination with computer algorithms to solve complex scientific problems. Such approach was successfully used in different research fields such as: structural biology, comparative genomics, macromolecular crystallography and RNA design. Herein we describe an attempt to use a similar approach in small-molecule drug discovery, specifically to drive search strategies of de novo drug design. This is assessed with a case study that consists of a series of public experiments in which participants had to explore the huge chemical space in silico to find predefined compounds by designing molecules and analyzing the score associate with them. Such a process may be seen as an instantaneous surrogate of the classical design-make-test cycles carried out by medicinal chemists during the drug discovery hit to lead phase but not hindered by long synthesis and testing times. We present first findings on (1) assessing human intelligence in chemical space exploration, (2) comparing individual and collective human intelligence performance in this task and (3) contrasting some human and artificial intelligence achievements in de novo drug design.
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Affiliation(s)
- Giovanni Cincilla
- Molomics, Barcelona Science Park, c/Baldiri i Reixac 4-12, 08028, Barcelona, Spain.
| | - Simone Masoni
- Molomics, Barcelona Science Park, c/Baldiri i Reixac 4-12, 08028, Barcelona, Spain.
| | - Jascha Blobel
- Molomics, Barcelona Science Park, c/Baldiri i Reixac 4-12, 08028, Barcelona, Spain.
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13
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Nichols PL. Automated and enabling technologies for medicinal chemistry. PROGRESS IN MEDICINAL CHEMISTRY 2021; 60:191-272. [PMID: 34147203 DOI: 10.1016/bs.pmch.2021.01.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Having always been driven by the need to get new treatments to patients as quickly as possible, drug discovery is a constantly evolving process. This chapter will review how medicinal chemistry was established, how it has changed over the years due to the emergence of new enabling technologies, and how early advances in synthesis, purification and analysis, have provided the foundations upon which the current automated and enabling technologies are built. Looking beyond the established technologies, this chapter will also consider technologies that are now emerging, and their impact on the future of drug discovery and the role of medicinal chemists.
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Affiliation(s)
- Paula L Nichols
- Synple Chem AG, Kemptthal, Switzerland; ETH, Zurich, Switzerland.
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14
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Meyers J, Fabian B, Brown N. De novo molecular design and generative models. Drug Discov Today 2021; 26:2707-2715. [PMID: 34082136 DOI: 10.1016/j.drudis.2021.05.019] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/21/2021] [Accepted: 05/26/2021] [Indexed: 02/09/2023]
Abstract
Molecular design strategies are integral to therapeutic progress in drug discovery. Computational approaches for de novo molecular design have been developed over the past three decades and, recently, thanks in part to advances in machine learning (ML) and artificial intelligence (AI), the drug discovery field has gained practical experience. Here, we review these learnings and present de novo approaches according to the coarseness of their molecular representation: that is, whether molecular design is modeled on an atom-based, fragment-based, or reaction-based paradigm. Furthermore, we emphasize the value of strong benchmarks, describe the main challenges to using these methods in practice, and provide a viewpoint on further opportunities for exploration and challenges to be tackled in the upcoming years.
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Affiliation(s)
| | | | - Nathan Brown
- BenevolentAI, 4-8 Maple Street, London W1T 5HD, UK
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15
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16
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Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today 2021; 26:80-93. [PMID: 33099022 PMCID: PMC7577280 DOI: 10.1016/j.drudis.2020.10.010] [Citation(s) in RCA: 417] [Impact Index Per Article: 104.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 09/03/2020] [Accepted: 10/13/2020] [Indexed: 02/07/2023]
Abstract
Artificial intelligence-integrated drug discovery and development has accelerated the growth of the pharmaceutical sector, leading to a revolutionary change in the pharma industry. Here, we discuss areas of integration, tools, and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them.
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Affiliation(s)
- Debleena Paul
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Gaurav Sanap
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Snehal Shenoy
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Dnyaneshwar Kalyane
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Kiran Kalia
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Rakesh K Tekade
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India.
