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Thomas M, Ahmad M, Tresadern G, de Fabritiis G. PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models. J Cheminform 2024; 16:77. [PMID: 38965600 PMCID: PMC11225391 DOI: 10.1186/s13321-024-00866-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 06/04/2024] [Indexed: 07/06/2024] Open
Abstract
SMILES-based generative models are amongst the most robust and successful recent methods used to augment drug design. They are typically used for complete de novo generation, however, scaffold decoration and fragment linking applications are sometimes desirable which requires a different grammar, architecture, training dataset and therefore, re-training of a new model. In this work, we describe a simple procedure to conduct constrained molecule generation with a SMILES-based generative model to extend applicability to scaffold decoration and fragment linking by providing SMILES prompts, without the need for re-training. In combination with reinforcement learning, we show that pre-trained, decoder-only models adapt to these applications quickly and can further optimize molecule generation towards a specified objective. We compare the performance of this approach to a variety of orthogonal approaches and show that performance is comparable or better. For convenience, we provide an easy-to-use python package to facilitate model sampling which can be found on GitHub and the Python Package Index.Scientific contributionThis novel method extends an autoregressive chemical language model to scaffold decoration and fragment linking scenarios. This doesn't require re-training, the use of a bespoke grammar, or curation of a custom dataset, as commonly required by other approaches.
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Affiliation(s)
- Morgan Thomas
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aguiader 88, 08003, Barcelona, Spain.
| | - Mazen Ahmad
- In Silico Discovery, Janssen Pharmaceutica N. V., Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Gary Tresadern
- In Silico Discovery, Janssen Pharmaceutica N. V., Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Gianni de Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aguiader 88, 08003, Barcelona, Spain.
- Acellera Labs, C Dr. Trueta 183, 08005, Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010, Barcelona, Spain.
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Wang L, Zhou Z, Yang X, Shi S, Zeng X, Cao D. The present state and challenges of active learning in drug discovery. Drug Discov Today 2024; 29:103985. [PMID: 38642700 DOI: 10.1016/j.drudis.2024.103985] [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: 03/03/2024] [Revised: 04/08/2024] [Accepted: 04/15/2024] [Indexed: 04/22/2024]
Abstract
Active learning (AL) is an iterative feedback process that efficiently identifies valuable data within vast chemical space, even with limited labeled data. This characteristic renders it a valuable approach to tackle the ongoing challenges faced in drug discovery, such as the ever-expanding explore space and the limitations of labeled data. Consequently, AL is increasingly gaining prominence in the field of drug development. In this paper, we comprehensively review the application of AL at all stages of drug discovery, including compounds-target interaction prediction, virtual screening, molecular generation and optimization, as well as molecular properties prediction. Additionally, we discuss the challenges and prospects associated with the current applications of AL in drug discovery.
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Affiliation(s)
- Lei Wang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China
| | - Zhenran Zhou
- Department of Computer Science, Hunan University, Changsha 410082, Hunan, China
| | - Xixi Yang
- Department of Computer Science, Hunan University, Changsha 410082, Hunan, China
| | - Shaohua Shi
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Xiangxiang Zeng
- Department of Computer Science, Hunan University, Changsha 410082, Hunan, China.
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China.
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3
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Wang S, Liang D, Wang J, Dong K, Zhang Y, Liang H, Xu X, Song T. FraHMT: A Fragment-Oriented Heterogeneous Graph Molecular Generation Model for Target Proteins. J Chem Inf Model 2024; 64:3718-3732. [PMID: 38644797 DOI: 10.1021/acs.jcim.4c00252] [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: 04/23/2024]
Abstract
The molecular generation task stands as a pivotal step in the domains of computational chemistry and drug discovery, aiming to computationally generate molecular structures for specific properties. In contrast to previous models that focused primarily on SMILES strings or molecular graphs, our model placed a special emphasis on the substructure information on molecules, enabling the model to learn richer chemical rules and structure features from fragments and chemical reaction information on molecules. To accomplish this, we fragmented the molecules to construct heterogeneous graph representations based on atom and fragment information. Then our model mapped the heterogeneous graph data into a latent vector space by using an encoder and employed a self-regressive generative model as a decoder for molecular generation. Additionally, we performed transfer learning on the model using a small set of ligand molecules known to be active against the target protein to generate molecules that bind better to the target protein. Experimental results demonstrate that our model is highly competitive with state-of-the-art models. It can generate valid and diverse molecules with favorable physicochemical properties and drug-likeness. Importantly, they produce novel molecules with high docking scores against the target proteins.
