1
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Renz P, Luukkonen S, Klambauer G. Diverse Hits in De Novo Molecule Design: Diversity-Based Comparison of Goal-Directed Generators. J Chem Inf Model 2024; 64:5756-5761. [PMID: 39029090 PMCID: PMC11323242 DOI: 10.1021/acs.jcim.4c00519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 07/10/2024] [Accepted: 07/11/2024] [Indexed: 07/21/2024]
Abstract
Since the rise of generative AI models, many goal-directed molecule generators have been proposed as tools for discovering novel drug candidates. However, molecule generators often produce highly similar molecules and tend to overemphasize conformity to an imperfect scoring function rather than capturing the true underlying properties sought. We rectify these two shortcomings by offering diversity-based evaluations using the #Circles metric and considering constraints on scoring function calls or computation time. Our findings highlight the superior performance of SMILES-based autoregressive models in generating diverse sets of desired molecules compared to graph-based models or genetic algorithms.
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Affiliation(s)
- Philipp Renz
- Johannes Kepler University Linz, Altenbergerstraße 69, Linz, AT 4040, Austria
| | - Sohvi Luukkonen
- Johannes Kepler University Linz, ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Altenbergerstraße 69, Linz, AT 4040, Austria
| | - Günter Klambauer
- Johannes Kepler University Linz, ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Altenbergerstraße 69, Linz, AT 4040, Austria
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2
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Hu X, Liu G, Yao Q, Zhao Y, Zhang H. Hamiltonian diversity: effectively measuring molecular diversity by shortest Hamiltonian circuits. J Cheminform 2024; 16:94. [PMID: 39113120 PMCID: PMC11308660 DOI: 10.1186/s13321-024-00883-4] [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: 02/08/2024] [Accepted: 07/11/2024] [Indexed: 08/10/2024] Open
Abstract
In recent years, significant advancements have been made in molecular generation algorithms aimed at facilitating drug development, and molecular diversity holds paramount importance within the realm of molecular generation. Nonetheless, the effective quantification of molecular diversity remains an elusive challenge, as extant metrics exemplified by Richness and Internal Diversity fall short in concurrently encapsulating the two main aspects of such diversity: quantity and dissimilarity. To address this quandary, we propose Hamiltonian diversity, a novel molecular diversity metric predicated upon the shortest Hamiltonian circuit. This metric embodies both aspects of molecular diversity in principle, and we implement its calculation with high efficiency and accuracy. Furthermore, through empirical experiments we demonstrate the high consistency of Hamiltonian diversity with real-world chemical diversity, and substantiate its effects in promoting diversity of molecular generation algorithms. Our implementation of Hamiltonian diversity in Python is available at: https://github.com/HXYfighter/HamDiv .Scientific contributionWe propose a more rational molecular diversity metric for the community of cheminformatics and drug development. This metric can be applied to evaluation of existing molecular generation methods and enhancing drug design algorithms.
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Affiliation(s)
- Xiuyuan Hu
- Department of Electronic Engineering, Tsinghua University, Beijing, China
- Microsoft Research AI for Science, Beijing, China
| | - Guoqing Liu
- Microsoft Research AI for Science, Beijing, China
| | - Quanming Yao
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Yang Zhao
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Hao Zhang
- Department of Electronic Engineering, Tsinghua University, Beijing, China.
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3
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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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4
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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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5
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Liu Y, Yu H, Duan X, Zhang X, Cheng T, Jiang F, Tang H, Ruan Y, Zhang M, Zhang H, Zhang Q. TransGEM: a molecule generation model based on Transformer with gene expression data. Bioinformatics 2024; 40:btae189. [PMID: 38632084 PMCID: PMC11078772 DOI: 10.1093/bioinformatics/btae189] [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: 11/21/2023] [Revised: 03/26/2024] [Accepted: 04/16/2024] [Indexed: 04/19/2024] Open
Abstract
MOTIVATION It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information. RESULTS We propose TransGEM (Transformer-based model from gene expression to molecules), which is a phenotype-based de novo drug design model. A specialized gene expression encoder is used to embed gene expression difference values between diseased cell lines and their corresponding normal tissue cells into TransGEM model. The results demonstrate that the TransGEM model can generate molecules with desirable evaluation metrics and property distributions. Case studies illustrate that TransGEM model can generate structurally novel molecules with good binding affinity to disease target proteins. The majority of genes with high attention scores obtained from TransGEM model are associated with the onset of the disease, indicating the potential of these genes as disease targets. Therefore, this study provides a new paradigm for de novo drug design, and it will promote phenotype-based drug discovery. AVAILABILITY AND IMPLEMENTATION The code is available at https://github.com/hzauzqy/TransGEM.
