Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, resulting in either models that generate molecules with poor performance or models that are overfit and produce close analogs of known molecules. In this paper, we reduce this data dependency for the generation of new chemotypes by incorporating docking scores of known and de novo molecules to expand the applicability domain of the reward function and diversify the compounds generated during reinforcement learning. Our approach employs a deep generative model initially trained using a combination of limited known drug activity and an approximate docking score provided by a second machine learned Bayes regression model, with final evaluation of high scoring compounds by a full docking simulation. This strategy results in molecules with docking scores improved by 10–20% compared to molecules of similar size, while being 130 × faster than a docking only approach on a typical GPU workstation. We also show that the increased docking scores correlate with (1) docking poses with interactions similar to known inhibitors and (2) result in higher MM-GBSA binding energies comparable to the energies of known DDR1 inhibitors, demonstrating that the Bayesian model contains sufficient information for the network to learn to efficiently interact with the binding pocket during reinforcement learning. This outcome shows that the combination of the learned latent molecular representation along with the feature-based docking regression is sufficient for reinforcement learning to infer the relationship between the molecules and the receptor binding site, which suggest that our method can be a powerful tool for the discovery of new chemotypes with potential therapeutic applications.

分子设计的生成方法是近年来深入研究的一个领域，作为一种生成具有所需特性的新药物的方法。然而，这些类型的努力通常受到有限的实验活动数据的限制，导致要么生成性能较差的分子的模型，要么生成过度拟合并生成与已知分子相近类似物的模型。在本文中，我们通过结合已知分子和从头分子的对接分数来扩展奖励函数的适用范围并使强化学习过程中生成的化合物多样化，从而减少了生成新化学型的数据依赖性。我们的方法采用了深度生成模型，最初使用有限的已知药物活性和第二个机器学习贝叶斯回归模型提供的近似对接得分的组合进行训练，并通过完整的对接模拟对高得分化合物进行最终评估。与类似大小的分子相比，该策略的分子对接分数提高了 10-20%，同时比典型 GPU 工作站上仅进行对接的方法快 130 倍。我们还表明，增加的对接分数与（1）与已知抑制剂类似的相互作用的对接姿势相关，以及（2）导致与已知 DDR1 抑制剂的能量相当的更高的 MM-GBSA 结合能，证明贝叶斯模型包含足够的信息使网络能够在强化学习期间学习与绑定口袋有效地交互。这一结果表明，学习到的潜在分子表示与基于特征的对接回归的结合足以让强化学习推断分子与受体结合位点之间的关系，这表明我们的方法可以成为预测分子与受体结合位点之间关系的强大工具。发现具有潜在治疗应用的新化学型。