SDEGen: learning to evolve molecular conformations from thermodynamic noise for conformation generation-Reference-Cited by-同舟云学术

SDEGen: learning to evolve molecular conformations from thermodynamic noise for conformation generation

Published:2023 Issue:6 Volume:14 Page:1557-1568
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Zhang Haotian¹,Li Shengming²,Zhang Jintu¹³^ORCID,Wang Zhe¹,Wang Jike¹⁴^ORCID,Jiang Dejun¹,Bian Zhiwen¹,Zhang Yixue¹,Deng Yafeng⁵,Song Jianfei⁵,Kang Yu¹^ORCID,Hou Tingjun¹³^ORCID

Affiliation:

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

2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China

3. State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, Zhejiang, China

4. School of Computer Science, Wuhan University, Wuhan 430072, Hubei, China

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

Abstract

In this paper, we developed a novel conformation generation model, termed SDEGen, learning how a molecule evolves in a stochastic dynamics system starting from noise and eventually relaxing to the conformation that falls into low energy minima.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/SC/D2SC04429C

Reference50 articles.

1. 3D-QSAR in Drug Design - A Review

2. Conformations and 3D pharmacophore searching