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Affiliation(s)
- Shuang Wang
- College of Computer Science and Technology, China University of Petroleum, QingDao 266580, China
| | - Dingming Liang
- College of Computer Science and Technology, China University of Petroleum, QingDao 266580, China
| | - Jianmin Wang
- College of Computer Science and Technology, China University of Petroleum, QingDao 266580, China
- The Interdisciplinary Graduate Program in Integrative Biotechnology, Yonsei University, Incheon 21983, Republic of Korea
| | - Kaiyu Dong
- College of Computer Science and Technology, China University of Petroleum, QingDao 266580, China
| | - Yunjing Zhang
- College of Computer Science and Technology, China University of Petroleum, QingDao 266580, China
| | - Huicong Liang
- Marine Biomedical Institute of Qingdao, School of Medicine and Pharmacy, Ocean University of China, QingDao 266580, China
| | - Ximing Xu
- Marine Biomedical Institute of Qingdao, School of Medicine and Pharmacy, Ocean University of China, QingDao 266580, China
| | - Tao Song
- College of Computer Science and Technology, China University of Petroleum, QingDao 266580, China
- Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Madrid 28031, Spain
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4
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Vogt M. Chemoinformatic approaches for navigating large chemical spaces. Expert Opin Drug Discov 2024; 19:403-414. [PMID: 38300511 DOI: 10.1080/17460441.2024.2313475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 01/30/2024] [Indexed: 02/02/2024]
Abstract
INTRODUCTION Large chemical spaces (CSs) include traditional large compound collections, combinatorial libraries covering billions to trillions of molecules, DNA-encoded chemical libraries comprising complete combinatorial CSs in a single mixture, and virtual CSs explored by generative models. The diverse nature of these types of CSs require different chemoinformatic approaches for navigation. AREAS COVERED An overview of different types of large CSs is provided. Molecular representations and similarity metrics suitable for large CS exploration are discussed. A summary of navigation of CSs in generative models is provided. Methods for characterizing and comparing CSs are discussed. EXPERT OPINION The size of large CSs might restrict navigation to specialized algorithms and limit it to considering neighborhoods of structurally similar molecules. Efficient navigation of large CSs not only requires methods that scale with size but also requires smart approaches that focus on better but not necessarily larger molecule selections. Deep generative models aim to provide such approaches by implicitly learning features relevant for targeted biological properties. It is unclear whether these models can fulfill this ideal as validation is difficult as long as the covered CSs remain mainly virtual without experimental verification.
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Affiliation(s)
- Martin Vogt
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany
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Zhu H, Zhou R, Cao D, Tang J, Li M. A pharmacophore-guided deep learning approach for bioactive molecular generation. Nat Commun 2023; 14:6234. [PMID: 37803000 PMCID: PMC10558534 DOI: 10.1038/s41467-023-41454-9] [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: 06/12/2022] [Accepted: 08/30/2023] [Indexed: 10/08/2023] Open
Abstract
The rational design of novel molecules with the desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. We propose a Pharmacophore-Guided deep learning approach for bioactive Molecule Generation (PGMG). Through the guidance of pharmacophore, PGMG provides a flexible strategy for generating bioactive molecules. PGMG uses a graph neural network to encode spatially distributed chemical features and a transformer decoder to generate molecules. A latent variable is introduced to solve the many-to-many mapping between pharmacophores and molecules to improve the diversity of the generated molecules. Compared to existing methods, PGMG generates molecules with strong docking affinities and high scores of validity, uniqueness, and novelty. In the case studies, we use PGMG in a ligand-based and structure-based drug de novo design. Overall, the flexibility and effectiveness make PGMG a useful tool to accelerate the drug discovery process.
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Affiliation(s)
- Huimin Zhu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Renyi Zhou
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410008, China
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, 00290, Finland
- Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, 00290, Finland
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
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Grisoni F. Chemical language models for de novo drug design: Challenges and opportunities. Curr Opin Struct Biol 2023; 79:102527. [PMID: 36738564 DOI: 10.1016/j.sbi.2023.102527] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/07/2022] [Accepted: 12/20/2022] [Indexed: 02/05/2023]
Abstract
Generative deep learning is accelerating de novo drug design, by allowing the generation of molecules with desired properties on demand. Chemical language models - which generate new molecules in the form of strings using deep learning - have been particularly successful in this endeavour. Thanks to advances in natural language processing methods and interdisciplinary collaborations, chemical language models are expected to become increasingly relevant in drug discovery. This minireview provides an overview of the current state-of-the-art of chemical language models for de novo design, and analyses current limitations, challenges, and advantages. Finally, a perspective on future opportunities is provided.
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Affiliation(s)
- Francesca Grisoni
- Eindhoven University of Technology, Institute for Complex Molecular Systems and Dept. Biomedical Engineering, Eindhoven, Netherlands; Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Netherlands.
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7
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Song S, Tang H, Ran T, Fang F, Tong L, Chen H, Xie H, Lu X. Application of deep generative model for design of Pyrrolo[2,3-d] pyrimidine derivatives as new selective TANK binding kinase 1 (TBK1) inhibitors. Eur J Med Chem 2023; 247:115034. [PMID: 36603506 DOI: 10.1016/j.ejmech.2022.115034] [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: 10/31/2022] [Revised: 12/08/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
The deep conditional transformer neural network SyntaLinker was applied to identify compounds with pyrrolo[2,3-d]pyrimidine scaffold as potent selective TBK1 inhibitor. Further medicinal chemistry optimization campaign led to the discovery of the most potent compound 7l, which exhibited strong enzymatic inhibitory activity against TBK1 with an IC50 value of 22.4 nM 7l had a superior inhibitory activity in human monocytic THP1-Blue cells reporter gene assay than MRT67307. Furthermore, 7l significantly inhibited TBK1 downstream target genes cxcl10 and ifnβ expression in THP1 and RAW264.7 cells induced by poly (I:C) and lipopolysaccharide, respectively. This study suggested that combination of deep conditional transformer neural network SyntaLinker and transfer learning could be a powerful tool for scaffold hopping in drug discovery.