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Affiliation(s)
- Yanguang Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Hailong Yu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xinya Duan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xiaomin Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Ting Cheng
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Feng Jiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Hao Tang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Yao Ruan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Miao Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Hongyu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Qingye Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
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6
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Karandashev K, Weinreich J, Heinen S, Arismendi Arrieta DJ, von Rudorff GF, Hermansson K, von Lilienfeld OA. Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design. J Chem Theory Comput 2023; 19:8861-8870. [PMID: 38009856 PMCID: PMC10720348 DOI: 10.1021/acs.jctc.3c00822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/29/2023] [Accepted: 10/30/2023] [Indexed: 11/29/2023]
Abstract
Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science but also a very difficult one due to the vast number of possible molecular systems. We propose an evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favorable properties of genetic algorithms. The method, dubbed MOSAiCS (Metropolis Optimization by Sampling Adaptively in Chemical Space), is tested on problems related to optimizing components of battery electrolytes, namely, minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO-LUMO gap; optimization was carried out over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) data sets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to 106 QM calculations and were thus feasible only thanks to computationally efficient ab initio approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches.
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Affiliation(s)
| | - Jan Weinreich
- Faculty
of Physics, University of Vienna, Kolingasse 14-16, AT-1090 Wien, Austria
| | - Stefan Heinen
- Vector
Institute for Artificial Intelligence, Toronto, M5S 1M1 Ontario, Canada
| | | | - Guido Falk von Rudorff
- Department
of Chemistry, University Kassel, Heinrich-Plett-Str.40, 34132 Kassel, Germany
- Center
for Interdisciplinary Nanostructure Science and Technology (CINSaT), Heinrich-Plett-Straße 40, 34132 Kassel, Germany
| | - Kersti Hermansson
- Department
of Chemistry-Ångström Laboratory, Uppsala University, Box 538, SE-75121 Uppsala, Sweden
| | - O. Anatole von Lilienfeld
- Vector
Institute for Artificial Intelligence, Toronto, M5S 1M1 Ontario, Canada
- Departments
of Chemistry, Materials Science and Engineering, and Physics, University of Toronto, St. George
Campus, Toronto, M5S 1A1 Ontario, Canada
- Machine
Learning Group, Technische Universität
Berlin and Institute for the Foundations of Learning and Data, 10587 Berlin, Germany
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7
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Pereira TO, Abbasi M, Oliveira RI, Guedes RA, Salvador JAR, Arrais JP. Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information. J Comput Aided Mol Des 2023; 37:791-806. [PMID: 37847342 PMCID: PMC10618333 DOI: 10.1007/s10822-023-00539-9] [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: 01/31/2023] [Accepted: 10/02/2023] [Indexed: 10/18/2023]
Abstract
In this work, we develop a method for generating targeted hit compounds by applying deep reinforcement learning and attention mechanisms to predict binding affinity against a biological target while considering stereochemical information. The novelty of this work is a deep model Predictor that can establish the relationship between chemical structures and their corresponding [Formula: see text] values. We thoroughly study the effect of different molecular descriptors such as ECFP4, ECFP6, SMILES and RDKFingerprint. Also, we demonstrated the importance of attention mechanisms to capture long-range dependencies in molecular sequences. Due to the importance of stereochemical information for the binding mechanism, this information was employed both in the prediction and generation processes. To identify the most promising hits, we apply the self-adaptive multi-objective optimization strategy. Moreover, to ensure the existence of stereochemical information, we consider all the possible enumerated stereoisomers to provide the most appropriate 3D structures. We evaluated this approach against the Ubiquitin-Specific Protease 7 (USP7) by generating putative inhibitors for this target. The predictor with SMILES notations as descriptor plus bidirectional recurrent neural network using attention mechanism has the best performance. Additionally, our methodology identify the regions of the generated molecules that are important for the interaction with the receptor's active site. Also, the obtained results demonstrate that it is possible to discover synthesizable molecules with high biological affinity for the target, containing the indication of their optimal stereochemical conformation.
