人工智能在药物靶点的筛选及验证方面的应用进展

王超; 肖辅; 李妙竹; 潘颖; 丁晓; 任峰; Zhavoronkov Alex; 王亚洲

doi:10.11665/j.issn.1000-5048.2023041102

人工智能在药物靶点的筛选及验证方面的应用进展

英矽智能科技（上海）有限公司，上海 201203

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出版历程
- 收稿日期: 2023-04-10
- 修回日期: 2023-06-12
- 刊出日期: 2023-06-24

Application progress of artificial intelligence in the screening and identification of drug targets

Insilico Medicine Shanghai Ltd., Shanghai 201203, China

摘要

摘要: 近年来人工智能发展迅速，随着算力的提升、算法的迭代，人工智能极大方便了生物信息、化学信息和临床数据的收集及处理，为新药研发注入了新的活力。本综述对人工智能在制药领域的发展历程及其主要算法进行了简要介绍，随后结合具体实例对人工智能在药物靶点筛选及验证方面的不同阶段进行了详细描述，包括药物靶点发现、蛋白结构预测以及苗头化合物生成与优化等。最后对人工智能平台“端到端”的一次高效应用过程进行了具体讨论。
- 人工智能 /
- 靶点发现 /
- 蛋白结构预测 /
- 苗头化合物生成 /
- 端到端应用
Abstract: In recent years, artificial intelligence (AI) has developed rapidly, with improved computing power and algorithms, which has greatly facilitated the collection and processing of biological, chemical information and clinical data, injecting new vitality into the research and development of new drugs.In this review, we began with a brief overview of the development and the main algorithms of AI in drug discovery.Then we elaborated through several specific cases on the various scenarios of AI application, including target identification, protein structure prediction, hit generation and optimization etc.Finally, we focused on a recent example to discuss the high efficiency of "end-to-end" application of AI.
- artificial intelligence /
- target identification /
- protein structure prediction /
- hit generation /
- end-to-end application

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参考文献(65)

