Korshunova Maria, Huang Niles, Capuzzi Stephen, Radchenko Dmytro S, Savych Olena, Moroz Yuriy S, Wells Carrow I, Willson Timothy M, Tropsha Alexander, Isayev Olexandr
Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Commun Chem. 2022 Oct 18;5(1):129. doi: 10.1038/s42004-022-00733-0.
Deep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties. Many deep learning approaches employ reinforcement learning for optimizing the target properties of the generated molecules. However, the success of this approach is often hampered by the problem of sparse rewards as the majority of the generated molecules are expectedly predicted as inactives. We propose several technical innovations to address this problem and improve the balance between exploration and exploitation modes in reinforcement learning. In a proof-of-concept study, we demonstrate the application of the deep generative recurrent neural network architecture enhanced by several proposed technical tricks to design inhibitors of the epidermal growth factor (EGFR) and further experimentally validate their potency. The proposed technical solutions are expected to substantially improve the success rate of finding novel bioactive compounds for specific biological targets using generative and reinforcement learning approaches.
深度生成神经网络在计算化学中越来越多地用于从头设计具有所需特性的分子。许多深度学习方法采用强化学习来优化所生成分子的目标特性。然而，这种方法的成功往往受到稀疏奖励问题的阻碍，因为预计大多数生成的分子会被预测为无活性。我们提出了几种技术创新来解决这个问题，并改善强化学习中探索和利用模式之间的平衡。在一项概念验证研究中，我们展示了通过几种提出的技术技巧增强的深度生成循环神经网络架构在设计表皮生长因子（EGFR）抑制剂方面的应用，并进一步通过实验验证了它们的效力。预计所提出的技术解决方案将大幅提高使用生成式和强化学习方法寻找针对特定生物靶点的新型生物活性化合物的成功率。
