有意义的机器学习模型和来自片段筛选活动的机器学习药效团
Meaningful machine learning models and machine-learned pharmacophores from fragment screening campaigns
摘要：
机器学习（ML）广泛用于药物发现以训练预测蛋白质-配体结合的模型。这些模型对药物化学家具有重要价值，特别是如果它们提供了对驱动结合过程的物理相互作用的特定情况的洞察。在这项研究中，我们从50多个片段筛选活动中推导出ML模型，以引入两个重要元素，我们认为这两个元素在迄今为止报道的大多数（如果不是全部的话）这种类型的ML研究中都不存在：首先，除了我们用来训练我们的模型的观察到的命中率之外，我们还纳入了真实的未命中率，并表明这些实验验证的阴性数据对导出模型的质量具有重要意义。其次，我们提供了一个物理上可解释和可验证的表示，ML模型认为成功绑定的重要性。这种表示是来自一个简单的归因程序，解释了预测的化学环境的（相互）作用。重要的是，我们验证了归因结果在大规模上对先前的注释独立的专家分子建模。我们发现ML模型提出的关键分子子结构与手动分配的分子子结构之间存在良好的一致性，即使该模型在区分命中和未命中方面的性能远非完美。通过将属性投射到预定义的相互作用原型（药效团）上，我们表明ML允许我们制定简单的规则，用于从筛选数据中自动驱动片段结合目标。
英文摘要：
Machine learning (ML) is widely used in drug discovery to train models that predict protein-ligand binding. These models are of great value to medicinal chemists, in particular if they provide case-specific insight into the physical interactions that drive the binding process. In this study we derive ML models from over 50 fragment-screening campaigns to introduce two important elements that we believe are absent in most -- if not all -- ML studies of this type reported to date: First, alongside the observed hits we use to train our models, we incorporate true misses and show that these experimentally validated negative data are of significant importance to the quality of the derived models. Second, we provide a physically interpretable and verifiable representation of what the ML model considers important for successful binding. This representation is derived from a straightforward attribution procedure that explains the prediction in terms of the (inter-)action of chemical environments. Critically, we validate the attribution outcome on a large scale against prior annotations made independently by expert molecular modellers. We find good agreement between the key molecular substructures proposed by the ML model and those assigned manually, even when the model's performance in discriminating hits from misses is far from perfect. By projecting the attribution onto predefined interaction prototypes (pharmacophores), we show that ML allows us to formulate simple rules for what drives fragment binding against a target automatically from screening data.
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