论文标题
霉菌：有效药物的交互式设计，深度学习
MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
论文作者
论文摘要
药物的功效取决于其与治疗靶标和药代动力学的结合亲和力。深度学习（DL）在预测药物疗效方面表现出了显着的进步。我们开发了霉菌签名人，这是一种人类的网络用户界面（UI），以帮助药物开发人员利用DL预测来设计更有效的药物。开发人员可以在界面中绘制药物分子。在后端，超过17个最先进的DL模型会对对药物有效性至关重要的重要指数产生预测。基于这些预测，药物开发人员可以编辑药物分子并重申到满意度。霉菌签名人可以实时做出预测，延迟少于一秒钟。
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics. Deep learning (DL) has demonstrated remarkable progress in predicting drug efficacy. We develop MolDesigner, a human-in-the-loop web user-interface (UI), to assist drug developers leverage DL predictions to design more effective drugs. A developer can draw a drug molecule in the interface. In the backend, more than 17 state-of-the-art DL models generate predictions on important indices that are crucial for a drug's efficacy. Based on these predictions, drug developers can edit the drug molecule and reiterate until satisfaction. MolDesigner can make predictions in real-time with a latency of less than a second.
