化学空间
计算机科学
人工神经网络
人工智能
强化学习
深度学习
发电机(电路理论)
集合(抽象数据类型)
代表(政治)
对抗制
可微函数
机器学习
生成语法
理论计算机科学
药物发现
程序设计语言
化学
政治学
量子力学
法学
功率(物理)
数学
数学分析
物理
生物化学
政治
作者
Evgeny Putin,Arip Asadulaev,Yan A. Ivanenkov,Vladimir Aladinskiy,Benjamín Sánchez-Lengeling,Alán Aspuru‐Guzik,Alex Zhavoronkov
标识
DOI:10.1021/acs.jcim.7b00690
摘要
In silico modeling is a crucial milestone in modern drug design and development. Although computer-aided approaches in this field are well-studied, the application of deep learning methods in this research area is at the beginning. In this work, we present an original deep neural network (DNN) architecture named RANC (Reinforced Adversarial Neural Computer) for the de novo design of novel small-molecule organic structures based on the generative adversarial network (GAN) paradigm and reinforcement learning (RL). As a generator RANC uses a differentiable neural computer (DNC), a category of neural networks, with increased generation capabilities due to the addition of an explicit memory bank, which can mitigate common problems found in adversarial settings. The comparative results have shown that RANC trained on the SMILES string representation of the molecules outperforms its first DNN-based counterpart ORGANIC by several metrics relevant to drug discovery: the number of unique structures, passing medicinal chemistry filters (MCFs), Muegge criteria, and high QED scores. RANC is able to generate structures that match the distributions of the key chemical features/descriptors (e.g., MW, logP, TPSA) and lengths of the SMILES strings in the training data set. Therefore, RANC can be reasonably regarded as a promising starting point to develop novel molecules with activity against different biological targets or pathways. In addition, this approach allows scientists to save time and covers a broad chemical space populated with novel and diverse compounds.
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