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Affiliation(s)
- Shukai Song
- School of Pharmacy, Jinan University, #855 Xingye Avenue, Guangzhou, 510632, China
| | - Haotian Tang
- Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, #555 Zuchongzhi Road, Shanghai, 201203, China; University of Chinese Academy of Sciences, No.19(A) Yuquan Road, Shijingshan District, Beijing, 100049, China
| | - Ting Ran
- Division of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou, 510530, China
| | - Feng Fang
- Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, #555 Zuchongzhi Road, Shanghai, 201203, China
| | - Linjiang Tong
- Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, #555 Zuchongzhi Road, Shanghai, 201203, China
| | - Hongming Chen
- Division of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou, 510530, China.
| | - Hua Xie
- Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, #555 Zuchongzhi Road, Shanghai, 201203, China; Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Cuiheng New District, Zhongshan City, China.
| | - Xiaoyun Lu
- School of Pharmacy, Jinan University, #855 Xingye Avenue, Guangzhou, 510632, China.
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Xiong F, Xu H, Yu M, Chen X, Zhong Z, Guo Y, Chen M, Ou H, Wu J, Xie A, Xiong J, Xu L, Zhang L, Zhong Q, Huang L, Li Z, Zhang T, Jin F, He X. 3CLpro inhibitors: DEL-based molecular generation. Front Pharmacol 2022; 13:1085665. [PMID: 36569316 PMCID: PMC9768338 DOI: 10.3389/fphar.2022.1085665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 11/23/2022] [Indexed: 12/12/2022] Open
Abstract
Molecular generation (MG) via machine learning (ML) has speeded drug structural optimization, especially for targets with a large amount of reported bioactivity data. However, molecular generation for structural optimization is often powerless for new targets. DNA-encoded library (DEL) can generate systematic, target-specific activity data, including novel targets with few or unknown activity data. Therefore, this study aims to overcome the limitation of molecular generation in the structural optimization for the new target. Firstly, we generated molecules using the structure-affinity data (2.96 million samples) for 3C-like protease (3CLpro) from our own-built DEL platform to get rid of using public databases (e.g., CHEMBL and ZINC). Subsequently, to analyze the effect of transfer learning on the positive rate of the molecule generation model, molecular docking and affinity model based on DEL data were applied to explore the enhanced impact of transfer learning on molecule generation. In addition, the generated molecules are subjected to multiple filtering, including physicochemical properties, drug-like properties, and pharmacophore evaluation, molecular docking to determine the molecules for further study and verified by molecular dynamics simulation.
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Affiliation(s)
- Feng Xiong
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China,*Correspondence: Feng Xiong, ; Feng Jin, ; Xun He,
| | - Honggui Xu
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China
| | - Mingao Yu
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China
| | - Xingyu Chen
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China
| | - Zhenmin Zhong
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Yuhan Guo
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China
| | - Meihong Chen
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Huanfang Ou
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Jiaqi Wu
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Anhua Xie
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Jiaqi Xiong
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Linlin Xu
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Lanmei Zhang
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Qijian Zhong
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Liye Huang
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Zhenwei Li
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | | | - Feng Jin
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China,*Correspondence: Feng Xiong, ; Feng Jin, ; Xun He,
| | - Xun He
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China,*Correspondence: Feng Xiong, ; Feng Jin, ; Xun He,
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Sauer S, Matter H, Hessler G, Grebner C. Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods. Front Chem 2022; 10:1012507. [PMID: 36339033 PMCID: PMC9629386 DOI: 10.3389/fchem.2022.1012507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 09/20/2022] [Indexed: 11/14/2022] Open
Abstract
The identification and optimization of promising lead molecules is essential for drug discovery. Recently, artificial intelligence (AI) based generative methods provided complementary approaches for generating molecules under specific design constraints of relevance in drug design. The goal of our study is to incorporate protein 3D information directly into generative design by flexible docking plus an adapted protein-ligand scoring function, thereby moving towards automated structure-based design. First, the protein-ligand scoring function RFXscore integrating individual scoring terms, ligand descriptors, and combined terms was derived using the PDBbind database and internal data. Next, design results for different workflows are compared to solely ligand-based reward schemes. Our newly proposed, optimal workflow for structure-based generative design is shown to produce promising results, especially for those exploration scenarios, where diverse structures fitting to a protein binding site are requested. Best results are obtained using docking followed by RFXscore, while, depending on the exact application scenario, it was also found useful to combine this approach with other metrics that bias structure generation into “drug-like” chemical space, such as target-activity machine learning models, respectively.
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10
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Thomas M, O'Boyle NM, Bender A, de Graaf C. Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation. J Cheminform 2022; 14:68. [PMID: 36192789 PMCID: PMC9531503 DOI: 10.1186/s13321-022-00646-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 09/23/2022] [Indexed: 11/10/2022] Open
Abstract
A plethora of AI-based techniques now exists to conduct de novo molecule generation that can devise molecules conditioned towards a particular endpoint in the context of drug design. One popular approach is using reinforcement learning to update a recurrent neural network or language-based de novo molecule generator. However, reinforcement learning can be inefficient, sometimes requiring up to 105 molecules to be sampled to optimize more complex objectives, which poses a limitation when using computationally expensive scoring functions like docking or computer-aided synthesis planning models. In this work, we propose a reinforcement learning strategy called Augmented Hill-Climb based on a simple, hypothesis-driven hybrid between REINVENT and Hill-Climb that improves sample-efficiency by addressing the limitations of both currently used strategies. We compare its ability to optimize several docking tasks with REINVENT and benchmark this strategy against other commonly used reinforcement learning strategies including REINFORCE, REINVENT (version 1 and 2), Hill-Climb and best agent reminder. We find that optimization ability is improved ~ 1.5-fold and sample-efficiency is improved ~ 45-fold compared to REINVENT while still delivering appealing chemistry as output. Diversity filters were used, and their parameters were tuned to overcome observed failure modes that take advantage of certain diversity filter configurations. We find that Augmented Hill-Climb outperforms the other reinforcement learning strategies used on six tasks, especially in the early stages of training or for more difficult objectives. Lastly, we show improved performance not only on recurrent neural networks but also on a reinforcement learning stabilized transformer architecture. Overall, we show that Augmented Hill-Climb improves sample-efficiency for language-based de novo molecule generation conditioning via reinforcement learning, compared to the current state-of-the-art. This makes more computationally expensive scoring functions, such as docking, more accessible on a relevant timescale.
