CarsiDock: a deep learning paradigm for accurate protein–ligand docking and screening based on large-scale pre-training-Reference-Cited by-同舟云学术

CarsiDock: a deep learning paradigm for accurate protein–ligand docking and screening based on large-scale pre-training

Published:2024 Issue:4 Volume:15 Page:1449-1471
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Cai Heng¹^ORCID,Shen Chao¹²^ORCID,Jian Tianye¹,Zhang Xujun²,Chen Tong¹,Han Xiaoqi¹,Yang Zhuo¹,Dang Wei¹,Hsieh Chang-Yu¹²^ORCID,Kang Yu²^ORCID,Pan Peichen²^ORCID,Ji Xiangyang³,Song Jianfei¹,Hou Tingjun¹²^ORCID,Deng Yafeng¹

Affiliation:

1. Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China

2. Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China

3. Department of Automation, Tsinghua University, Beijing 100084, China

Abstract

Here we propose CarsiDock, a deep learning-guided docking approach that leverages large-scale pre-training of millions of docking complexes for protein–ligand binding pose generation.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Fundamental Research Funds for the Central Universities

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2024/SC/D3SC05552C

Reference75 articles.