.png){kind=icon}
返回
Sample efficient reinforcement learning with active learning for molecular design
.png){kind=icon}
OA
AI
DOI：10.1039/d3sc04653b.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes and solving real scientific problems with complex and involved environments (up to actual laboratory experiments) requires improvements in terms of sample efficiency to make the most of expensive information. The discovery of new drugs is a major commercial application of RL, motivated by the very large nature of the chemical space and the need to perform multiparameter optimization (MPO) across different properties. In silico methods, such as virtual library screening (VS) and de novo molecular generation with RL, show great promise in accelerating this search. However, incorporation of increasingly complex computational models in these workflows requires increasing sample efficiency. Here, we introduce an active learning system linked with an RL model (RL-AL) for molecular design, which aims to improve the sample-efficiency of the optimization process. We identity and characterize unique challenges combining RL and AL, investigate the interplay between the systems, and develop a novel AL approach to solve the MPO problem. Our approach greatly expedites the search for novel solutions relative to baseline-RL for simple ligand- and structure-based oracle functions, with a 5-66-fold increase in hits generated for a fixed oracle budget and a 4-64-fold reduction in computational time to find a specific number of hits. Furthermore, compounds discovered through RL-AL display substantial enrichment of a multi-parameter scoring objective, indicating superior efficacy in curating high-scoring compounds, without a reduction in output diversity. This significant acceleration improves the feasibility of oracle functions that have largely been overlooked in RL due to high computational costs, for example free energy perturbation methods, and in principle is applicable to any RL domain. Active learning accelerates the design of molecules during generative reinforcement learning by creating surrogate models of expensive reward functions, obtaining a 4- to 64-fold reduction in computational effort per hit.
Keyword:
DRUG DISCOVERY
CONFORMER GENERATION
DOCKING
OPTIMIZATION
INFORMATION
CHEMBL
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.4
论文数:
1.7W
被引数:
9.3W
机构
引用论文
Molecular generative model based on conditional variational autoencoder for de novo molecular designOAAI
OAAI分享收藏分享收藏AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python BindingsAutoDock Vina 1.2.0: 新的对接方法，扩展的力场和Python绑定OAAI
OAAI分享收藏Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization.png)
ACS CENTRAL SCIENCEOAAI
ACS CENTRAL SCIENCEIF10.4
OAAI分享收藏Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor生成式深度学习可以发现一种有效的选择性RIPK1抑制剂NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏分享收藏
查看更多内容