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Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Noel M O'Boyle
- Computational Chemistry, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Chris de Graaf
- Computational Chemistry, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
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D'Souza S, Kv P, Balaji S. Training recurrent neural networks as generative neural networks for molecular structures: how does it impact drug discovery? Expert Opin Drug Discov 2022; 17:1071-1079. [PMID: 36216812 DOI: 10.1080/17460441.2023.2134340] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
INTRODUCTION Deep learning approaches have become popular in recent years in de novo drug design. Generative models for molecule generation and optimization have shown promising results. Molecules trained on different chemical data could regenerate molecules that were similar to the query molecule, thus supporting lead optimization. Recurrent neural network-based generative models have demonstrated application in low-data drug discovery, fragment-based drug design and in lead optimization. AREAS COVERED In this review, we have provided an overview of recurrent neural network models and their variants for molecule generation with recent examples. The input representation of molecules as SMILES and molecular graphs have been discussed. The evaluation benchmarks and metrics used in generative neural network models are also highlighted. For this, ScienceDirect, Web of Science, and Google Scholar databases were searched with the article's keywords and their combinations to retrieve the most relevant and up-to-date information. EXPERT OPINION The simplicity of SMILES notation makes it suitable for training a sequence-based model such as a recurrent neural network. However, models that could be trained on molecular graphs to generate molecular structures which could be synthesized could open new possibility for valid molecule generation and synthetic feasibility.
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Affiliation(s)
- Sofia D'Souza
- Department of Computer Science and Engineering, Manipal Institute of Technology, MAHE, Manipal, India
| | - Prema Kv
- Department of Computer Science and Engineering, Manipal Institute of Technology, MAHE, Manipal, India
| | - Seetharaman Balaji
- Department of Computer Science and Engineering, Manipal Institute of Technology, MAHE, Manipal, India
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12
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Kuntz D, Wilson AK. Machine learning, artificial intelligence, and chemistry: how smart algorithms are reshaping simulation and the laboratory. PURE APPL CHEM 2022. [DOI: 10.1515/pac-2022-0202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Machine learning and artificial intelligence are increasingly gaining in prominence through image analysis, language processing, and automation, to name a few applications. Machine learning is also making profound changes in chemistry. From revisiting decades-old analytical techniques for the purpose of creating better calibration curves, to assisting and accelerating traditional in silico simulations, to automating entire scientific workflows, to being used as an approach to deduce underlying physics of unexplained chemical phenomena, machine learning and artificial intelligence are reshaping chemistry, accelerating scientific discovery, and yielding new insights. This review provides an overview of machine learning and artificial intelligence from a chemist’s perspective and focuses on a number of examples of the use of these approaches in computational chemistry and in the laboratory.
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Affiliation(s)
- David Kuntz
- Department of Chemistry , University of North Texas , Denton , TX 76201 , USA
| | - Angela K. Wilson
- Department of Chemistry , Michigan State University , East Lansing , MI 48824 , USA
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13
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Li C, Wang C, Sun M, Zeng Y, Yuan Y, Gou Q, Wang G, Guo Y, Pu X. Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime. J Chem Inf Model 2022; 62:4873-4887. [PMID: 35998331 DOI: 10.1021/acs.jcim.2c00997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Motivated by the challenging of deep learning on the low data regime and the urgent demand for intelligent design on highly energetic materials, we explore a correlated deep learning framework, which consists of three recurrent neural networks (RNNs) correlated by the transfer learning strategy, to efficiently generate new energetic molecules with a high detonation velocity in the case of very limited data available. To avoid the dependence on the external big data set, data augmentation by fragment shuffling of 303 energetic compounds is utilized to produce 500,000 molecules to pretrain RNN, through which the model can learn sufficient structure knowledge. Then the pretrained RNN is fine-tuned by focusing on the 303 energetic compounds to generate 7153 molecules similar to the energetic compounds. In order to more reliably screen the molecules with a high detonation velocity, the SMILE enumeration augmentation coupled with the pretrained knowledge is utilized to build an RNN-based prediction model, through which R2 is boosted from 0.4446 to 0.9572. The comparable performance with the transfer learning strategy based on an existing big database (ChEMBL) to produce the energetic molecules and drug-like ones further supports the effectiveness and generality of our strategy in the low data regime. High-precision quantum mechanics calculations further confirm that 35 new molecules present a higher detonation velocity and lower synthetic accessibility than the classic explosive RDX, along with good thermal stability. In particular, three new molecules are comparable to caged CL-20 in the detonation velocity. All the source codes and the data set are freely available at https://github.com/wangchenghuidream/RNNMGM.