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Affiliation(s)
- Tiago O Pereira
- Centre for Informatics and Systems, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal.
| | - Maryam Abbasi
- Centre for Informatics and Systems, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
- Applied Research Institute, Polytechnic Institute of Coimbra, Coimbra, Portugal
- Research Centre for Natural Resources Environment and Society (CERNAS), Polytechnic Institute of Coimbra, Coimbra, Portugal
| | - Rita I Oliveira
- Laboratory of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Coimbra, Coimbra, Portugal
- Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, Coimbra, Portugal
| | - Romina A Guedes
- Laboratory of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Coimbra, Coimbra, Portugal
- Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, Coimbra, Portugal
| | - Jorge A R Salvador
- Laboratory of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Coimbra, Coimbra, Portugal
- Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, Coimbra, Portugal
| | - Joel P Arrais
- Centre for Informatics and Systems, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
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8
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Ilnicka A, Schneider G. Designing molecules with autoencoder networks. NATURE COMPUTATIONAL SCIENCE 2023; 3:922-933. [PMID: 38177601 DOI: 10.1038/s43588-023-00548-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 10/03/2023] [Indexed: 01/06/2024]
Abstract
Autoencoders are versatile tools in molecular informatics. These unsupervised neural networks serve diverse tasks such as data-driven molecular representation and constructive molecular design. This Review explores their algorithmic foundations and applications in drug discovery, highlighting the most active areas of development and the contributions autoencoder networks have made in advancing this field. We also explore the challenges and prospects concerning the utilization of autoencoders and the various adaptations of this neural network architecture in molecular design.
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Affiliation(s)
- Agnieszka Ilnicka
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland.
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9
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Al-Jumaily A, Mukaidaisi M, Vu A, Tchagang A, Li Y. Examining multi-objective deep reinforcement learning frameworks for molecular design. Biosystems 2023; 232:104989. [PMID: 37544406 DOI: 10.1016/j.biosystems.2023.104989] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 07/27/2023] [Indexed: 08/08/2023]
Abstract
Drug design and optimization are challenging tasks that call for strategic and efficient exploration of the extremely vast search space. Multiple fragmentation strategies have been proposed in the literature to mitigate the complexity of the molecular search space. From an optimization standpoint, drug design can be considered as a multi-objective optimization problem. Deep reinforcement learning (DRL) frameworks have demonstrated encouraging results in the field of drug design. However, the scalability of these frameworks is impeded by substantial training intervals and inefficient use of sample data. In this paper, we (1) examine the core principles of deep or multi-objective RL methods and their applications in molecular design, (2) analyze the performance of a recent multi-objective DRL-based and fragment-based drug design framework, named DeepFMPO, in a real-world application by incorporating optimization of protein-ligand docking affinity with varying numbers of other objectives, and (3) compare this method with a single-objective variant. Through trials, our results indicate that the DeepFMPO framework (with docking score) can achieve success, however, it suffers from training instability. Our findings encourage additional exploration and improvement of the framework. Potential sources of the framework's instability and suggestions of further modifications to stabilize the framework are discussed.
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Affiliation(s)
- Aws Al-Jumaily
- Department of Computer Science, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, L2S 3A1, Ontario, Canada.
| | - Muhetaer Mukaidaisi
- Department of Computer Science, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, L2S 3A1, Ontario, Canada.
| | - Andrew Vu
- Department of Computer Science, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, L2S 3A1, Ontario, Canada.
| | - Alain Tchagang
- Digital Technologies Research Centre, National Research Council Canada, 1200 Montreal Road, Ottawa, K1A 0R6, Ontario, Canada.
| | - Yifeng Li
- Department of Computer Science, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, L2S 3A1, Ontario, Canada.
