化学空间
生成语法
对抗制
计算机科学
集合(抽象数据类型)
生成设计
强化学习
生成对抗网络
空格(标点符号)
人工智能
机器学习
生化工程
药物发现
深度学习
材料科学
工程类
生物信息学
生物
操作系统
复合材料
程序设计语言
相容性(地球化学)
作者
Benjamín Sánchez-Lengeling,Carlos Outeiral,Gabriel L. Guimaraes,Alán Aspuru‐Guzik
标识
DOI:10.26434/chemrxiv.5309668
摘要
Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.
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