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Affiliation(s)
- Chuan Li
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Chenghui Wang
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Ming Sun
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yan Zeng
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Yuan Yuan
- College of Management, Southwest University for Nationalities, Chengdu 610041, China
| | - Qiaolin Gou
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Guangchuan Wang
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu 610064, China
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14
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Xie W, Wang F, Li Y, Lai L, Pei J. Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models. J Chem Inf Model 2022; 62:2269-2279. [PMID: 35544331 DOI: 10.1021/acs.jcim.2c00042] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
A persistent goal for de novo drug design is to generate novel chemical compounds with desirable properties in a labor-, time-, and cost-efficient manner. Deep generative models provide alternative routes to this goal. Numerous model architectures and optimization strategies have been explored in recent years, most of which have been developed to generate two-dimensional molecular structures. Some generative models aiming at three-dimensional (3D) molecule generation have also been proposed, gaining attention for their unique advantages and potential to directly design drug-like molecules in a target-conditioning manner. This review highlights current developments in 3D molecular generative models combined with deep learning and discusses future directions for de novo drug design.
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Affiliation(s)
- Weixin Xie
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Fanhao Wang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Yibo Li
- Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,Peking-Tsinghua Center for Life Science at BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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15
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Hua Y, Fang X, Xing G, Xu Y, Liang L, Deng C, Dai X, Liu H, Lu T, Zhang Y, Chen Y. Effective Reaction-Based De Novo Strategy for Kinase Targets: A Case Study on MERTK Inhibitors. J Chem Inf Model 2022; 62:1654-1668. [PMID: 35353505 DOI: 10.1021/acs.jcim.2c00068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Reaction-based de novo design is the computational generation of novel molecular structures by linking building blocks using reaction vectors derived from chemistry knowledge. In this work, we first adopted a recurrent neural network (RNN) model to generate three groups of building blocks with different functional groups and then constructed an in silico target-focused combinatorial library based on chemical reaction rules. Mer tyrosine kinase (MERTK) was used as a study case. Combined with a scaffold enrichment analysis, 15 novel MERTK inhibitors covering four scaffolds were achieved. Among them, compound 5a obtained an IC50 value of 53.4 nM against MERTK without any further optimization. The efficiency of hit identification could be significantly improved by shrinking the compound library with the fragment iterative optimization strategy and enriching the dominant scaffold in the hinge region. We hope that this strategy can provide new insights for accelerating the drug discovery process.
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Affiliation(s)
- Yi Hua
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Xiaobao Fang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Guomeng Xing
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yuan Xu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Li Liang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Chenglong Deng
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Xiaowen Dai
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.,State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
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16
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Abstract
Artificial intelligence (AI) tools find increasing application in drug discovery supporting every stage of the Design-Make-Test-Analyse (DMTA) cycle. The main focus of this chapter is the application in molecular generation with the aid of deep neural networks (DNN). We present a historical overview of the main advances in the field. We analyze the concepts of distribution and goal-directed learning and then highlight some of the recent applications of generative models in drug design with a focus into research work from the biopharmaceutical industry. We present in some more detail REINVENT which is an open-source software developed within our group in AstraZeneca and the main platform for AI molecular design support for a number of medicinal chemistry projects in the company and we also demonstrate some of our work in library design. Finally, we present some of the main challenges in the application of AI in Drug Discovery and different approaches to respond to these challenges which define areas for current and future work.
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17
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Abstract
INTRODUCTION The popularity and success of advanced AI methods like deep neural networks has led to novel ways for exploring chemical space. Their opaque nature poses challenges for model evaluation regarding novelty, uniqueness, and distribution of the chemical space covered. However, these methods also promise to be able to explore uncharted chemical space in novel ways that do not rely directly on structural similarity. AREAS COVERED This review provides an overview of popular deep learning methods for chemical space exploration. Crucial aspects like choice of molecular representation, training for focused chemical space exploration, and criteria for assessing and validating chemical space coverage are discussed. EXPERT OPINION Deep learning offers great potential for chemical space exploration beyond conventional fragment-based methods. Given the rarity of prospective applications and considering the difficulty in assessing representativeness and comprehensiveness of chemical space covered, developing criteria for assessing and validating generative models is of great significance. Latent space models like variational autoencoders are conceptually appealing for inverse QSAR/QSPR approaches as neighborhood relationships in latent space can be trained to reflect property similarities. Future research in understanding and interpreting generative models might lead to a better understanding of biologically relevant properties of molecules.
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Affiliation(s)
- Martin Vogt
- Department of Life Science Informatics, B-it, Limes Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich Wilhelms-Universität, Bonn, Germany
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18
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Abstract
Optimal design of polymers is a challenging task due to their enormous chemical and configurational space. Recent advances in computations, machine learning, and increasing trends in data and software availability can potentially address this problem and accelerate the molecular-scale design of polymers. Here, the central problem of polymer design is reviewed, and the general ideas of data-driven methods and their working principles in the context of polymer design are discussed. This Review provides a historical perspective and a summary of current trends and outlines future scopes of data-driven methods for polymer research. A few representative case studies on the use of such data-driven methods for discovering new polymers with exceptional properties are presented. Moreover, attempts are made to highlight how data-driven strategies aid in establishing new correlations and advancing the fundamental understanding of polymers. This Review posits that the combination of machine learning, rapid computational characterization of polymers, and availability of large open-sourced homogeneous data will transform polymer research and development over the coming decades. It is hoped that this Review will serve as a useful reference to researchers who wish to develop and deploy data-driven methods for polymer research and education.
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19
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Kim H, Na J, Lee WB. Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention. J Chem Inf Model 2021; 61:5804-5814. [PMID: 34855384 DOI: 10.1021/acs.jcim.1c01289] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Discovering new materials better suited to specific purposes is an important issue in improving the quality of human life. Here, a neural network that creates molecules that meet some desired multiple target conditions based on a deep understanding of chemical language is proposed (generative chemical Transformer, GCT). The attention mechanism in GCT allows a deeper understanding of molecular structures beyond the limitations of chemical language itself which cause semantic discontinuity by paying attention to characters sparsely. The significance of language models for inverse molecular design problems is investigated by quantitatively evaluating the quality of the generated molecules. GCT generates highly realistic chemical strings that satisfy both chemical and linguistic grammar rules. Molecules parsed from the generated strings simultaneously satisfy the multiple target properties and vary for a single condition set. These advances will contribute to improving the quality of human life by accelerating the process of desired material discovery.