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10
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Bae B, Bae H, Nam H. LOGICS: Learning optimal generative distribution for designing de novo chemical structures. J Cheminform 2023; 15:77. [PMID: 37674239 PMCID: PMC10483765 DOI: 10.1186/s13321-023-00747-3] [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: 04/06/2023] [Accepted: 08/23/2023] [Indexed: 09/08/2023] Open
Abstract
In recent years, the field of computational drug design has made significant strides in the development of artificial intelligence (AI) models for the generation of de novo chemical compounds with desired properties and biological activities, such as enhanced binding affinity to target proteins. These high-affinity compounds have the potential to be developed into more potent therapeutics for a broad spectrum of diseases. Due to the lack of data required for the training of deep generative models, however, some of these approaches have fine-tuned their molecular generators using data obtained from a separate predictor. While these studies show that generative models can produce structures with the desired target properties, it remains unclear whether the diversity of the generated structures and the span of their chemical space align with the distribution of the intended target molecules. In this study, we present a novel generative framework, LOGICS, a framework for Learning Optimal Generative distribution Iteratively for designing target-focused Chemical Structures. We address the exploration-exploitation dilemma, which weighs the choice between exploring new options and exploiting current knowledge. To tackle this issue, we incorporate experience memory and employ a layered tournament selection approach to refine the fine-tuning process. The proposed method was applied to the binding affinity optimization of two target proteins of different protein classes, κ-opioid receptors, and PIK3CA, and the quality and the distribution of the generative molecules were evaluated. The results showed that LOGICS outperforms competing state-of-the-art models and generates more diverse de novo chemical structures with optimized properties. The source code is available at the GitHub repository ( https://github.com/GIST-CSBL/LOGICS ).
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Affiliation(s)
- Bongsung Bae
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-Gu, Gwangju, 61005, Republic of Korea
| | - Haelee Bae
- AI Graduate School, Gwangju Institute of Science and Technology (GIST), Buk-Gu, Gwangju, 61005, Republic of Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-Gu, Gwangju, 61005, Republic of Korea.
- AI Graduate School, Gwangju Institute of Science and Technology (GIST), Buk-Gu, Gwangju, 61005, Republic of Korea.
- Center for AI-Applied High Efficiency Drug Discovery (AHEDD), Gwangju Institute of Science and Technology (GIST), Buk-Gu, Gwangju, 61005, Republic of Korea.
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11
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Elsborg J, Bhowmik A. Equivariant Graph-Representation-Based Actor-Critic Reinforcement Learning for Nanoparticle Design. J Chem Inf Model 2023. [PMID: 37276140 DOI: 10.1021/acs.jcim.3c00394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We have developed an actor-critic-type policy-based reinforcement learning (RL) method to find low-energy nanoparticle structures and compared its effectiveness to classical basin-hopping. We took a molecule building approach where nanoalloy particles can be regarded as metallic molecules, albeit with much higher flexibility in structure. We explore the strengths of our approach by tasking an agent with the construction of stable mono- and bimetallic clusters. Following physics, an appropriate reward function and an equivariant molecular graph representation framework is used to learn the policy. The agent succeeds in finding well-known stable configuration for small clusters in both single and multicluster experiments. However, for certain use cases the agent lacks generalization to avoid overfitting. We relate this to the pitfalls of actor-critic methods for molecular design and discuss what learning properties an agent will require to achieve universality.
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Affiliation(s)
- Jonas Elsborg
- Department of Energy Conversion and Storage, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Arghya Bhowmik
- Department of Energy Conversion and Storage, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
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12
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Dost K, Pullar-Strecker Z, Brydon L, Zhang K, Hafner J, Riddle PJ, Wicker JS. Combatting over-specialization bias in growing chemical databases. J Cheminform 2023; 15:53. [PMID: 37208694 DOI: 10.1186/s13321-023-00716-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 03/25/2023] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND Predicting in advance the behavior of new chemical compounds can support the design process of new products by directing the research toward the most promising candidates and ruling out others. Such predictive models can be data-driven using Machine Learning or based on researchers' experience and depend on the collection of past results. In either case: models (or researchers) can only make reliable assumptions about compounds that are similar to what they have seen before. Therefore, consequent usage of these predictive models shapes the dataset and causes a continuous specialization shrinking the applicability domain of all trained models on this dataset in the future, and increasingly harming model-based exploration of the space. PROPOSED SOLUTION In this paper, we propose CANCELS (CounterActiNg Compound spEciaLization biaS), a technique that helps to break the dataset specialization spiral. Aiming for a smooth distribution of the compounds in the dataset, we identify areas in the space that fall short and suggest additional experiments that help bridge the gap. Thereby, we generally improve the dataset quality in an entirely unsupervised manner and create awareness of potential flaws in the data. CANCELS does not aim to cover the entire compound space and hence retains a desirable degree of specialization to a specified research domain. RESULTS An extensive set of experiments on the use-case of biodegradation pathway prediction not only reveals that the bias spiral can indeed be observed but also that CANCELS produces meaningful results. Additionally, we demonstrate that mitigating the observed bias is crucial as it cannot only intervene with the continuous specialization process, but also significantly improves a predictor's performance while reducing the number of required experiments. Overall, we believe that CANCELS can support researchers in their experimentation process to not only better understand their data and potential flaws, but also to grow the dataset in a sustainable way. All code is available under github.com/KatDost/Cancels .
