Discovery of novel SOS1 inhibitors using machine learning

Author:

Duo Lihui1,Chen Yi23ORCID,Liu Qiupei12,Ma Zhangyi1,Farjudian Amin4ORCID,Ho Wan Yong5,Low Sze Shin1,Ren Jianfeng1,Hirst Jonathan D.6ORCID,Xie Hua237,Tang Bencan1ORCID

Affiliation:

1. Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, P. R. China

2. Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, 201203 Shanghai, China

3. University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China

4. School of Mathematics, Watson Building, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK

5. Faculty of Medicine and Health Sciences, University of Nottingham (Malaysia Campus), Semenyih 43500, Malaysia

6. School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK

7. Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan Tsuihang New District, Zhongshan 528400, China

Abstract

Machine learning enabled ligand-based virtual screening is a valuable tool in discovering effective SOS1 inhibitors.

Funder

National Key Research and Development Program of China

Royal Academy of Engineering

Ningbo Municipal Bureau of Science and Technology

National Natural Science Foundation of China

Science and Technology Department of Zhejiang Province

Natural Science Foundation of Ningbo

Publisher

Royal Society of Chemistry (RSC)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Construction of IRAK4 inhibitor activity prediction model based on machine learning;Molecular Diversity;2024-07-06

2. Three-Branch Molecular Representation Learning Framework for Predicting Molecular Properties in Drug Discovery;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

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