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Lambrinidis G, Tsantili-Kakoulidou A. Multi-objective optimization methods in novel drug design. Expert Opin Drug Discov 2020; 16:647-658. [PMID: 33353441 DOI: 10.1080/17460441.2021.1867095] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Introduction: In multi-objective drug design, optimization gains importance, being upgraded to a discipline that attracts its own research. Current strategies are broadly classified into single - objective optimization (SOO) and multi-objective optimization (MOO).Areas covered: Starting with SOO and the ways used to incorporate multiple criteria into it, the present review focuses on MOO techniques, their comparison, advantages, and restrictions. Pareto analysis and the concept of dominance stand in the core of MOO. The Pareto front, Pareto ranking, and limitations of Pareto-based methods, due to high dimensions and data uncertainty, are outlined. Desirability functions and the weighted sum approaches are described as stand-alone techniques to transform the MOO problem to SOO or in combination with pareto analysis and evolutionary algorithms. Representative applications in different drug research areas are also discussed.Expert opinion: Despite their limitations, the use of combined MOO techniques, as well as being complementary to SOO or in conjunction with artificial intelligence, contributes dramatically to efficient drug design, assisting decisions and increasing success probabilities. For multi-target drug design, optimization is supported by network approaches, while applicability of MOO to other fields like drug technology or biological complexity opens new perspectives in the interrelated fields of medicinal chemistry and molecular biology.
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Affiliation(s)
- George Lambrinidis
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Zografou, Athens, Greece
| | - Anna Tsantili-Kakoulidou
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Zografou, Athens, Greece
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18
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Vanhaelen Q, Lin YC, Zhavoronkov A. The Advent of Generative Chemistry. ACS Med Chem Lett 2020; 11:1496-1505. [PMID: 32832015 PMCID: PMC7429972 DOI: 10.1021/acsmedchemlett.0c00088] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/14/2020] [Indexed: 12/12/2022] Open
Abstract
Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for de novo molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry.
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Affiliation(s)
- Quentin Vanhaelen
- Insilico
Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Yen-Chu Lin
- Insilico
Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
- Insilico
Taiwan, Taipei City 115, Taiwan, R.O.C
| | - Alex Zhavoronkov
- Insilico
Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
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19
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Watson OP, Cortes-Ciriano I, Taylor AR, Watson JA. A decision-theoretic approach to the evaluation of machine learning algorithms in computational drug discovery. Bioinformatics 2020; 35:4656-4663. [PMID: 31070704 PMCID: PMC6853675 DOI: 10.1093/bioinformatics/btz293] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 03/22/2019] [Accepted: 04/17/2019] [Indexed: 02/07/2023] Open
Abstract
Motivation Artificial intelligence, trained via machine learning (e.g. neural nets, random forests) or computational statistical algorithms (e.g. support vector machines, ridge regression), holds much promise for the improvement of small-molecule drug discovery. However, small-molecule structure-activity data are high dimensional with low signal-to-noise ratios and proper validation of predictive methods is difficult. It is poorly understood which, if any, of the currently available machine learning algorithms will best predict new candidate drugs. Results The quantile-activity bootstrap is proposed as a new model validation framework using quantile splits on the activity distribution function to construct training and testing sets. In addition, we propose two novel rank-based loss functions which penalize only the out-of-sample predicted ranks of high-activity molecules. The combination of these methods was used to assess the performance of neural nets, random forests, support vector machines (regression) and ridge regression applied to 25 diverse high-quality structure-activity datasets publicly available on ChEMBL. Model validation based on random partitioning of available data favours models that overfit and ‘memorize’ the training set, namely random forests and deep neural nets. Partitioning based on quantiles of the activity distribution correctly penalizes extrapolation of models onto structurally different molecules outside of the training data. Simpler, traditional statistical methods such as ridge regression can outperform state-of-the-art machine learning methods in this setting. In addition, our new rank-based loss functions give considerably different results from mean squared error highlighting the necessity to define model optimality with respect to the decision task at hand. Availability and implementation All software and data are available as Jupyter notebooks found at https://github.com/owatson/QuantileBootstrap. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Isidro Cortes-Ciriano
- Goring on Thames, Evariste Technologies Ltd., RG8 9AL UK.,Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Aimee R Taylor
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA 02115 USA.,Infectious Disease Microbiome Program, Broad Institute, Cambridge, MA 02142 USA
| | - James A Watson
- Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford OX3, 7LF UK.,Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand
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20
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Coley CW, Eyke NS, Jensen KF. Autonomous Discovery in the Chemical Sciences Part I: Progress. Angew Chem Int Ed Engl 2020; 59:22858-22893. [DOI: 10.1002/anie.201909987] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Indexed: 01/05/2023]
Affiliation(s)
- Connor W. Coley
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Natalie S. Eyke
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
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21
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Coley CW, Eyke NS, Jensen KF. Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.201909987] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Connor W. Coley
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Natalie S. Eyke
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
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22
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Ghanakota P, Bos PH, Konze KD, Staker J, Marques G, Marshall K, Leswing K, Abel R, Bhat S. Combining Cloud-Based Free-Energy Calculations, Synthetically Aware Enumerations, and Goal-Directed Generative Machine Learning for Rapid Large-Scale Chemical Exploration and Optimization. J Chem Inf Model 2020; 60:4311-4325. [PMID: 32484669 DOI: 10.1021/acs.jcim.0c00120] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Phani Ghanakota
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Pieter H. Bos
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Kyle D. Konze
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Joshua Staker
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Gabriel Marques
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Kyle Marshall
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Karl Leswing
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Robert Abel
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Sathesh Bhat
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
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23
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Polishchuk P. CReM: chemically reasonable mutations framework for structure generation. J Cheminform 2020; 12:28. [PMID: 33430959 PMCID: PMC7178718 DOI: 10.1186/s13321-020-00431-w] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 04/15/2020] [Indexed: 12/12/2022] Open
Abstract
Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space.
