1
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Gangwal A, Lavecchia A. Unleashing the power of generative AI in drug discovery. Drug Discov Today 2024; 29:103992. [PMID: 38663579 DOI: 10.1016/j.drudis.2024.103992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/22/2024] [Accepted: 04/18/2024] [Indexed: 05/04/2024]
Abstract
Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy, Dhule 424001, Maharashtra, India
| | - Antonio Lavecchia
- "Drug Discovery" Laboratory, Department of Pharmacy, University of Naples Federico II, I-80131 Naples, Italy.
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2
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Xu M, Chen H. Tree-Invent: A Novel Multipurpose Molecular Generative Model Constrained with a Topological Tree. J Chem Inf Model 2023; 63:7067-7082. [PMID: 37962855 DOI: 10.1021/acs.jcim.3c01626] [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: 11/15/2023]
Abstract
De novo molecular design plays an important role in drug discovery. Here, a novel generative model, Tree-Invent, was proposed to integrate topological constraints in the generation of a molecular graph. In this model, a molecular graph is represented as a topological tree in which a ring system, a nonring atom, and a chemical bond are regarded as the ring node, single node, and edge, respectively. The molecule generation is driven by three independent submodels for carrying out operations of node addition, ring generation, and node connection. One unique feature of the generative model is that the topological tree structure can be specified as a constraint for structure generation, which provides more precise control of structure generation. Combined with reinforcement learning, the Tree-Invent model could efficiently explore targeted chemical space. Moreover, the Tree-Invent model is flexible enough to be used in versatile molecule design settings such as scaffold decoration, scaffold hopping, and linker generation.
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Affiliation(s)
- Mingyuan Xu
- Guangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou, Guangdong 510005, China
| | - Hongming Chen
- Guangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou, Guangdong 510005, China
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3
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Hu C, Li S, Yang C, Chen J, Xiong Y, Fan G, Liu H, Hong L. ScaffoldGVAE: scaffold generation and hopping of drug molecules via a variational autoencoder based on multi-view graph neural networks. J Cheminform 2023; 15:91. [PMID: 37794460 PMCID: PMC10548653 DOI: 10.1186/s13321-023-00766-0] [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: 08/11/2023] [Accepted: 09/25/2023] [Indexed: 10/06/2023] Open
Abstract
In recent years, drug design has been revolutionized by the application of deep learning techniques, and molecule generation is a crucial aspect of this transformation. However, most of the current deep learning approaches do not explicitly consider and apply scaffold hopping strategy when performing molecular generation. In this work, we propose ScaffoldGVAE, a variational autoencoder based on multi-view graph neural networks, for scaffold generation and scaffold hopping of drug molecules. The model integrates several important components, such as node-central and edge-central message passing, side-chain embedding, and Gaussian mixture distribution of scaffolds. To assess the efficacy of our model, we conduct a comprehensive evaluation and comparison with baseline models based on seven general generative model evaluation metrics and four scaffold hopping generative model evaluation metrics. The results demonstrate that ScaffoldGVAE can explore the unseen chemical space and generate novel molecules distinct from known compounds. Especially, the scaffold hopped molecules generated by our model are validated by the evaluation of GraphDTA, LeDock, and MM/GBSA. The case study of generating inhibitors of LRRK2 for the treatment of PD further demonstrates the effectiveness of ScaffoldGVAE in generating novel compounds through scaffold hopping. This novel approach can also be applied to other protein targets of various diseases, thereby contributing to the future development of new drugs. Source codes and data are available at https://github.com/ecust-hc/ScaffoldGVAE .
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Affiliation(s)
- Chao Hu
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Song Li
- School of Physics and Astronomy and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China
| | - Chenxing Yang
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China
| | - Jun Chen
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China
| | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
- Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, 201203, China
| | - Guisheng Fan
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Hao Liu
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China.
| | - Liang Hong
- School of Physics and Astronomy and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, 201203, China.