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Affiliation(s)
- Katharina Dost
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand.
- enviPath UG & Co. KG, In den Graswiesen 13, 55437, Ockenheim, Germany.
| | - Zac Pullar-Strecker
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
| | - Liam Brydon
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
| | - Kunyang Zhang
- Eawag-Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600, Dübendorf, Switzerland
| | - Jasmin Hafner
- Eawag-Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600, Dübendorf, Switzerland
| | - Patricia J Riddle
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
| | - Jörg S Wicker
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
- enviPath UG & Co. KG, In den Graswiesen 13, 55437, Ockenheim, Germany
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13
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Hu P, Zou J, Yu J, Shi S. De novo drug design based on Stack-RNN with multi-objective reward-weighted sum and reinforcement learning. J Mol Model 2023; 29:121. [PMID: 36991180 DOI: 10.1007/s00894-023-05523-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/20/2023] [Indexed: 03/31/2023]
Abstract
CONTEXT In recent decades, drug development has become extremely important as different new diseases have emerged. However, drug discovery is a long and complex process with a very low success rate, and methods are needed to improve the efficiency of the process and reduce the possibility of failure. Among them, drug design from scratch has become a promising approach. Molecules are generated from scratch, reducing the reliance on trial and error and prefabricated molecular repositories, but the optimization of its molecular properties is still a challenging multi-objective optimization problem. METHODS In this study, two stack-augmented recurrent neural networks were used to compose a generative model for generating drug-like molecules, and then reinforcement learning was used for optimization to generate molecules with desirable properties, such as binding affinity and the logarithm of the partition coefficient between octanol and water. In addition, a memory storage network was added to increase the internal diversity of the generated molecules. For multi-objective optimization, we proposed a new approach which utilized the magnitude of different attribute reward values to assign different weights to molecular optimization. The proposed model not only solves the problem that the properties of the generated molecules are extremely biased towards a certain attribute due to the possible conflict between the attributes, but also improves various properties of the generated molecules compared with the traditional weighted sum and alternating weighted sum, among which the molecular validity reaches 97.3%, the internal diversity is 0.8613, and the desirable molecules increases from 55.9 to 92%.
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Affiliation(s)
- Pengwei Hu
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China
- Institute of Mathematics and Interdisciplinary Sciences, Nanchang University, Nanchang, 330031, China
| | - Jinping Zou
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China
- Institute of Mathematics and Interdisciplinary Sciences, Nanchang University, Nanchang, 330031, China
| | - Jialin Yu
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China
- Institute of Mathematics and Interdisciplinary Sciences, Nanchang University, Nanchang, 330031, China
| | - Shaoping Shi
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China.
- Institute of Mathematics and Interdisciplinary Sciences, Nanchang University, Nanchang, 330031, China.
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14
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Queiroz LP, Rebello CM, Costa EA, Santana VV, Rodrigues BCL, Rodrigues AE, Ribeiro AM, Nogueira IBR. A Reinforcement Learning Framework to Discover Natural Flavor Molecules. Foods 2023; 12:foods12061147. [PMID: 36981074 PMCID: PMC10048107 DOI: 10.3390/foods12061147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/01/2023] [Accepted: 03/06/2023] [Indexed: 03/11/2023] Open
Abstract
Flavor is the focal point in the flavor industry, which follows social tendencies and behaviors. The research and development of new flavoring agents and molecules are essential in this field. However, the development of natural flavors plays a critical role in modern society. Considering this, the present work proposes a novel framework based on scientific machine learning to undertake an emerging problem in flavor engineering and industry. It proposes a combining system composed of generative and reinforcement learning models. Therefore, this work brings an innovative methodology to design new flavor molecules. The molecules were evaluated regarding synthetic accessibility, the number of atoms, and the likeness to a natural or pseudo-natural product. This work brings as contributions the implementation of a web scraper code to sample a flavors database and the integration of two scientific machine learning techniques in a complex system as a framework. The implementation of the complex system instead of the generative model by itself obtained 10% more molecules within the optimal results. The designed molecules obtained as an output of the reinforcement learning model’s generation were assessed regarding their existence or not in the market and whether they are already used in the flavor industry or not. Thus, we corroborated the potentiality of the framework presented for the search of molecules to be used in the development of flavor-based products.