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Affiliation(s)
- Hyunseung Kim
- School of Chemical and Biological Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jonggeol Na
- Department of Chemical Engineering and Materials Science, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Won Bo Lee
- School of Chemical and Biological Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
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20
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Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry. Future Med Chem 2021; 14:245-270. [PMID: 34939433 DOI: 10.4155/fmc-2021-0243] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.
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21
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Grebner C, Matter H, Hessler G. Artificial Intelligence in Compound Design. Methods Mol Biol 2021; 2390:349-382. [PMID: 34731477 DOI: 10.1007/978-1-0716-1787-8_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced by different research groups. These artificial intelligence-based design methods can be applied for suggesting novel chemical motifs in lead generation or scaffold hopping as well as for optimization of desired property profiles during lead optimization. In lead generation, broad sampling of the chemical space for identification of novel motifs is required, while in the lead optimization phase, a detailed exploration of the chemical neighborhood of a current lead series is advantageous. These different requirements for successful design outcomes render different combinations of artificial intelligence technologies useful. Overall, we observe that a combination of different approaches with tailored scoring and evaluation schemes appears beneficial for efficient artificial intelligence-based compound design.
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Affiliation(s)
- Christoph Grebner
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany
| | - Hans Matter
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany
| | - Gerhard Hessler
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany.
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22
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De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach. Pharmaceuticals (Basel) 2021; 14:ph14121249. [PMID: 34959651 PMCID: PMC8706867 DOI: 10.3390/ph14121249] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 11/21/2021] [Accepted: 11/24/2021] [Indexed: 12/31/2022] Open
Abstract
Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an urgent need to develop novel caspase-6 candidate inhibitors. Herein, a gated recurrent unit (GRU)-based recurrent neural network (RNN) combined with transfer learning was used to build a molecular generative model of caspase-6 inhibitors. The results showed that the GRU-based RNN model can accurately learn the SMILES grammars of about 2.4 million chemical molecules including ionic and isomeric compounds and can generate potential caspase-6 inhibitors after transfer learning of the known 433 caspase-6 inhibitors. Based on the novel molecules derived from the molecular generative model, an optimal logistic regression model and Surflex-dock were employed for predicting and ranking the inhibitory activities. According to the prediction results, three potential caspase-6 inhibitors with different scaffolds were selected as the promising candidates for further research. In general, this paper provides an efficient combinational strategy for de novo molecular design of caspase-6 inhibitors.
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23
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Thomas M, Boardman A, Garcia-Ortegon M, Yang H, de Graaf C, Bender A. Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2390:1-59. [PMID: 34731463 DOI: 10.1007/978-1-0716-1787-8_1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Artificial intelligence (AI) has undergone rapid development in recent years and has been successfully applied to real-world problems such as drug design. In this chapter, we review recent applications of AI to problems in drug design including virtual screening, computer-aided synthesis planning, and de novo molecule generation, with a focus on the limitations of the application of AI therein and opportunities for improvement. Furthermore, we discuss the broader challenges imposed by AI in translating theoretical practice to real-world drug design; including quantifying prediction uncertainty and explaining model behavior.
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Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Andrew Boardman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Miguel Garcia-Ortegon
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.,Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK
| | - Hongbin Yang
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | | | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.
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24
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Hu L, Yang Y, Zheng S, Xu J, Ran T, Chen H. Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches. J Chem Inf Model 2021; 61:4900-4912. [PMID: 34586824 DOI: 10.1021/acs.jcim.1c00608] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The protein kinase family contains many promising drug targets. Many kinase inhibitors target the ATP-binding pocket, leading to approved drugs in past decades. Scaffold hopping is an effective approach for drug design. The kinase ATP-binding pocket is highly conserved, crossing the whole kinase family. This provides an opportunity to develop a scaffold hopping approach to explore diversified scaffolds among various kinase inhibitors. In this work, we report the SyntaLinker-Hybrid scheme for kinase inhibitor scaffold hopping. With this scheme, we replace molecular fragments bound at the conserved kinase hinge region with deep generative models. Thus, we are able to generate new kinase-inhibitor-like structures hybridizing the privileged fragments against the hinge region. We demonstrate that this scheme allows generation of kinase-inhibitor-like molecules with novel scaffolds, while retaining the binding features of existing kinase inhibitors. This work can be employed in lead identification against kinase targets.