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4
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Ivanenkov Y, Zagribelnyy B, Malyshev A, Evteev S, Terentiev V, Kamya P, Bezrukov D, Aliper A, Ren F, Zhavoronkov A. The Hitchhiker's Guide to Deep Learning Driven Generative Chemistry. ACS Med Chem Lett 2023; 14:901-915. [PMID: 37465301 PMCID: PMC10351082 DOI: 10.1021/acsmedchemlett.3c00041] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 06/09/2023] [Indexed: 07/20/2023] Open
Abstract
This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental in vitro validation in biochemical assays of the generated molecular structures, where we analyze the reported structures' relevance in modern medicinal chemistry and their novelty. The authors believe that this review would be appreciated by medicinal chemistry and AI-driven drug design (AIDD) communities and can be adopted as a comprehensive approach for qualifying different research outcomes in AIDD.
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Affiliation(s)
- Yan Ivanenkov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Bogdan Zagribelnyy
- Insilico
Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, P.O.
Box 145748, Masdar City, Abu Dhabi United Arab Emirates
| | - Alex Malyshev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Sergei Evteev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Victor Terentiev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Petrina Kamya
- Insilico
Medicine Canada Inc., 3710-1250 René-Lévesque Blvd W, Montreal, Quebec, Canada H3B 4W8
| | - Dmitry Bezrukov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Alex Aliper
- Insilico
Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, P.O.
Box 145748, Masdar City, Abu Dhabi United Arab Emirates
| | - Feng Ren
- Insilico
Medicine Shanghai Ltd., Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road, Pudong New District, Shanghai 201203, China
| | - Alex Zhavoronkov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
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5
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Yu Y, Huang J, He H, Han J, Ye G, Xu T, Sun X, Chen X, Ren X, Li C, Li H, Huang W, Liu Y, Wang X, Gao Y, Cheng N, Guo N, Chen X, Feng J, Hua Y, Liu C, Zhu G, Xie Z, Yao L, Zhong W, Chen X, Liu W, Li H. Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design. ACS Med Chem Lett 2023; 14:297-304. [PMID: 36923916 PMCID: PMC10009793 DOI: 10.1021/acsmedchemlett.2c00515] [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: 12/08/2022] [Accepted: 01/19/2023] [Indexed: 02/11/2023] Open
Abstract
Selective CDK2 inhibitors have the potential to provide effective therapeutics for CDK2-dependent cancers and for combating drug resistance due to high cyclin E1 (CCNE1) expression intrinsically or CCNE1 amplification induced by treatment of CDK4/6 inhibitors. Generative models that take advantage of deep learning are being increasingly integrated into early drug discovery for hit identification and lead optimization. Here we report the discovery of a highly potent and selective macrocyclic CDK2 inhibitor QR-6401 (23) accelerated by the application of generative models and structure-based drug design (SBDD). QR-6401 (23) demonstrated robust antitumor efficacy in an OVCAR3 ovarian cancer xenograft model via oral administration.
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Affiliation(s)
- Yang Yu
- Tencent
AI Lab, Tencent, Shenzhen 518057, China
| | | | - Hu He
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Jing Han
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Geyan Ye
- Tencent
AI Lab, Tencent, Shenzhen 518057, China
| | - Tingyang Xu
- Tencent
AI Lab, Tencent, Shenzhen 518057, China
| | | | - Xiumei Chen
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Xiaoming Ren
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Chunlai Li
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Huijuan Li
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Wei Huang
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Yangyang Liu
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Xinjuan Wang
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Yongzhi Gao
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Nianhe Cheng
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Na Guo
- BioDuro-Sundia, Shanghai, 200131, China
| | - Xibo Chen
- BioDuro-Sundia, Shanghai, 200131, China
| | | | - Yuxia Hua
- BioDuro-Sundia, Beijing, 102200, China
| | - Chong Liu
- BioDuro-Sundia, Beijing, 102200, China
| | - Guoyun Zhu
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Zhi Xie
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Lili Yao
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Wenge Zhong