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Affiliation(s)
- Luana P. Queiroz
- LSRE-LCM—Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- ALiCE—Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Carine M. Rebello
- Chemical Engineering Department, Polytechnic School Federal University of Bahia, Salvador 40210-630, Brazil
| | - Erbet A. Costa
- Chemical Engineering Department, Polytechnic School Federal University of Bahia, Salvador 40210-630, Brazil
| | - Vinícius V. Santana
- LSRE-LCM—Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- ALiCE—Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Bruno C. L. Rodrigues
- LSRE-LCM—Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- ALiCE—Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Alírio E. Rodrigues
- LSRE-LCM—Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- ALiCE—Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Ana M. Ribeiro
- LSRE-LCM—Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- ALiCE—Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Idelfonso B. R. Nogueira
- Chemical Engineering Department, Norwegian University of Science and Technology, Sem Sælandsvei 4, Kjemiblokk 5, N-7491 Trondheim, Norway
- Correspondence:
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15
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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: 25] [Impact Index Per Article: 25.0] [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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16
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Shimizu H, Kodama M, Matsumoto M, Orba Y, Sasaki M, Sato A, Sawa H, Nakayama KI. LIGHTHOUSE illuminates therapeutics for a variety of diseases including COVID-19. iScience 2022; 25:105314. [PMID: 36246574 PMCID: PMC9549714 DOI: 10.1016/j.isci.2022.105314] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 08/08/2022] [Accepted: 10/05/2022] [Indexed: 11/26/2022] Open
Abstract
One of the bottlenecks in the application of basic research findings to patients is the enormous cost, time, and effort required for high-throughput screening of potential drugs for given therapeutic targets. Here we have developed LIGHTHOUSE, a graph-based deep learning approach for discovery of the hidden principles underlying the association of small-molecule compounds with target proteins. Without any 3D structural information for proteins or chemicals, LIGHTHOUSE estimates protein-compound scores that incorporate known evolutionary relations and available experimental data. It identified therapeutics for cancer, lifestyle related disease, and bacterial infection. Moreover, LIGHTHOUSE predicted ethoxzolamide as a therapeutic for coronavirus disease 2019 (COVID-19), and this agent was indeed effective against alpha, beta, gamma, and delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are rampant worldwide. We envision that LIGHTHOUSE will help accelerate drug discovery and fill the gap between bench side and bedside. LIGHTHOUSE discovers therapeutics solely on the basis of the primary sequence The predictions of LIGHTHOUSE against multiple diseases were experimentally correct LIGHTHOUSE facilitates optimization of lead compounds as well
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Affiliation(s)
- Hideyuki Shimizu
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka 812-8582, Japan,Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA,Wyss Institute for Biologically Inspired Engineering, Harvard Medical School, Boston, MA 02115, USA,Department of AI Systems Medicine, M&D Data Science Center, Tokyo Medical and Dental University, Tokyo 113-8510, Japan,Corresponding author
| | - Manabu Kodama
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka 812-8582, Japan
| | - Masaki Matsumoto
- Department of Omics and Systems Biology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
| | - Yasuko Orba
- Division of Molecular Pathobiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo 060-8638, Japan
| | - Michihito Sasaki
- Division of Molecular Pathobiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo 060-8638, Japan
| | - Akihiko Sato
- Division of Molecular Pathobiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo 060-8638, Japan,Drug Discovery and Disease Research Laboratory, Shionogi & Co. Ltd., Osaka 561-0825, Japan
| | - Hirofumi Sawa
- Division of Molecular Pathobiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo 060-8638, Japan,International Collaboration Unit, International Institute for Zoonosis Control, Hokkaido University, Sapporo 060-8638, Japan,One Health Research Center, Hokkaido University, Sapporo 060-8638, Japan,Global Virus Network, Baltimore, MD 21201, USA,Hokkaido University, Institute for Vaccine Research and Development (HU-IVReD)
| | - Keiichi I. Nakayama
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka 812-8582, Japan,Corresponding author
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17
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Wang J, Wang X, Sun H, Wang M, Zeng Y, Jiang D, Wu Z, Liu Z, Liao B, Yao X, Hsieh CY, Cao D, Chen X, Hou T. ChemistGA: A Chemical Synthesizable Accessible Molecular Generation Algorithm for Real-World Drug Discovery. J Med Chem 2022; 65:12482-12496. [PMID: 36065998 DOI: 10.1021/acs.jmedchem.2c01179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Many deep learning (DL)-based molecular generative models have been proposed to design novel molecules. These models may perform well on benchmarks, but they usually do not take real-world constraints into account, such as available training data set, synthetic accessibility, and scaffold diversity in drug discovery. In this study, a new algorithm, ChemistGA, was proposed by combining the traditional heuristic algorithm with DL, in which the crossover of the traditional genetic algorithm (GA) was redefined by DL in conjunction with GA, and an innovative backcrossing operation was implemented to generate desired molecules. Our results clearly show that ChemistGA not only retains the strength of the traditional GA but also greatly enhances the synthetic accessibility and success rate of the generated molecules with desired properties. Calculations on the two benchmarks illustrate that ChemistGA achieves impressive performance among the state-of-the-art baselines, and it opens a new avenue for the application of generative models to real-world drug discovery scenarios.
