Virtual screening of potential ATR kinase inhibitors based on machine learning and molecular docking

YAN Yingchao; ZENG Chen; CHEN Yadong

doi:10.11665/j.issn.1000-5048.2023022802

Journal of China Pharmaceutical University > 2023 > 54(3): 323-332. > DOI: 10.11665/j.issn.1000-5048.2023022802

YAN Yingchao, ZENG Chen, CHEN Yadong. Virtual screening of potential ATR kinase inhibitors based on machine learning and molecular docking[J]. Journal of China Pharmaceutical University, 2023, 54(3): 323-332. DOI: 10.11665/j.issn.1000-5048.2023022802

Citation:

PDF (2640 KB)

Virtual screening of potential ATR kinase inhibitors based on machine learning and molecular docking

Institute of Medical Big Data and Artificial Intelligence, School of Science, China Pharmaceutical University, Nanjing 211198, China

More Information

Received Date: February 27, 2023
Revised Date: June 11, 2023

Graphical Abstract

Abstract

Abstract

Screening potential active compounds from molecular libraries is a common method for drug discovery.However, with the continuous exploration of chemical space, there are already compound libraries with more than billions of molecules, so molecular docking is no longer enough to quickly screen specific target inhibitors from the ultra-large compound libraries.This study proposes a method for screening potential active compounds, which involves filtering and selecting compounds from a candidate compound library containing over 5.5 billion molecules through a series of steps, including calculating physical and chemical property similarities, constructing machine learning prediction models, and molecular docking.In the end, 51 compounds with potential ataxia telangiectasia-mutated and rad3-related (ATR) inhibitory activity were obtained.This method is effective for rapidly screening novel potential active compounds from large compound libraries.
- machine learning,
- molecular docking,
- virtual screening,
- ultra-large library,
- ataxia telangiectasia-mutated and Rad3-related (ATR)

FullText(HTML)

References (34)