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Xinde Chen
- Tencent
AI Lab, Tencent, Shenzhen 518057, China
| | - Wei Liu
- Tencent
AI Lab, Tencent, Shenzhen 518057, China
| | - Hailong Li
- Regor
Therapeutics Group, Shanghai, 201210, China
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6
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Abate C, Decherchi S, Cavalli A. Graph neural networks for conditional de novo drug design. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Carlo Abate
- Fondazione Istituto Italiano di Tecnologia Genoa Italy
- Università degli Studi di Bologna Bologna Italy
| | | | - Andrea Cavalli
- Fondazione Istituto Italiano di Tecnologia Genoa Italy
- Università degli Studi di Bologna Bologna Italy
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7
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Bai Q, Liu S, Tian Y, Xu T, Banegas‐Luna AJ, Pérez‐Sánchez H, Huang J, Liu H, Yao X. Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1581] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Qifeng Bai
- Key Lab of Preclinical Study for New Drugs of Gansu Province Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University Lanzhou Gansu China
| | - Shuo Liu
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Yanan Tian
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Tingyang Xu
- Tencent AI Lab, Shenzhen Tencent Computer Ltd Shenzhen China
| | - Antonio Jesús Banegas‐Luna
- Structural Bioinformatics and High Performance Computing Research Group (BIO‐HPC), Computer Engineering Department UCAM Universidad Católica de Murcia Murcia Spain
| | - Horacio Pérez‐Sánchez
- Structural Bioinformatics and High Performance Computing Research Group (BIO‐HPC), Computer Engineering Department UCAM Universidad Católica de Murcia Murcia Spain
| | - Junzhou Huang
- Tencent AI Lab, Shenzhen Tencent Computer Ltd Shenzhen China
| | - Huanxiang Liu
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Xiaojun Yao
- College of Chemistry and Chemical Engineering Lanzhou University Lanzhou Gansu China
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8
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Taldaev A, Rudnev VR, Nikolsky KS, Kulikova LI, Kaysheva AL. Molecular Modeling Insights into Upadacitinib Selectivity upon Binding to JAK Protein Family. Pharmaceuticals (Basel) 2021; 15:ph15010030. [PMID: 35056087 PMCID: PMC8778839 DOI: 10.3390/ph15010030] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 01/07/2023] Open
Abstract
Rheumatoid arthritis (RA) is a chronic disease characterized by bone joint damage and incapacitation. The mechanism underlying RA pathogenesis is autoimmunity in the connective tissue. Cytokines play an important role in the human immune system for signal transduction and in the development of inflammatory responses. Janus kinases (JAK) participate in the JAK/STAT pathway, which mediates cytokine effects, in particular interleukin 6 and IFNγ. The discovery of small molecule inhibitors of the JAK protein family has led to a revolution in RA therapy. The novel JAK inhibitor upadacitinib (RinvoqTM) has a higher selectivity for JAK1 compared to JAK2 and JAK3 in vivo. Currently, details on the molecular recognition of JAK1 by upadacitinib are not available. We found that characteristics of hydrogen bond formation with the glycine loop and hinge in JAKs define the selectivity. Our molecular modeling study could provide insight into the drug action mechanism and pharmacophore model differences in JAK isoforms.
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Affiliation(s)
- Amir Taldaev
- Biobanking Group, V.N. Orekhovich Institute of Biomedical Chemistry, 109028 Moscow, Russia; (A.T.); (V.R.R.); (K.S.N.); (L.I.K.)
- Department of Chemistry, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
| | - Vladimir R. Rudnev
- Biobanking Group, V.N. Orekhovich Institute of Biomedical Chemistry, 109028 Moscow, Russia; (A.T.); (V.R.R.); (K.S.N.); (L.I.K.)
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia
| | - Kirill S. Nikolsky
- Biobanking Group, V.N. Orekhovich Institute of Biomedical Chemistry, 109028 Moscow, Russia; (A.T.); (V.R.R.); (K.S.N.); (L.I.K.)
| | - Liudmila I. Kulikova
- Biobanking Group, V.N. Orekhovich Institute of Biomedical Chemistry, 109028 Moscow, Russia; (A.T.); (V.R.R.); (K.S.N.); (L.I.K.)
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia
- Institute of Mathematical Problems of Biology RAS—The Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, 142290 Pushchino, Russia
| | - Anna L. Kaysheva
- Biobanking Group, V.N. Orekhovich Institute of Biomedical Chemistry, 109028 Moscow, Russia; (A.T.); (V.R.R.); (K.S.N.); (L.I.K.)
- Correspondence:
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