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Affiliation(s)
- Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.,School of Computer Science, Wuhan University, Wuhan 430072, Hubei, P. R. China.,CarbonSilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, P. R. China
| | - Xiaorui Wang
- CarbonSilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, P. R. China.,State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa 999078, Macau(SAR), P. R. China
| | - Huiyong Sun
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, P. R. China
| | - Mingyang Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.,CarbonSilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, P. R. China
| | - Yundian Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.,CarbonSilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, P. R. China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Zeyi Liu
- DAMTP, Centre for Mathematical Sciences, University of Cambridge, Cambridge CB30WA, U.K
| | - Ben Liao
- Tencent Quantum Laboratory, Tencent, Shenzhen 518057, Guangdong, P. R. China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa 999078, Macau(SAR), P. R. China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.,Tencent Quantum Laboratory, Tencent, Shenzhen 518057, Guangdong, P. R. China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004, Hunan, P. R. China
| | - Xi Chen
- School of Computer Science, Wuhan University, Wuhan 430072, Hubei, P. R. China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
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18
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Tan RK, Liu Y, Xie L. Reinforcement learning for systems pharmacology-oriented and personalized drug design. Expert Opin Drug Discov 2022; 17:849-863. [PMID: 35510835 PMCID: PMC9824901 DOI: 10.1080/17460441.2022.2072288] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
INTRODUCTION Many multi-genic systemic diseases such as neurological disorders, inflammatory diseases, and the majority of cancers do not have effective treatments yet. Reinforcement learning powered systems pharmacology is a potentially effective approach to designing personalized therapies for untreatable complex diseases. AREAS COVERED In this survey, state-of-the-art reinforcement learning methods and their latest applications to drug design are reviewed. The challenges on harnessing reinforcement learning for systems pharmacology and personalized medicine are discussed. Potential solutions to overcome the challenges are proposed. EXPERT OPINION In spite of successful application of advanced reinforcement learning techniques to target-based drug discovery, new reinforcement learning strategies are needed to address systems pharmacology-oriented personalized de novo drug design.
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Affiliation(s)
- Ryan K. Tan
- Department of Computer Science, Hunter College, The City University of New York
| | - Yang Liu
- Department of Computer Science, Hunter College, The City University of New York
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York,Ph.D. Program in Computer Science, Biology & Biochemistry, The Graduate Center, The City University of New York,Helen and Robert Appel Alzheimer’s Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University,Correspondence should be addressed to Lei Xie -
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19
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Tan Z, Li Y, Zhang Z, Wu X, Penfold T, Shi W, Yang S. Efficient Adversarial Generation of Thermally Activated Delayed Fluorescence Molecules. ACS OMEGA 2022; 7:18179-18188. [PMID: 35664624 PMCID: PMC9161419 DOI: 10.1021/acsomega.2c02253] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Adversarial generative models are becoming an essential tool in molecular design and discovery due to their efficiency in exploring the desired chemical space with the assistance of deep learning. In this article, we introduce an integrated framework by combining the modules of algorithmic synthesis, deep prediction, adversarial generation, and fine screening for the purpose of effective design of the thermally activated delayed fluorescence (TADF) molecules that can be used in the organic light-emitting diode devices. The retrosynthetic rules are employed to algorithmically synthesize the D-A complex based on the empirically defined donor and acceptor moieties, which is followed by the high-throughput labeling and prediction with the deep neural network. The new D-A molecules are subsequently generated via the adversarial autoencoder, with the excited-state property distributions perfectly matching those of the original samples. Fine screening of the generated molecules, including the spin-orbital coupling calculation and the excited-state optimization, is eventually implemented to select the qualified TADF candidates within the novel chemical space. Further investigation shows that the created structures fully mimic the original D-A samples by maintaining a significant charge transfer characteristic, a minimal adiabatic singlet-triplet gap, and a moderate spin-orbital coupling that are desirable for the delayed fluorescence.
