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Xu C, Zheng L, Fan Q, Liu Y, Zeng C, Ning X, Liu H, Du K, Lu T, Chen Y, Zhang Y. Progress in the application of artificial intelligence in molecular generation models based on protein structure. Eur J Med Chem 2024; 277:116735. [PMID: 39098131 DOI: 10.1016/j.ejmech.2024.116735] [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: 05/20/2024] [Revised: 07/12/2024] [Accepted: 07/30/2024] [Indexed: 08/06/2024]
Abstract
The molecular generation models based on protein structures represent a cutting-edge research direction in artificial intelligence-assisted drug discovery. This article aims to comprehensively summarize the research methods and developments by analyzing a series of novel molecular generation models predicated on protein structures. Initially, we categorize the molecular generation models based on protein structures and highlight the architectural frameworks utilized in these models. Subsequently, we detail the design and implementation of protein structure-based molecular generation models by introducing different specific examples. Lastly, we outline the current opportunities and challenges encountered in this field, intending to offer guidance and a referential framework for developing and studying new models in related fields in the future.
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Affiliation(s)
- Chengcheng Xu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Lidan Zheng
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Qing Fan
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Yingxu Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Chen Zeng
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Xiangzhen Ning
- 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
| | - Ke Du
- 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.
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
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Zhao Y, He S, Xing Y, Li M, Cao Y, Wang X, Zhao D, Bo X. A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction. Int J Mol Sci 2024; 25:9280. [PMID: 39273227 PMCID: PMC11394757 DOI: 10.3390/ijms25179280] [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: 08/03/2024] [Revised: 08/25/2024] [Accepted: 08/26/2024] [Indexed: 09/15/2024] Open
Abstract
Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exploring the nature of disease. However, accurately identifying protein-ligand binding sites remains a challenging task. To address this, we propose PGpocket, a geometric deep learning-based framework to improve protein-ligand binding site prediction. Initially, the protein surface is converted into a point cloud, and then the geometric and chemical properties of each point are calculated. Subsequently, the point cloud graph is constructed based on the inter-point distances, and the point cloud graph neural network (GNN) is applied to extract and analyze the protein surface information to predict potential binding sites. PGpocket is trained on the scPDB dataset, and its performance is verified on two independent test sets, Coach420 and HOLO4K. The results show that PGpocket achieves a 58% success rate on the Coach420 dataset and a 56% success rate on the HOLO4K dataset. These results surpass competing algorithms, demonstrating PGpocket's advancement and practicality for protein-ligand binding site prediction.
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Affiliation(s)
- Yanpeng Zhao
- Academy of Military Medical Sciences, Beijing 100850, China
| | - Song He
- Academy of Military Medical Sciences, Beijing 100850, China
| | - Yuting Xing
- Defense Innovation Institute, Beijing 100071, China
| | - Mengfan Li
- Academy of Military Medical Sciences, Beijing 100850, China
| | - Yang Cao
- Academy of Military Medical Sciences, Beijing 100850, China
| | - Xuanze Wang
- Academy of Military Medical Sciences, Beijing 100850, China
| | - Dongsheng Zhao
- Academy of Military Medical Sciences, Beijing 100850, China
| | - Xiaochen Bo
- Academy of Military Medical Sciences, Beijing 100850, China
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Shen C, Song J, Hsieh CY, Cao D, Kang Y, Ye W, Wu Z, Wang J, Zhang O, Zhang X, Zeng H, Cai H, Chen Y, Chen L, Luo H, Zhao X, Jian T, Chen T, Jiang D, Wang M, Ye Q, Wu J, Du H, Shi H, Deng Y, Hou T. DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery. J Chem Inf Model 2024; 64:5381-5391. [PMID: 38920405 DOI: 10.1021/acs.jcim.4c00621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.