References

[1]	Bradbury A, Hall S, Curtin N, et al. Targeting ATR as Cancer Therapy: a new era for synthetic lethality and synergistic combinations [J]? Pharmacol Ther, 2020, 207: 107450.
[2]	Zimmermann A, Dahmen H, Grombacher T, et al. Abstract 2588: M1774, a novel potent and selective ATR inhibitor, shows antitumor effects as monotherapy and in combination[J]. Cancer Res, 2022, 82(12_Suppl): 2588.
[3]	Yap Timothy A, Tolcher Anthony W, Ruth PE, et al. A first-in-human phase I study of ATR inhibitor M1774 in patients with solid tumors[J]. J Clin Oncol, 2021, 39(15_suppl): TPS3153.
[4]	Zenke FT, Zimmermann A, Dahmen H, et al. Antitumor activity of M4344, a potent and selective ATR inhibitor, in monotherapy and combination therapy [J]. Cancer Res, 2019, 79(13_Suppl): 369.
[5]	Fokas E, Prevo R, Pollard JR, et al. Targeting ATR in vivo using the novel inhibitor VE-822 results in selective sensitization of pancreatic tumors to radiation[J]. Cell Death Dis, 2012, 3(12): e441.
[6]	Knegtel R, Charrier JD, Durrant S, et al. Rational design of 5-(4-(isopropylsulfonyl) phenyl)-3-(3-(4-((methylamino) methyl) phenyl) isoxazol-5-yl) pyrazin-2-amine (VX-970, M6620): optimization of intra- and intermolecular polar interactions of a new ataxia telangiectasia mutated and Rad3-related (ATR) kinase inhibitor[J]. J Med Chem, 2019, 62(11): 5547-5561.
[7]	Foote KM, Nissink JWM, McGuire T, et al. Discovery and characterization of AZD6738, a potent inhibitor of ataxia telangiectasia mutated and Rad3 related (ATR) kinase with application as an anticancer agent[J]. J Med Chem, 2018, 61(22): 9889-9907.
[8]	Foote KM, Lau A. Drugging ATR: progress in the development of specific inhibitors for the treatment of cancer[J]. Future Med Chem, 2015, 7(7): 873-891.
[9]	Luecking U, Lefranc J, Wengner A, et al. Abstract 983: identification of potent, highly selective and orally available ATR inhibitor BAY 1895344 with favorable PK properties and promising efficacy in monotherapy and combination in preclinical tumor models[J]. Cancer Res, 2017, 77(13_Suppl): 983.
[10]	Wengner AM, Siemeister G, Lücking U, et al. The novel ATR inhibitor BAY 1895344 is efficacious as monotherapy and combined with DNA damage-inducing or repair-compromising therapies in preclinical cancer models[J]. Mol Cancer Ther, 2020, 19(1): 26-38.
[11]	Roulston A, Zimmermann M, Papp R, et al. RP-3500: a novel, potent, and selective ATR inhibitor that is effective in preclinical models as a monotherapy and in combination with PARP inhibitors[J]. Mol Cancer Ther, 2022, 21(2): 245-256.
[12]	Lipinski CA, Lombardo F, Dominy BW, et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings[J]. Adv Drug Deliv Rev, 2001, 46(1/2/3): 3-26.
[13]	Veber DF, Johnson SR, Cheng HY, et al. Molecular properties that influence the oral bioavailability of drug candidates[J]. J Med Chem, 2002, 45(12): 2615-2623.
[14]	Taylor RD, MacCoss M, Lawson AD. Rings in drugs: miniperspective [J]. J Med Chem, 2014, 57(14): 5845-5859.
[15]	Friedman JH. Greedy function approximation: a gradient boosting machine[J]. Ann Statist, 2001, 29(5): 1189-1232.
[16]	Chen TQ, Guestrin C. XGBoost: a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 785-794.
[17]	Dorogush AV, Ershov V, Gulin A. CatBoost: gradient boosting with categorical features support[J]. arXiv, 2018: 1810.11363.
[18]	Tropsha A, Gramatica P, Gombar V. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models[J]. QSAR Comb Sci, 2003, 22(1): 69-77.
[19]	Kar S, Roy K. First report on development of quantitative interspecies structure—carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines[J]. Chemosphere, 2012, 87(4): 339-355.
[20]	Ojha P, Mitra I, Das R, et al. Further exploring rm2 metrics for validation of QSPR models[J]. Chemom Intell Lab Syst, 2011, 107: 194-205.
[21]	Roy PP, Leonard JT, Roy K. Exploring the impact of size of training sets for the development of predictive QSAR models[J]. Chemom Intell Lab Syst, 2008, 90(1): 31-42.
[22]	Pratim Roy P, Paul S, Mitra I, et al. On two novel parameters for validation of predictive QSAR models[J]. Molecules, 2009, 14(5): 1660-1701.
[23]	Mitra I, Roy PP, Kar S, et al. On further application of r as a metric for validation of QSAR models[J]. J Chemom, 2010, 24(1): 22-33.
[24]	Brian Houston J, Carlile DJ. Prediction of hepatic clearance from microsomes, hepatocytes, and liver slices[J]. Drug Metab Rev, 1997, 29(4): 891-922.
[25]	Tang HD, Hussain A, Leal M, et al. Interspecies prediction of human drug clearance based on scaling data from one or two animal species[J]. Drug Metab Dispos, 2007, 35(10): 1886-1893.
[26]	Friesner RA, Banks JL, Murphy RB, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy[J]. J Med Chem, 2004, 47(7): 1739-1749.
[27]	Friesner RA, Murphy RB, Repasky MP, et al. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes[J]. J Med Chem, 2006, 49(21): 6177-6196.
[28]	Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery[J]. Curr Top Med Chem, 2008, 8(18): 1555-1572.
[29]	Vass M, Kooistra AJ, Ritschel T, et al. Molecular interaction fingerprint approaches for GPCR drug discovery[J]. Curr Opin Pharmacol, 2016, 30: 59-68.
[30]	Kozakov D, Grove LE, Hall DR, et al. The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins[J]. Nat Protoc, 2015, 10(5): 733-755.
[31]	Bemis GW, Murcko MA. The properties of known drugs. 1. molecular frameworks[J]. J Med Chem, 1996, 39(15): 2887-2893.
[32]	Polykovskiy D, Zhebrak A, Sanchez-Lengeling B, et al. Molecular sets (MOSES): a benchmarking platform for molecular generation models[J]. Front Pharmacol, 2020, 11: 565644.
[33]	Fearn T. Probabilistic principal component analysis[J]. NIR News, 2014, 25(3): 23.
[34]	Lu YP, Knapp M, Crawford K, et al. Rationally designed PI3Kα mutants to mimic ATR and their use to understand binding specificity of ATR inhibitors[J]. J Mol Biol, 2017, 429(11): 1684-1704.

