...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Natural science >Exploring Local Chemical Space in De Novo Molecular Generation Using Multi-Agent Deep Reinforcement Learning
【24h】

Exploring Local Chemical Space in De Novo Molecular Generation Using Multi-Agent Deep Reinforcement Learning

机译：利用多症深度加强学习探索德诺分子发电中的当地化学空间

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Single-agent reinforcement learning (RL) is commonly used to learn how to play computer games, in which the agent makes one move before making the next in a sequential decision process. Recently single agent was also employed in the design of molecules and drugs. While a single agent is a good fit for computer games, it has limitations when used in molecule design. Its sequential learning makes it impossible to modify or improve the previous steps while working on the current step. In this paper, we proposed to apply the multi-agent RL approach to the research of molecules, which can optimize all sites of a molecule simultaneously. To elucidate the validity of our approach, we chose one chemical compound Favipiravir to explore its local chemical space. Favipiravir is a broad-spectrum inhibitor of viral RNA polymerase, and is one of the compounds that are currently being used in SARS-CoV-2 (COVID-19) clinical trials. Our experiments revealed the collaborative learning of a team of deep RL agents as well as the learning of its individual learning agent in the exploration of Favipiravir. In particular, our multi-agents not only discovered the molecules near Favipiravir in chemical space, but also the learnability of each site in the string representation of Favipiravir, critical information for us to understand the underline mechanism that supports machine learning of molecules.
机译：单代理强化学习（RL）通常用于学习如何播放计算机游戏，其中代理在顺序决策过程中进行下一步之前进行一次移动。最近，单身剂也用于分子和药物的设计。虽然单个代理商适合电脑游戏，但在分子设计中使用时它具有局限性。其顺序学习使得在研究当前步骤时无法修改或改进前一步骤。在本文中，我们提出将多蛋白酶R1方法应用于分子的研究，其可以同时优化分子的所有部位。为了阐明我们的方法的有效性，我们选择了一种化学品化合物FaviPiraviR探索其当地化学空间。 Favipiravir是一种广谱抑制病毒RNA聚合酶，是目前用于SARS-COV-2（Covid-19）临床试验的化合物之一。我们的实验揭示了一个深入RL代理团队的协作学习以及在FaviPiravir探索中的个人学习代理人的学习。特别是，我们的多代理不仅在化学空间中发现了FaviPiraviR附近的分子，而且还在FaviPiravir的字符串表示中的每个站点的可读性，让我们了解支持分子机器学习的下划线机制。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号