Optimization of Molecules via Deep Reinforcement Learning
Optimization of Molecules via Deep Reinforcement Learning
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DOI：
10.1038/s41598-019-47148-x
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发表时间：
2019-07-24
影响因子：
4.6
通讯作者：
Riley, Patrick
中科院分区：
文献类型：
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作者：
Zhou, Zhenpeng;Kearnes, Steven;Riley, Patrick
我们提出了一个框架，我们称之为分子深度Q网络（MolDQN），通过结合化学领域知识和最先进的强化学习技术（双Q学习和随机值函数）进行分子优化。我们直接定义分子上的修饰，从而确保100%的化学有效性。此外，我们在没有对任何数据集进行预训练的情况下进行操作，以避免该集合的选择可能产生的偏差。MoldQN实现了与其他几个最近发表的基准分子优化任务的算法相当或更好的性能。然而，我们也认为，这些任务中的许多并不代表药物发现中的真实的优化问题。受药物化学铅优化过程中面临的问题的启发，我们用多目标强化学习扩展了我们的模型，该模型在保持与原始分子相似性的同时最大化了药物相似性。我们进一步展示了通过化学空间实现分子优化的路径，以了解模型是如何工作的。
We present a framework, which we call Molecule Deep Q-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double Q-learning and randomized value functions). We directly define modifications on molecules, thereby ensuring 100% chemical validity. Further, we operate without pre-training on any dataset to avoid possible bias from the choice of that set. MolDQN achieves comparable or better performance against several other recently published algorithms for benchmark molecular optimization tasks. However, we also argue that many of these tasks are not representative of real optimization problems in drug discovery. Inspired by problems faced during medicinal chemistry lead optimization, we extend our model with multi-objective reinforcement learning, which maximizes drug-likeness while maintaining similarity to the original molecule. We further show the path through chemical space to achieve optimization for a molecule to understand how the model works.