摘要：Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical *** to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.

摘要：基于砂/树脂断裂与砂树脂界面脱粘失效行为的砂/树脂三维微细观单胞模型被构建,用于研究砂/树脂在拉伸载荷作用下的微观应力特征、树脂粘结桥的损伤破坏模式及微观结构(树脂含量、砂粒粒径、砂粒级配及粘结桥有效横截面积比)对其宏观拉伸强度的影响。该三维微细观单胞模型采用内聚力行为方法刻画粘结砂/树脂界面的失效,采用扩展有限元方法(Extented finite element,XFEM)捕捉树脂基体损伤和裂纹扩展。计算结果表明:所构建的三维微细观单胞模型能够显式刻画砂/树脂微细观结构断裂过程,解释微细观结构断裂机制,能够有效地提供树脂含量、砂粒粒径、砂粒级配、粘结桥有效横截面积比等微细观结构对砂/树脂宏观拉伸强度(St)影响的信息,可为砂/树脂优化设计提供理论指导。