深层生成模型已经显示出设计有效和新颖化学的能力,这可以显著加速生物活性化合物的鉴定。当前的许多型号,然而,使用分子描述符或基于配体的预测方法来引导分子生成朝向期望的性质空间。这将它们的应用限制在数据相对丰富的目标上,忽略那些没有多少数据可以充分训练预测器的地方。此外,基于配体的方法通常将分子生成偏向于先前建立的化学空间,从而限制了他们识别真正新颖化学型的能力。在这项工作中,我们评估了通过Glide-一种基于结构的方法-使用分子对接作为评分函数来指导深度生成模型REINVENT的能力,并将模型性能和行为与基于配体的评分函数进行比较.此外,我们修改了之前发表的MOSES基准测试数据集,以消除对非质子化基团的任何诱导偏倚.我们还提出了一种新的度量数据集多样性的指标,与常用的内部多样性度量相比,重原子计数的分布较少混淆。关于主要发现,我们发现,当优化针对DRD2的对接评分时,该模型将预测的配体亲和力提高到超过已知DRD2活性分子的水平.此外,与基于配体的方法相比,生成的分子占据互补的化学和物理化学空间,和新的物理化学空间相比,已知的DRD2活性分子。此外,基于结构的方法学习生成满足关键残基相互作用的分子,这是仅在考虑蛋白质结构时可用的信息。总的来说,这项工作证明了使用分子对接来指导从头分子生成相对于基于配体的预测因子的优势,新奇,以及识别配体和蛋白质靶标之间关键相互作用的能力。实际上,这种方法在早期命中一代活动中具有应用,以丰富针对特定目标的虚拟库,在以新颖性为中心的项目中,其中从头分子生成要么没有现有的配体知识,要么不应该被它偏颇。
Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it.