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Affiliation(s)
- Pavel Polishchuk
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University and University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic.
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24
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Konze KD, Bos PH, Dahlgren MK, Leswing K, Tubert-Brohman I, Bortolato A, Robbason B, Abel R, Bhat S. Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors. J Chem Inf Model 2019; 59:3782-3793. [PMID: 31404495 DOI: 10.1021/acs.jcim.9b00367] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The hit-to-lead and lead optimization processes usually involve the design, synthesis, and profiling of thousands of analogs prior to clinical candidate nomination. A hit finding campaign may begin with a virtual screen that explores millions of compounds, if not more. However, this scale of computational profiling is not frequently performed in the hit-to-lead or lead optimization phases of drug discovery. This is likely due to the lack of appropriate computational tools to generate synthetically tractable lead-like compounds in silico, and a lack of computational methods to accurately profile compounds prospectively on a large scale. Recent advances in computational power and methods provide the ability to profile much larger libraries of ligands than previously possible. Herein, we report a new computational technique, referred to as "PathFinder", that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. In this work, the integration of PathFinder-driven compound generation, cloud-based FEP simulations, and active learning are used to rapidly optimize R-groups, and generate new cores for inhibitors of cyclin-dependent kinase 2 (CDK2). Using this approach, we explored >300 000 ideas, performed >5000 FEP simulations, and identified >100 ligands with a predicted IC50 < 100 nM, including four unique cores. To our knowledge, this is the largest set of FEP calculations disclosed in the literature to date. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.
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Affiliation(s)
- Kyle D Konze
- Schrödinger Inc. , 120 West 45th Street, 17th floor , New York , New York 10036 , United States
| | - Pieter H Bos
- Schrödinger Inc. , 120 West 45th Street, 17th floor , New York , New York 10036 , United States
| | - Markus K Dahlgren
- Schrödinger Inc. , 120 West 45th Street, 17th floor , New York , New York 10036 , United States
| | - Karl Leswing
- Schrödinger Inc. , 120 West 45th Street, 17th floor , New York , New York 10036 , United States
| | - Ivan Tubert-Brohman
- Schrödinger Inc. , 120 West 45th Street, 17th floor , New York , New York 10036 , United States
| | - Andrea Bortolato
- Schrödinger Inc. , 120 West 45th Street, 17th floor , New York , New York 10036 , United States
| | - Braxton Robbason
- Schrödinger Inc. , 120 West 45th Street, 17th floor , New York , New York 10036 , United States
| | - Robert Abel
- Schrödinger Inc. , 120 West 45th Street, 17th floor , New York , New York 10036 , United States
| | - Sathesh Bhat
- Schrödinger Inc. , 120 West 45th Street, 17th floor , New York , New York 10036 , United States
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26
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Can we accelerate medicinal chemistry by augmenting the chemist with Big Data and artificial intelligence? Drug Discov Today 2018; 23:1373-1384. [DOI: 10.1016/j.drudis.2018.03.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 02/27/2018] [Accepted: 03/20/2018] [Indexed: 12/18/2022]
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27
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Discovery of selective EGFR modulator to inhibit L858R/T790M double mutants bearing a N-9-Diphenyl-9H-purin-2-amine scaffold. Bioorg Med Chem 2018; 26:1810-1822. [DOI: 10.1016/j.bmc.2018.02.029] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 02/15/2018] [Accepted: 02/17/2018] [Indexed: 12/21/2022]
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Heikamp K, Zuccotto F, Kiczun M, Ray P, Gilbert IH. Exhaustive sampling of the fragment space associated to a molecule leading to the generation of conserved fragments. Chem Biol Drug Des 2018; 91:655-667. [PMID: 29063731 PMCID: PMC5836963 DOI: 10.1111/cbdd.13129] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 10/09/2017] [Accepted: 10/14/2017] [Indexed: 11/28/2022]
Abstract
The first step in hit optimization is the identification of the pharmacophore, which is normally achieved by deconstruction of the hit molecule to generate "deletion analogues." In silico fragmentation approaches often focus on the generation of small fragments that do not describe properly the fragment space associated to the deletion analogues. We present significant modifications to the molecular fragmentation programme molBLOCKS, which allows the exhaustive sampling of the fragment space associated with a molecule to generate all possible molecular fragments. This generates larger fragments, by combining the smallest fragments. Additionally, it has been modified to deal with the problem of changing pharmacophoric properties through fragmentation, by highlighting bond cuts. The modified molBLOCKS programme was used on a set of drug compounds, where it generated more unique fragments than standard fragmentation approaches by increasing the number of fragments derived per compound. This fragment set was found to be more diverse than those generated by standard fragmentation programmes and was relevant to drug discovery as it contains the key fragments representing the pharmacophoric elements associated with ligand recognition. The use of dummy atoms to highlight bond cuts further increases the information content of fragments by visualizing their previous bonding pattern.