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Affiliation(s)
- Lizhao Hu
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, China.,Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Yuyao Yang
- Center of Cell Lineage and Atlas, Bioland Laboratory (Guangzhou Regenerative Medicine and Health-Guangdong Laboratory), Guangzhou 510530, China.,Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Shuangjia Zheng
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China
| | - Jun Xu
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, China.,Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Ting Ran
- Center of Cell Lineage and Atlas, Bioland Laboratory (Guangzhou Regenerative Medicine and Health-Guangdong Laboratory), Guangzhou 510530, China
| | - Hongming Chen
- Center of Cell Lineage and Atlas, Bioland Laboratory (Guangzhou Regenerative Medicine and Health-Guangdong Laboratory), Guangzhou 510530, China
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25
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Peng SP, Yang XY, Zhao Y. Molecular Conditional Generation and Property Analysis of Non-Fullerene Acceptors with Deep Learning. Int J Mol Sci 2021; 22:9099. [PMID: 34445805 PMCID: PMC8396663 DOI: 10.3390/ijms22169099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/19/2021] [Accepted: 08/20/2021] [Indexed: 11/29/2022] Open
Abstract
The proposition of non-fullerene acceptors (NFAs) in organic solar cells has made great progress in the raise of power conversion efficiency, and it also broadens the ways for searching and designing new acceptor molecules. In this work, the design of novel NFAs with required properties is performed with the conditional generative model constructed from a convolutional neural network (CNN). The temporal CNN is firstly trained to be a good string-based molecular conditional generative model to directly generate the desired molecules. The reliability of generated molecular properties is then demonstrated by a graph-based prediction model and evaluated with quantum chemical calculations. Specifically, the global attention mechanism is incorporated in the prediction model to pool the extracted information of molecular structures and provide interpretability. By combining the generative and prediction models, thousands of NFAs with required frontier molecular orbital energies are generated. The generated new molecules essentially explore the chemical space and enrich the database of transformation rules for molecular design. The conditional generation model can also be trained to generate the molecules from molecular fragments, and the contribution of molecular fragments to the properties is subsequently predicted by the prediction model.
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Affiliation(s)
| | | | - Yi Zhao
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Fujian Provincial Key Lab of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China; (S.-P.P.); (X.-Y.Y.)
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26
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27
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Predicting Polymers' Glass Transition Temperature by a Chemical Language Processing Model. Polymers (Basel) 2021; 13:polym13111898. [PMID: 34200505 PMCID: PMC8201381 DOI: 10.3390/polym13111898] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/03/2021] [Accepted: 06/04/2021] [Indexed: 12/14/2022] Open
Abstract
We propose a chemical language processing model to predict polymers' glass transition temperature (Tg) through a polymer language (SMILES, Simplified Molecular Input Line Entry System) embedding and recurrent neural network. This model only receives the SMILES strings of a polymer's repeat units as inputs and considers the SMILES strings as sequential data at the character level. Using this method, there is no need to calculate any additional molecular descriptors or fingerprints of polymers, and thereby, being very computationally efficient. More importantly, it avoids the difficulties to generate molecular descriptors for repeat units containing polymerization point '*'. Results show that the trained model demonstrates reasonable prediction performance on unseen polymer's Tg. Besides, this model is further applied for high-throughput screening on an unlabeled polymer database to identify high-temperature polymers that are desired for applications in extreme environments. Our work demonstrates that the SMILES strings of polymer repeat units can be used as an effective feature representation to develop a chemical language processing model for predictions of polymer Tg. The framework of this model is general and can be used to construct structure-property relationships for other polymer properties.
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28
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Thomas M, Smith RT, O'Boyle NM, de Graaf C, Bender A. Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study. J Cheminform 2021; 13:39. [PMID: 33985583 PMCID: PMC8117600 DOI: 10.1186/s13321-021-00516-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/02/2021] [Indexed: 12/14/2022] Open
Abstract
Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it.
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Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Robert T Smith
- Computational Chemistry, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
| | - Noel M O'Boyle
- Computational Chemistry, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
| | - Chris de Graaf
- Computational Chemistry, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK.
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
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29
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Nazarova AL, Yang L, Liu K, Mishra A, Kalia RK, Nomura KI, Nakano A, Vashishta P, Rajak P. Dielectric Polymer Property Prediction Using Recurrent Neural Networks with Optimizations. J Chem Inf Model 2021; 61:2175-2186. [PMID: 33871989 DOI: 10.1021/acs.jcim.0c01366] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Despite the growing success of machine learning for predicting structure-property relationships in molecules and materials, such as predicting the dielectric properties of polymers, it is still in its infancy. We report on the effectiveness of solving structure-property relationships for a computer-generated database of dielectric polymers using recurrent neural network (RNN) models. The implementation of a series of optimization strategies was crucial to achieving high learning speeds and sufficient accuracy: (1) binary and nonbinary representations of SMILES (Simplified Molecular Input Line System) fingerprints and (2) backpropagation with affine transformation of the input sequence (ATransformedBP) and resilient backpropagation with initial weight update parameter optimizations (iRPROP- optimized). For the investigated database of polymers, the binary SMILES representation was found to be superior to the decimal representation with respect to the training and prediction performance. All developed and optimized Elman-type RNN algorithms outperformed nonoptimized RNN models in the efficient prediction of nonlinear structure-activity relationships. The average relative standard deviation (RSD) remained well below 5%, and the maximum RSD did not exceed 30%. Moreover, we provide a C++ codebase as a testbed for a new generation of open programming languages that target increasingly diverse computer architectures.