[1]	TANG Linfang, ZHANG Ziqiang, SU Rina, HE Shuwang, YAO Jing. Advances in taste-masking technology of oral paediatric medicine[J]. Journal of China Pharmaceutical University, 2017, 48(2): 135-141. DOI: 10.11665/j.issn.1000-5048.20170202
[2]	DENG Yan-ping, XIAO Yan-yu, PING Qi-neng, GU Xiao-zhen, BAO Quan-ying. Combined system of sinomenine hydrochloride sustained-release pellets[J]. Journal of China Pharmaceutical University, 2009, 40(3): 222-226.
[3]	Preparation of Turbutaline Sulphate Pulsatile Controlled-release Pellets[J]. Journal of China Pharmaceutical University, 2004, (4): 17-20.
[4]	Studies on Famotidine Pulsatile Controlled-Release Capsules[J]. Journal of China Pharmaceutical University, 1997, (3): 25-29.
[6]	Study on the Controlled-Release System of Propranolol Hydrochloride[J]. Journal of China Pharmaceutical University, 1994, (2): 83-87.
[7]	Studies on Isosorbide- 5- Mononitrate Controlled Release Tablets[J]. Journal of China Pharmaceutical University, 1993, (6): 327-330.
[8]	Studies on Controlled Release Tablet of Piroxicam[J]. Journal of China Pharmaceutical University, 1990, (4): 201-204.
[9]	DEVELOPMENT OF CONTROLLED-RELEASE CHLORPHENIRAMINE PELLETS[J]. Journal of China Pharmaceutical University, 1985, (1): 28-37.
[10]	Li Hanyun, Li Fengwen, Liu Guojie, Chen Shuguang, Cheng Yun. PREDICTION OF SHELF-LIFE OF CONTROLLED- RELEASE OPHTHALMIC FILM OF PILOCARPINE[J]. Journal of China Pharmaceutical University, 1984, (3): 1-5.

Cited By

Cited by

Periodical cited type(2)

1.	杨婧雯，陈芊，单云龙，刘嘉莉，尉宁，王婧，王广基，周芳. 间充质干细胞产品及其外泌体在炎症性肠病治疗中的研究进展. 中国药科大学学报. 2024(01): 103-114 . 本站查看
2.	张强，罗曦，韩丽颖，王帅，包永睿，李天娇，孟宪生. 基于代谢组学研究hUC-MSCs-Exos联合复方木鸡颗粒抑制人肝癌细胞SMMC-7721增殖机制. 中华中医药杂志. 2024(10): 5481-5487 .

Other cited types(1)

Get Citation

PDF

XML

Article views (199) PDF downloads (379) Cited by(3)

Turn off MathJax

Article Contents

Abstract

References

Virtual screening of potential ATR kinase inhibitors based on machine learning and molecular docking

Abstract

References

Related Articles

Cited by

Periodical cited type(2)

Other cited types(1)

Catalog

Virtual screening of potential ATR kinase inhibitors based on machine learning and molecular docking

Abstract

References

Related Articles

Cited by

Periodical cited type(2)

Other cited types(1)

Catalog

Export File

Citation

Format

Content