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Affiliation(s)
- Kathrin Heikamp
- Drug Discovery UnitDivision of Biological Chemistry and Drug DiscoverySchool of Life SciencesUniversity of DundeeDundeeScotland, UK
| | - Fabio Zuccotto
- Drug Discovery UnitDivision of Biological Chemistry and Drug DiscoverySchool of Life SciencesUniversity of DundeeDundeeScotland, UK
| | - Michael Kiczun
- Drug Discovery UnitDivision of Biological Chemistry and Drug DiscoverySchool of Life SciencesUniversity of DundeeDundeeScotland, UK
| | - Peter Ray
- Drug Discovery UnitDivision of Biological Chemistry and Drug DiscoverySchool of Life SciencesUniversity of DundeeDundeeScotland, UK
| | - Ian H. Gilbert
- Drug Discovery UnitDivision of Biological Chemistry and Drug DiscoverySchool of Life SciencesUniversity of DundeeDundeeScotland, UK
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Lombardo F, Desai PV, Arimoto R, Desino KE, Fischer H, Keefer CE, Petersson C, Winiwarter S, Broccatelli F. In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development. J Med Chem 2017; 60:9097-9113. [DOI: 10.1021/acs.jmedchem.7b00487] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Franco Lombardo
- Alkermes Inc., 852 Winter Street, Waltham, Massachusetts 02451, United States
| | - Prashant V. Desai
- Computational
ADME, Drug Disposition, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Rieko Arimoto
- Vertex Pharmaceuticals Inc., 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | | | - Holger Fischer
- Roche
Pharmaceutical Research and Early Development, Pharmaceutical Sciences,
Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | | | - Carl Petersson
- Discovery Drug Disposition, Biopharma, R&D Global Early Development, EMD Serono, Frankfurter Strasse 250 I Postcode D39/001, 64293 Darmstadt, Germany
| | - Susanne Winiwarter
- Drug Safety and Metabolism, AstraZeneca R&D Gothenburg, 431 83 Mölndal, Sweden
| | - Fabio Broccatelli
- Genentech Inc., South San Francisco, California 94080, United States
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Meyers J, Carter M, Mok NY, Brown N. On the origins of three-dimensionality in drug-like molecules. Future Med Chem 2016; 8:1753-67. [PMID: 27572621 PMCID: PMC5796639 DOI: 10.4155/fmc-2016-0095] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 07/01/2016] [Indexed: 01/18/2023] Open
Abstract
AIM Many medicinal chemistry-relevant structures and core scaffolds tend toward geometric planarity, which hampers the optimization of physicochemical properties desirable in drug-like molecules. As challenging drug target classes emerge, the exploitation of molecular three-dimensionality in lead optimization is becoming increasingly important. While recent interest has emphasized the importance of enhanced three-dimensionality in molecular fragment designs, the extent to which this is required in core scaffolds remains unclear. MATERIALS & METHODS Three computational methods, Scaffold Tree deconstruction, Synthetic Disconnection Rules retrosynthetic deconstruction and virtual library enumeration, are applied, together with the descriptors plane of best fit and principal moments of inertia, to investigate the origins of three-dimensionality in drug-like molecules. CONCLUSION This study informs on the stage at which molecular three-dimensionality should be considered in drug design.
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Affiliation(s)
- Joshua Meyers
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer
Therapeutics, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Michael Carter
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer
Therapeutics, The Institute of Cancer Research, London, SM2 5NG, UK
| | - N. Yi Mok
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer
Therapeutics, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Nathan Brown
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer
Therapeutics, The Institute of Cancer Research, London, SM2 5NG, UK
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