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Affiliation(s)
- Antonina L Nazarova
- Department of Chemistry, Loker Hydrocarbon Research Institute, and USC Bridge Institue, University of Southern California, Los Angeles, California 90089, United States
| | - Liqiu Yang
- Collaboratory of Advanced Computing and Simulations, Department of Computer Science, Department of Physics & Astronomy, Department of Chemical Engineering & Materials Science, and Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, United States
| | - Kuang Liu
- Collaboratory of Advanced Computing and Simulations, Department of Computer Science, Department of Physics & Astronomy, Department of Chemical Engineering & Materials Science, and Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, United States
| | - Ankit Mishra
- Collaboratory of Advanced Computing and Simulations, Department of Computer Science, Department of Physics & Astronomy, Department of Chemical Engineering & Materials Science, and Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, United States
| | - Rajiv K Kalia
- Collaboratory of Advanced Computing and Simulations, Department of Computer Science, Department of Physics & Astronomy, Department of Chemical Engineering & Materials Science, and Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, United States
| | - Ken-Ichi Nomura
- Collaboratory of Advanced Computing and Simulations, Department of Computer Science, Department of Physics & Astronomy, Department of Chemical Engineering & Materials Science, and Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, United States
| | - Aiichiro Nakano
- Collaboratory of Advanced Computing and Simulations, Department of Computer Science, Department of Physics & Astronomy, Department of Chemical Engineering & Materials Science, and Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, United States
| | - Priya Vashishta
- Collaboratory of Advanced Computing and Simulations, Department of Computer Science, Department of Physics & Astronomy, Department of Chemical Engineering & Materials Science, and Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, United States
| | - Pankaj Rajak
- Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, Illinois 60439, United States
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30
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Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1513] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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31
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Affiliation(s)
- Kenneth M Merz
- Department of Chemistry, Michigan State University, Michigan, East Lansing 48824, United States
| | - Gianni De Fabritiis
- Computational Science Laboratory, Barcelona Biomedical Research Park (PRBB), Universitat Pompeu Fabra, C Dr Aiguader 88, 08003 Barcelona, Spain
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, Michigan, East Lansing 48824, United States
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32
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Kim M, Park K, Kim W, Jung S, Cho AE. Target-Specific Drug Design Method Combining Deep Learning and Water Pharmacophore. J Chem Inf Model 2020; 61:36-45. [PMID: 33252229 DOI: 10.1021/acs.jcim.0c00757] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Following identification of a target protein, hit identification, which finds small organic molecules that bind to the target, is an important first step of a structure-based drug design project. In this study, we demonstrate a target-specific drug design method that can autonomously generate a series of target-favorable compounds. This method utilizes the seq2seq model based on a deep learning algorithm and a water pharmacophore. Water pharmacophore models are used to screen compounds that are favorable to a given target in a large compound database, and seq2seq compound generators are used to train the screened compounds and generate entirely new compounds based on the training model. Our method was tested through binding energy calculation studies of six pharmaceutically relevant targets in the directory of useful decoys (DUD) set with docking. The compounds generated by our method had lower average binding energies than decoy compounds in five out of six cases and included a number of compounds that had lower binding energies than the average binding energies of the active compounds in four cases. The generated compound lists for these four cases featured compounds with lower binding energies than even the most active compounds.
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Affiliation(s)
- Minsup Kim
- inCerebro Co., Ltd. Drug Discovery Institute, Seoul Technopark, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea
| | - Kichul Park
- inCerebro Co., Ltd. Drug Discovery Institute, Seoul Technopark, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea.,Department of Bioinformatics, Korea University, Sejong 30019, Korea
| | - Wonsang Kim
- inCerebro Co., Ltd. Drug Discovery Institute, Seoul Technopark, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea
| | - Sangwon Jung
- SK Biopharmaceuticals Co., Ltd., 221 Pangyoyeok-ro, Seongnam-si, Gyeonggi-do 13494, Korea
| | - Art E Cho
- inCerebro Co., Ltd. Drug Discovery Institute, Seoul Technopark, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea.,Department of Bioinformatics, Korea University, Sejong 30019, Korea
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33
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Vázquez J, López M, Gibert E, Herrero E, Luque FJ. Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches. Molecules 2020; 25:E4723. [PMID: 33076254 PMCID: PMC7587536 DOI: 10.3390/molecules25204723] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/06/2020] [Accepted: 10/11/2020] [Indexed: 12/20/2022] Open
Abstract
Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.
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Affiliation(s)
- Javier Vázquez
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
| | - Manel López
- AB Science, Parc Scientifique de Luminy, Zone Luminy Enterprise, Case 922, 163 Av. de Luminy, 13288 Marseille, France;
| | - Enric Gibert
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - Enric Herrero
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - F. Javier Luque
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
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34
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Bush JT, Pogany P, Pickett SD, Barker M, Baxter A, Campos S, Cooper AWJ, Hirst D, Inglis G, Nadin A, Patel VK, Poole D, Pritchard J, Washio Y, White G, Green DVS. A Turing Test for Molecular Generators. J Med Chem 2020; 63:11964-11971. [PMID: 32955254 DOI: 10.1021/acs.jmedchem.0c01148] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Machine learning approaches promise to accelerate and improve success rates in medicinal chemistry programs by more effectively leveraging available data to guide a molecular design. A key step of an automated computational design algorithm is molecule generation, where the machine is required to design high-quality, drug-like molecules within the appropriate chemical space. Many algorithms have been proposed for molecular generation; however, a challenge is how to assess the validity of the resulting molecules. Here, we report three Turing-inspired tests designed to evaluate the performance of molecular generators. Profound differences were observed between the performance of molecule generators in these tests, highlighting the importance of selection of the appropriate design algorithms for specific circumstances. One molecule generator, based on match molecular pairs, performed excellently against all tests and thus provides a valuable component for machine-driven medicinal chemistry design workflows.
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Affiliation(s)
- Jacob T Bush
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Peter Pogany
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Stephen D Pickett
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Mike Barker
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Andrew Baxter
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Sebastien Campos
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Anthony W J Cooper
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - David Hirst
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Graham Inglis
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Alan Nadin
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Vipulkumar K Patel
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Darren Poole
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - John Pritchard
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Yoshiaki Washio
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Gemma White
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Darren V S Green
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
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