The rapid development of new machine learning techniques led to significant progress in the area of computer-aided drug design. However, despite the enormous predictive power of new methods, they lack explainability and are often used as black boxes. The most important decisions in drug discovery are still made by human experts who rely on intuitions and simplified representation of the field. We used D3R Grand Challenge 4 to model contributions of human experts during the prediction of the structure of protein-ligand complexes, and prediction of binding affinities for series of ligands in the context of absence or abundance of experimental data. We demonstrated that human decisions have a series of biases: a tendency to focus on easily identifiable protein-ligand interactions such as hydrogen bonds, and neglect for a more distributed and complex electrostatic interactions and solvation effects. While these biases still allow human experts to compete with blind algorithms in some areas, the underutilization of the information leads to significantly worse performance in data-rich tasks such as binding affinity prediction.

中文翻译：

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人在循环中的作用：D3R挑战4的教训。

新机器学习技术的迅速发展导致计算机辅助药物设计领域的重大进步。但是，尽管新方法具有巨大的预测能力，但它们缺乏解释性，经常被用作黑匣子。依靠直觉和简化领域表示的人类专家仍然在药物发现中做出最重要的决定。我们使用D3R大挑战4在蛋白质-配体复合物的结构预测和模型配体的结合亲和力预测中，在缺乏或没有实验数据的情况下，对人类专家的贡献进行建模。我们证明了人类的决策存在一系列偏见：倾向于集中于易于识别的蛋白质-配体相互作用（如氢键，而忽略了更分散和复杂的静电相互作用和溶剂化作用。尽管这些偏见仍使人类专家可以在某些领域与盲目算法竞争，但信息的未充分利用导致在诸如绑定亲和力预测之类的数据丰富的任务中，性能明显下降。