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Deep reinforcement learning for de novo drug design

机译：从头开始药物设计的深度强化学习

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We have devised and implemented a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). On the basis of deep and reinforcement learning (RL) approaches, ReLeaSE integrates two deep neural networks—generative and predictive—that are trained separately but are used jointly to generate novel targeted chemical libraries. ReLeaSE uses simple representation of molecules by their simplified molecular-input line-entry system (SMILES) strings only. Generative models are trained with a stack-augmented memory network to produce chemically feasible SMILES strings, and predictive models are derived to forecast the desired properties of the de novo–generated compounds. In the first phase of the method, generative and predictive models are trained separately with a supervised learning algorithm. In the second phase, both models are trained jointly with the RL approach to bias the generation of new chemical structures toward those with the desired physical and/or biological properties. In the proof-of-concept study, we have used the ReLeaSE method to design chemical libraries with a bias toward structural complexity or toward compounds with maximal, minimal, or specific range of physical properties, such as melting point or hydrophobicity, or toward compounds with inhibitory activity against Janus protein kinase 2. The approach proposed herein can find a general use for generating targeted chemical libraries of novel compounds optimized for either a single desired property or multiple properties.
机译：我们已经设计并实施了一种新的计算策略，用于从头设计具有所需特性的分子，称为ReLeaSE（结构演化的增强学习）。 ReLeaSE在深度学习和强化学习（RL）方法的基础上，集成了两个深度神经网络（生成式和预测式），它们分别经过训练，但可以共同用于生成新颖的目标化学库。 ReLeaSE仅通过其简化的分子输入行输入系统（SMILES）字符串使用分子的简单表示形式。用堆栈增强存储网络训练生成模型，以生成化学上可行的SMILES字符串，并推导预测模型以预测从头生成的化合物的所需特性。在该方法的第一阶段，使用监督学习算法分别训练生成模型和预测模型。在第二阶段中，将两种模型与RL方法一起进行训练，以将新化学结构的生成偏向具有所需物理和/或生物学特性的化学结构。在概念验证研究中，我们已经使用ReLeaSE方法设计了化学文库，这些文库偏向于结构复杂性或偏向于具有最大，最小或特定物理特性范围（例如熔点或疏水性）的化合物，或者偏向于化合物具有针对Janus蛋白激酶2的抑制活性的化合物。本文提出的方法可用于产生针对单个期望性质或多个性质优化的新型化合物的靶向化学文库。

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