宇宙癌症数据库
对抗制
空格(标点符号)
物理
分子动力学
经典力学
计算机科学
统计物理学
人工智能
天文
量子力学
操作系统
作者
М. М. Кузнецов,Fedor Ryabov,Roman Schutski,Rim Shayakhmetov,Yi-Fei Lin,Alex Aliper,Daniil Polykovskiy
标识
DOI:10.1021/acs.jcim.3c00989
摘要
The fast and accurate conformation space modeling is an essential part of computational approaches for solving ligand and structure-based drug discovery problems. Recent state-of-the-art diffusion models for molecular conformation generation show promising distribution coverage and physical plausibility metrics but suffer from a slow sampling procedure. We propose a novel adversarial generative framework, COSMIC, that shows comparable generative performance but provides a time-efficient sampling and training procedure. Given a molecular graph and random noise, the generator produces a conformation in two stages. First, it constructs a conformation in a rotation and translation invariant representation─internal coordinates. In the second step, the model predicts the distances between neighboring atoms and performs a few fast optimization steps to refine the initial conformation. The proposed model considers conformation energy, achieving comparable space coverage, and diversity metrics results.
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