论文标题
PIGNET：一种具有物理信息的深度学习模型
PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions
论文作者
论文摘要
最近，基于深层的神经网络（DNN）基于药物目标的相互作用（DTI）模型的高度准确性和负担得起的计算成本被突出显示。然而，模型的概括不足仍然是一个具有挑战性的问题，在实践中发现药物的实践。我们提出了两种关键策略，以增强DTI模型中的概括。首先是通过用神经网络参数化的物理形式的方程来预测原子原子的成对相互作用，并提供蛋白质文明复合物作为其总和的总结合亲和力。我们通过扩大对训练数据的更广泛的结合姿势和配体来进一步改善模型的概括。我们在2016年评分函数（CASF）的比较评估中验证了模型Pignet，证明了比以前的方法表现出优于大的对接和筛选功能。我们的物理信息策略还可以通过可视化配体子结构的贡献，从而提供进一步优化的见解来解释预测的亲和力。
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.