[1]	Yang X, Wang YF, Byrne R, et al. Concepts of artificial intelligence for computer-assisted drug discovery[J]. Chem Rev, 2019, 119(18): 10520-10594.
[2]	Sun DX, Gao W, Hu HX, et al. Why 90% of clinical drug development fails and how to improve it[J]? Acta Pharm Sin B, 2022, 12(7): 3049-3062.
[3]	Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects[J]. Drug Discov Today, 2019, 24(3): 773-780.
[4]	Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: progress, challenges and recommendations[J]. Nat Rev Drug Discov, 2019, 18(1): 41-58.
[5]	Merico D, Spickett C, O''Hara M, et al. ATP7B variant c.1934T > G p.Met645Arg causes Wilson disease by promoting exon 6 skipping[J]. NPJ Genom Med, 2020, 5: 16.
[6]	Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors[J]. Nat Biotechnol, 2019, 37(9): 1038-1040.
[7]	Senior AW, Evans R, Jumper J, et al. Improved protein structure prediction using potentials from deep learning[J]. Nature, 2020, 577(7792): 706-710.
[8]	Huawei T Technologies Co Ltd. A general introduction to artificial intelligence[M]//Artificial Intelligence Technology. Singapore: Springer Nature Singapore, 2022: 1-41.
[9]	Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development[J]. Nat Rev Drug Discov, 2019, 18(6): 463-477.
[10]	Vijayan RSK, Kihlberg J, Cross JB, et al. Enhancing preclinical drug discovery with artificial intelligence[J]. Drug Discov Today, 2022, 27(4): 967-984.
[11]	Nag S, Baidya ATK, Mandal A, et al. Deep learning tools for advancing drug discovery and development[J]. 3 Biotech, 2022, 12(5): 110.
[12]	LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[13]	McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity[J]. Bull Math Biol, 1990, 52(1/2): 99-115.
[14]	Zhao WX, Zhou K, Li j, et al. A survey of large language models[J].arXiv,2023:2303.18223.
[15]	Santos R, Ursu O, Gaulton A, et al. A comprehensive map of molecular drug targets[J]. Nat Rev Drug Discov, 2017, 16(1): 19-34.
[16]	Narain N, Kiebish M, Vishnudas V, et al. CSAO-1. Interrogative Biology: Unraveling insights into causal disease drivers by use of a dynamic systems biology and Bayesian AI to identify the intersect of disease and healthy signatures[J]. Neuro Oncol Adv, 2021, 3(Supplement_2): ii1.
[17]	Richardson P, Griffin I, Tucker C, et al. Baricitinib as potential treatment for 2019-nCoV acute respiratory disease[J]. Lancet, 2020, 395(10223): e30-e31.
[18]	Zheng SJ, Rao JH, Song Y, et al. PharmKG: a dedicated knowledge graph benchmark for biomedical data mining[J]. Brief Bioinform, 2021, 22(4): bbaa344.
[19]	Ozerov IV, Lezhnina KV, Izumchenko E, et al. In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development[J]. Nat Commun, 2016, 7(1): 1-11.
[20]	Lee A, Lee K, Kim D. Using reverse docking for target identification and its applications for drug discovery[J]. Expert Opin Drug Discov, 2016, 11(7): 707-715.
[21]	Gao ZT, Li HL, Zhang HL, et al. PDTD: a web-accessible protein database for drug target identification[J]. BMC Bioinformatics, 2008, 9: 104.
[22]	Wang F, Wu FX, Li CZ, et al. ACID: a free tool for drug repurposing using consensus inverse docking strategy[J]. J Cheminform, 2019, 11(1): 73.
[23]	Wang X, Shen YH, Wang SW, et al. PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database[J]. Nucleic Acids Res, 2017, 45(W1): W356-W360.
[24]	Dana JM, Gutmanas A, Tyagi N, et al. SIFTS: updated Structure Integration with Function, Taxonomy and Sequences resource allows 40-fold increase in coverage of structure-based annotations for proteins[J]. Nucleic Acids Res, 2019, 47(D1): D482-D489.
[25]	AlQuraishi M. Machine learning in protein structure prediction[J]. Curr Opin Chem Biol, 2021, 65: 1-8.
[26]	Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold[J]. Nature, 2021, 596(7873): 583-589.
[27]	Baek M, DiMaio F, Anishchenko I, et al. Accurate prediction of protein structures and interactions using a three-track neural network[J]. Science, 2021, 373(6557): 871-876.
[28]	Lin ZM, Akin H, Rao R, et al. Evolutionary-scale prediction of atomic-level protein structure with a language model[J]. Science, 2023, 379(6637): 1123-1130.
[29]	Chowdhury R, Bouatta N, Biswas S, et al. Single-sequence protein structure prediction using a language model and deep learning[J]. Nat Biotechnol, 2022, 40(11): 1617-1623.
[30]	Xie QZ, Luong MT, Hovy E, et al. Self-training with noisy student improves ImageNet classification[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 10684-10695.
[31]	Yang ZY, Zeng XX, Zhao Y, et al. AlphaFold2 and its applications in the fields of biology and medicine[J]. Signal Transduct Target Ther, 2023, 8(1): 115.
[32]	Marsango S, Barki N, Jenkins L, et al. Therapeutic validation of an orphan G protein-coupled receptor: the case of GPR84[J]. Br J Pharmacol, 2022, 179(14): 3529-3541.
[33]	Mahindra A, Jenkins L, Marsango S, et al. Investigating the structure-activity relationship of 1, 2, 4-triazine G-protein-coupled receptor 84 (GPR84) antagonists[J]. J Med Chem, 2022, 65(16): 11270-11290.
[34]	Schneider P, Schneider G. De novo design at the edge of chaos[J]. J Med Chem, 2016, 59(9): 4077-4086.
