Recently, deep neural network (DNN)-based drug-target interaction (DTI)
models were highlighted for their high accuracy with affordable computational
costs. Yet, the models' insufficient generalization remains a challenging
problem in the practice of in-silico drug discovery. We propose two key
strategies to enhance generalization in the DTI model. The first is to predict
the atom-atom pairwise interactions via physics-informed equations
parameterized with neural networks and provides the total binding affinity of a
protein-ligand complex as their sum. We further improved the model
generalization by augmenting a broader range of binding poses and ligands to
training data. We validated our model, PIGNet, in the comparative assessment of
scoring functions (CASF) 2016, demonstrating the outperforming docking and
screening powers than previous methods. Our physics-informing strategy also
enables the interpretation of predicted affinities by visualizing the
contribution of ligand substructures, providing insights for further ligand
optimization.

本研究提出通过预测物理信息方程中的原子间相互作用并将其总和用于蛋白质 - 配体复合物的结合亲和力，以增强深度神经网络（DNN）药物靶标交互模型的泛化能力，并通过扩大训练数据的结合位姿和配体来改进模型的泛化能力物。通过可视化配体亚结构的贡献，这种物理信息策略为进一步配体优化提供了见解。我们的模型 PIGNet 在 CASF2016 比较评估中得到验证，表现出优于以前方法的对接和筛选能力。