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Eguida M, Bret G, Sindt F, Li F, Chau I, Ackloo S, Arrowsmith C, Bolotokova A, Ghiabi P, Gibson E, Halabelian L, Houliston S, Harding RJ, Hutchinson A, Loppnau P, Perveen S, Seitova A, Zeng H, Schapira M, Rognan D. Subpocket Similarity-Based Hit Identification for Challenging Targets: Application to the WDR Domain of LRRK2. J Chem Inf Model 2024; 64:5344-5355. [PMID: 38916159 DOI: 10.1021/acs.jcim.4c00601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
We herewith applied a priori a generic hit identification method (POEM) for difficult targets of known three-dimensional structure, relying on the simple knowledge of physicochemical and topological properties of a user-selected cavity. Searching for local similarity to a set of fragment-bound protein microenvironments of known structure, a point cloud registration algorithm is first applied to align known subpockets to the target cavity. The resulting alignment then permits us to directly pose the corresponding seed fragments in a target cavity space not typically amenable to classical docking approaches. Last, linking potentially connectable atoms by a deep generative linker enables full ligand enumeration. When applied to the WD40 repeat (WDR) central cavity of leucine-rich repeat kinase 2 (LRRK2), an unprecedented binding site, POEM was able to quickly propose 94 potential hits, five of which were subsequently confirmed to bind in vitro to LRRK2-WDR.
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Affiliation(s)
- Merveille Eguida
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, Strasbourg, France
| | - Guillaume Bret
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, Strasbourg, France
| | - François Sindt
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, Strasbourg, France
| | - Fengling Li
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Irene Chau
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Suzanne Ackloo
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Cheryl Arrowsmith
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 1L7, Canada
| | - Albina Bolotokova
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Pegah Ghiabi
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Elisa Gibson
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Levon Halabelian
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 1L7, Canada
| | - Scott Houliston
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 1L7, Canada
| | - Rachel J Harding
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Ashley Hutchinson
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Peter Loppnau
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Sumera Perveen
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Almagul Seitova
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Hong Zeng
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Matthieu Schapira
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Didier Rognan
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, Strasbourg, France
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Guo J, Schwaller P. Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning. JACS AU 2024; 4:2160-2172. [PMID: 38938817 PMCID: PMC11200228 DOI: 10.1021/jacsau.4c00066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 06/29/2024]
Abstract
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparent when using oracles that can provide increased predictive accuracy but impose significant computational cost. Consequently, designing molecules that are optimized for such oracles cannot be achieved under a practical computational budget. Molecular generative models based on simplified molecular-input line-entry system (SMILES) have shown remarkable sample efficiency when coupled with reinforcement learning, as demonstrated in the practical molecular optimization (PMO) benchmark. Here, we first show that experience replay drastically improves the performance of multiple previously proposed algorithms. Next, we propose a novel algorithm called Augmented Memory that combines data augmentation with experience replay. We show that scores obtained from oracle calls can be reused to update the model multiple times. We compare Augmented Memory to previously proposed algorithms and show significantly enhanced sample efficiency in an exploitation task, a drug discovery case study requiring both exploration and exploitation, and a materials design case study optimizing explicitly for quantum-mechanical properties. Our method achieves a new state-of-the-art in sample-efficient de novo molecular design, outperforming all of the previously reported methods. The code is available at https://github.com/schwallergroup/augmented_memory.
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Affiliation(s)
- Jeff Guo
- Laboratory
of Artificial Chemical Intelligence (LIAC), Institut des Sciences
et Ingénierie Chimiques, Ecole Polytechnique
Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
- National
Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Philippe Schwaller
- Laboratory
of Artificial Chemical Intelligence (LIAC), Institut des Sciences
et Ingénierie Chimiques, Ecole Polytechnique
Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
- National
Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
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Woodhead AJ, Erlanson DA, de Esch IJP, Holvey RS, Jahnke W, Pathuri P. Fragment-to-Lead Medicinal Chemistry Publications in 2022. J Med Chem 2024; 67:2287-2304. [PMID: 38289623 DOI: 10.1021/acs.jmedchem.3c02070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
Abstract
This Perspective is the eighth in an annual series that summarizes successful fragment-to-lead (F2L) case studies published each year. A tabulated summary of relevant articles published in 2022 is provided, and features such as target class, screening methods, and ligand efficiency are discussed both for the 2022 examples and for the combined examples over the years 2015-2022. In addition, trends and new developments in the field are summarized. In 2022, 18 publications described successful fragment-to-lead studies, including the development of three clinical compounds (MTRX1719, MK-8189, and BI-823911).
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Affiliation(s)
- Andrew J Woodhead
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - Daniel A Erlanson
- Frontier Medicines, 151 Oyster Point Blvd., South San Francisco, California 94080, United States
| | - Iwan J P de Esch
- Division of Medicinal Chemistry, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Rhian S Holvey
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - Wolfgang Jahnke
- Novartis Biomedical Research, Discovery Sciences, 4002 Basel, Switzerland
| | - Puja Pathuri
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
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Zhang H, Huang J, Xie J, Huang W, Yang Y, Xu M, Lei J, Chen H. GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning. J Chem Inf Model 2024; 64:666-676. [PMID: 38241022 DOI: 10.1021/acs.jcim.3c01700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2024]
Abstract
Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery.
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Affiliation(s)
- Hao Zhang
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou 510006, China
| | - Jinchao Huang
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou 510006, China
| | - Junjie Xie
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Weifeng Huang
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou 510006, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Mingyuan Xu
- Guangzhou National Laboratory, Guangzhou International Bio Island, No. 9 Xin Dao Huan Bei Road, Guangzhou 510005, China
| | - Jinping Lei
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou 510006, China
| | - Hongming Chen
- Guangzhou National Laboratory, Guangzhou International Bio Island, No. 9 Xin Dao Huan Bei Road, Guangzhou 510005, China
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Janet JP, Mervin L, Engkvist O. Artificial intelligence in molecular de novo design: Integration with experiment. Curr Opin Struct Biol 2023; 80:102575. [PMID: 36966692 DOI: 10.1016/j.sbi.2023.102575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/09/2023] [Accepted: 02/18/2023] [Indexed: 06/04/2023]
Abstract
In this mini review, we capture the latest progress of applying artificial intelligence (AI) techniques based on deep learning architectures to molecular de novo design with a focus on integration with experimental validation. We will cover the progress and experimental validation of novel generative algorithms, the validation of QSAR models and how AI-based molecular de novo design is starting to become connected with chemistry automation. While progress has been made in the last few years, it is still early days. The experimental validations conducted thus far should be considered proof-of-principle, providing confidence that the field is moving in the right direction.
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Affiliation(s)
- Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Lewis Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
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Wang L, Song Y, Wang H, Zhang X, Wang M, He J, Li S, Zhang L, Li K, Cao L. Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade. Pharmaceuticals (Basel) 2023; 16:253. [PMID: 37259400 PMCID: PMC9963982 DOI: 10.3390/ph16020253] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 10/13/2023] Open
Abstract
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Kang Li
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
| | - Lei Cao
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
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Eguida M, Rognan D. Estimating the Similarity between Protein Pockets. Int J Mol Sci 2022; 23:12462. [PMID: 36293316 PMCID: PMC9604425 DOI: 10.3390/ijms232012462] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/15/2022] [Accepted: 10/16/2022] [Indexed: 10/28/2023] Open
Abstract
With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.
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Affiliation(s)
| | - Didier Rognan
- Laboratoire d’Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
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