[35]	Vanhaelen Q, Lin YC, Zhavoronkov A. The advent of generative chemistry[J]. ACS Med Chem Lett, 2020, 11(8): 1496-1505.
[36]	Zhavoronkov A. Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry[J]. Mol Pharmaceutics, 2018, 15(10): 4311-4313.
[37]	Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: generative models for matter engineering[J]. Science, 2018, 361(6400): 360-365.
[38]	SMILES Weininger D., a chemical language and information system. 1. Introduction to methodology and encoding rules[J]. J Chem Inf Comput Sci, 1988, 28(1): 31-36.
[39]	You JX, Liu BW, Ying R, et al. Graph convolutional policy network for goal-directed molecular graph generation[J].arXiv,2018: 1806.02473.
[40]	Zhou ZP, Kearnes S, Li L, et al. Optimization of molecules via deep reinforcement learning[J]. Sci Rep, 2019, 9(1): 10752.
[41]	Gupta A, Müller AT, Huisman BJH, et al. Generative recurrent networks for de novo drug design[J]. Mol Inform, 2018, 37(1/2): 1700111.
[42]	Segler MHS, Kogej T, Tyrchan C, et al. Generating focused molecule libraries for drug discovery with recurrent neural networks[J]. ACS Cent Sci, 2018, 4(1): 120-131.
[43]	Olivecrona M, Blaschke T, Engkvist O, et al. Molecular de-novo design through deep reinforcement learning[J]. J Cheminform, 2017, 9(1): 48.
[44]	Kingma DP, Welling M. Auto-encoding variational Bayes[J]. arXiv,2013:1312.6114.
[45]	Gómez-Bombarelli R, Wei JN, Duvenaud D, et al. Automatic chemical design using a data-driven continuous representation of molecules[J]. ACS Cent Sci, 2018, 4(2): 268-276.
[46]	Blaschke T, Olivecrona M, Engkvist O, et al. Application of generative autoencoder in de novo molecular design[J]. Mol Inform, 2018, 37(1/2): 1700123.
[47]	De Cao N, Kipf T. MolGAN: an implicit generative model for small molecular graphs[J]. arXiv,2018: 1805.11973.
[48]	Sanchez-Lengeling B, Outeiral C, Guimaraes GL, et al. Optimizing distributions over molecular space. An objective-reinforced generative adversarial network for inverse-design chemistry (ORGANIC)[J]. ChemRxiv, 2017:5309668.v3.
[49]	Kadurin A, Aliper A, Kazennov A, et al. The cornucopia of meaningful leads: applying deep adversarial autoencoders for new molecule development in oncology[J]. Oncotarget, 2017, 8(7): 10883-10890.
[50]	Polykovskiy D, Zhebrak A, Vetrov D, et al. Entangled conditional adversarial autoencoder for de novo drug discovery[J]. Mol Pharmaceutics, 2018, 15(10): 4398-4405.
[51]	Shayakhmetov R, Kuznetsov M, Zhebrak A, et al. Molecular generation for desired transcriptome changes with adversarial autoencoders[J]. Front Pharmacol, 2020, 11: 269.
[52]	Yu Y, Xu TY, Li JW, et al. A novel scalarized scaffold hopping algorithm with graph-based variational autoencoder for discovery of JAK1 inhibitors[J]. ACS Omega, 2021, 6(35): 22945-22954.
[53]	Kadurin A, Nikolenko S, Khrabrov K, et al. druGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico[J]. Mol Pharmaceutics, 2017, 14(9): 3098-3104.
[54]	Putin E, Asadulaev A, Ivanenkov Y, et al. Reinforced adversarial neural computer for de novo molecular design[J]. J Chem Inf Model, 2018, 58(6): 1194-1204.
[55]	Richter H, Satz AL, Bedoucha M, et al. DNA-encoded library-derived DDR1 inhibitor prevents fibrosis and renal function loss in a genetic mouse model of alport syndrome[J]. ACS Chem Biol, 2019, 14(1): 37-49.
[56]	Verrall L, Burnet PWJ, Betts JF, et al. The neurobiology of D-amino acid oxidase and its involvement in schizophrenia[J]. Mol Psychiatry, 2010, 15(2): 122-137.
[57]	Tang HF, Jensen K, Houang E, et al. Discovery of a novel class of d-amino acid oxidase inhibitors using the Schr?dinger computational platform[J]. J Med Chem, 2022, 65(9): 6775-6802.
[58]	Bos PH, Houang EM, Ranalli F, et al. AutoDesigner, a De novo design algorithm for rapidly exploring large chemical space for lead optimization: application to the design and synthesis of d-amino acid oxidase inhibitors[J]. J Chem Inf Model, 2022, 62(8): 1905-1915.
[59]	Beuming T, Martín H, Díaz-Rovira AM, et al. Are deep learning structural models sufficiently accurate for free-energy calculations? application of FEP+ to AlphaFold2-predicted structures[J]. J Chem Inf Model, 2022, 62(18): 4351-4360.
[60]	Ren F, Ding X, Zheng M, et al. AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor[J]. Chem Sci, 2023, 14(6): 1443-1452.
[61]	Pun FW, Liu BHM, Long X, et al. Identification of therapeutic targets for amyotrophic lateral sclerosis using PandaOmics-an AI-enabled biological target discovery platform[J]. Front Aging Neurosci, 2022, 14: 914017.
[62]	Pun FW, Leung GHD, Leung HW, et al. Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine[J]. Aging, 2022, 14(6): 2475-2506.
[63]	Ivanenkov YA, Polykovskiy D, Bezrukov D, et al. Chemistry42: an AI-driven platform for molecular design and optimization[J]. J Chem Inf Model, 2023, 63(3): 695-701.
[64]	Mok MT, Zhou JY, Tang WS, et al. CCRK is a novel signalling hub exploitable in cancer immunotherapy[J]. Pharmacol Ther, 2018, 186: 138-151.
[65]	Chen W, Liu XS, Zhang SY, et al. Artificial intelligence for drug discovery: resources, methods, and applications[J]. Mol Ther Nucleic Acids, 2023, 31: 691-702.