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Affiliation(s)
- Zheng Tan
- Chengdu
Polytechnic, 83 Tianyi
Street, Chengdu, Sichuan 610000, P. R. China
| | - Yan Li
- Xiyuan
Quantitative Technology, 388 Yizhou Road, Chengdu, Sichuan 610000, P.
R. China
| | - Ziying Zhang
- Guangzhou
Yinfo Information Technology, 2 Ruyi Road, Panyu District, Guangzhou 511431, P. R. China
| | - Xin Wu
- Xiyuan
Quantitative Technology, 388 Yizhou Road, Chengdu, Sichuan 610000, P.
R. China
| | - Thomas Penfold
- Chemistry-School
of Natural and Environmental Sciences, Newcastle
University, Newcastle Upon Tyne NE1 7RU, U.K.
| | - Weimei Shi
- Chengdu
Polytechnic, 83 Tianyi
Street, Chengdu, Sichuan 610000, P. R. China
| | - Shiqing Yang
- Chengdu
Polytechnic, 83 Tianyi
Street, Chengdu, Sichuan 610000, P. R. China
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20
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Guo J, Janet JP, Bauer MR, Nittinger E, Giblin KA, Papadopoulos K, Voronov A, Patronov A, Engkvist O, Margreitter C. DockStream: a docking wrapper to enhance de novo molecular design. J Cheminform 2021; 13:89. [PMID: 34789335 PMCID: PMC8596819 DOI: 10.1186/s13321-021-00563-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 10/29/2021] [Indexed: 01/09/2023] Open
Abstract
Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at https://github.com/MolecularAI/DockStream .
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Affiliation(s)
- Jeff Guo
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Jon Paul Janet
- Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Matthias R Bauer
- Structure & Biophysics, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Eva Nittinger
- Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Kathryn A Giblin
- Medicinal Chemistry, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | | | - Alexey Voronov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Atanas Patronov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden
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21
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Leguy J, Glavatskikh M, Cauchy T, Da Mota B. Scalable estimator of the diversity for de novo molecular generation resulting in a more robust QM dataset (OD9) and a more efficient molecular optimization. J Cheminform 2021; 13:76. [PMID: 34600576 PMCID: PMC8487551 DOI: 10.1186/s13321-021-00554-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 09/15/2021] [Indexed: 01/21/2023] Open
Abstract
Chemical diversity is one of the key term when dealing with machine learning and molecular generation. This is particularly true for quantum chemical datasets. The composition of which should be done meticulously since the calculation is highly time demanding. Previously we have seen that the most known quantum chemical dataset QM9 lacks chemical diversity. As a consequence, ML models trained on QM9 showed generalizability shortcomings. In this paper we would like to present (i) a fast and generic method to evaluate chemical diversity, (ii) a new quantum chemical dataset of 435k molecules, OD9, that includes QM9 and new molecules generated with a diversity objective, (iii) an analysis of the diversity impact on unconstrained and goal-directed molecular generation on the example of QED optimization. Our innovative approach makes it possible to individually estimate the impact of a solution to the diversity of a set, allowing for effective incremental evaluation. In the first application, we will see how the diversity constraint allows us to generate more than a million of molecules that would efficiently complete the reference datasets. The compounds were calculated with DFT thanks to a collaborative effort through the QuChemPedIA@home BOINC project. With regard to goal-directed molecular generation, getting a high QED score is not complicated, but adding a little diversity can cut the number of calls to the evaluation function by a factor of ten.
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Affiliation(s)
- Jules Leguy
- Univ Angers, LERIA, SFR MATHSTIC, 49000, Angers, France
| | - Marta Glavatskikh
- Univ Angers, LERIA, SFR MATHSTIC, 49000, Angers, France.,Univ Angers, CNRS, MOLTECH-ANJOU, SFR MATRIX, 49000, Angers, France
| | - Thomas Cauchy
- Univ Angers, CNRS, MOLTECH-ANJOU, SFR MATRIX, 49000, Angers, France.
| | - Benoit Da Mota
- Univ Angers, LERIA, SFR MATHSTIC, 49000, Angers, France.
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