Small-molecule drugs are of significant importance to human health. The use of efficient model-based de novo drug design method is an option worth considering for expediting the discovery of drugs with satisfactory properties. In this article, a deep learning model is first developed for identifications of protein-ligand complexes with high binding affinity, where the Mol2vec descriptor, the convolutional neural network, and the gate augmentation-based Attention mechanism are used for the model construction. Then, an optimization-based de novo drug design framework is established by integrating the deep learning model into a Mixed-Integer NonLinear Programming (MINLP) model for drug candidate design. The optimal solution of the MINLP model is further verified by the physics-based methods of molecular docking and molecular dynamics simulation. Finally, two case studies involving the design of anticoagulant and antitumor drug candidates are presented to highlight the wide applicability and effectiveness of the MINLP-based de novo drug design framework.

中文翻译：

---

基于数学规划方法和深度学习模型的从头药物设计框架

小分子药物对人类健康具有重要意义。使用有效的基于模型的从头药物设计方法是加快发现具有令人满意特性的药物的一个值得考虑的选择。在本文中，首先开发了一种深度学习模型，用于识别具有高结合亲和力的蛋白质-配体复合物，其中 Mol2vec 描述符、卷积神经网络和基于门增强的注意力机制用于模型构建。然后，基于优化的de novo通过将深度学习模型集成到用于候选药物设计的混合整数非线性规划 (MINLP) 模型中，建立了药物设计框架。MINLP模型的最优解通过基于物理的分子对接和分子动力学模拟方法得到进一步验证。最后，提出了两个涉及抗凝剂和抗肿瘤候选药物设计的案例研究，以突出基于 MINLP 的从头药物设计框架的广泛适用性和有效性。