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Affiliation(s)
- Chao Shen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jianfei Song
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Chang-Yu Hsieh
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004, Hunan, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Wenling Ye
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Odin Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hao Zeng
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Heng Cai
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Yu Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Linkang Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Hao Luo
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Xinda Zhao
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Tianye Jian
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Tong Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Mingyang Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Qing Ye
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jialu Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hongyan Du
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hui Shi
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Tingjun Hou
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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Zhang M, Chen T, Lu X, Lan X, Chen Z, Lu S. G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery. Signal Transduct Target Ther 2024; 9:88. [PMID: 38594257 PMCID: PMC11004190 DOI: 10.1038/s41392-024-01803-6] [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: 08/15/2023] [Revised: 02/19/2024] [Accepted: 03/13/2024] [Indexed: 04/11/2024] Open
Abstract
G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.
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Affiliation(s)
- Mingyang Zhang
- Key Laboratory of Protection, Development and Utilization of Medicinal Resources in Liupanshan Area, Ministry of Education, Peptide & Protein Drug Research Center, School of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Ting Chen
- Department of Cardiology, Changzheng Hospital, Affiliated to Naval Medical University, Shanghai, 200003, China
| | - Xun Lu
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xiaobing Lan
- Key Laboratory of Protection, Development and Utilization of Medicinal Resources in Liupanshan Area, Ministry of Education, Peptide & Protein Drug Research Center, School of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Ziqiang Chen
- Department of Orthopedics, Changhai Hospital, Affiliated to Naval Medical University, Shanghai, 200433, China.
| | - Shaoyong Lu
- Key Laboratory of Protection, Development and Utilization of Medicinal Resources in Liupanshan Area, Ministry of Education, Peptide & Protein Drug Research Center, School of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China.
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Tu G, Fu T, Zheng G, Xu B, Gou R, Luo D, Wang P, Xue W. Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives. J Chem Inf Model 2024; 64:1433-1455. [PMID: 38294194 DOI: 10.1021/acs.jcim.3c01736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.
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Affiliation(s)
- Gao Tu
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Tingting Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | | | - Binbin Xu
- Chengdu Sintanovo Biotechnology Co., Ltd., Chengdu 610200, China
| | - Rongpei Gou
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Ding Luo
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Weiwei Xue
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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Wang M, Wu Z, Wang J, Weng G, Kang Y, Pan P, Li D, Deng Y, Yao X, Bing Z, Hsieh CY, Hou T. Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space. J Chem Inf Model 2024; 64:1213-1228. [PMID: 38302422 DOI: 10.1021/acs.jcim.3c01964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Deep learning-based de novo molecular design has recently gained significant attention. While numerous DL-based generative models have been successfully developed for designing novel compounds, the majority of the generated molecules lack sufficiently novel scaffolds or high drug-like profiles. The aforementioned issues may not be fully captured by commonly used metrics for the assessment of molecular generative models, such as novelty, diversity, and quantitative estimation of the drug-likeness score. To address these limitations, we proposed a genetic algorithm-guided generative model called GARel (genetic algorithm-based receptor-ligand interaction generator), a novel framework for training a DL-based generative model to produce drug-like molecules with novel scaffolds. To efficiently train the GARel model, we utilized dense net to update the parameters based on molecules with novel scaffolds and drug-like features. To demonstrate the capability of the GARel model, we used it to design inhibitors for three targets: AA2AR, EGFR, and SARS-Cov2. The results indicate that GARel-generated molecules feature more diverse and novel scaffolds and possess more desirable physicochemical properties and favorable docking scores. Compared with other generative models, GARel makes significant progress in balancing novelty and drug-likeness, providing a promising direction for the further development of DL-based de novo design methodology with potential impacts on drug discovery.
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Affiliation(s)
- Mingyang Wang
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang ,China
| | - Zhengjian Wu
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
- School of Computer Science, Wuhan University, Wuhan 430072, Hubei ,China
| | - Jike Wang
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang ,China
| | - Gaoqi Weng
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Yu Kang
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Peichen Pan
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Dan Li
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang ,China
| | - Xiaojun Yao
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery Macau Institute for Applied Research in Medicine and Health State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China
| | - Zhitong Bing
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Tingjun Hou
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
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