Ligand-based discovery of coronavirus main protease inhibitors using MACAW molecular embeddings

Ligand-based drug design methods are thought to require large experimental datasets to become useful for virtual screening. In this work, we propose a computational strategy to design novel inhibitors of coronavirus main protease, M^pro. The pipeline integrates publicly available screening and binding affinity data in a two-stage machine-learning model using the recent MACAW embeddings. Once trained, the model can be deployed to rapidly screen large libraries of molecules <i>in silico</i>. Several hundred thousand compounds were virtually screened and 10 of them were selected for experimental testing. From these 10 compounds, 8 showed a clear inhibitory effect on recombinant M^pro, with half-maximal inhibitory concentration values (IC₅₀) in the range 0.18–18.82 μM. Cellular assays were also conducted to evaluate cytotoxic, haemolytic, and antiviral properties. A promising lead compound against coronavirus M^pro was identified with dose-dependent inhibition of virus infectivity and minimal toxicity on human MRC-5 cells.

基于配体的药物设计（Ligand-based drug design）方法通常被认为需要大规模实验数据集，方能有效应用于虚拟筛选（virtual screening）。本研究提出一种计算策略，用于设计新型冠状病毒主蛋白酶（Mpro）抑制剂。该流程将公开可用的筛选与结合亲和力数据，与基于最新MACAW嵌入的两阶段机器学习模型相结合。模型训练完成后，可部署用于快速in silico筛选大规模分子库。本研究对数十万种化合物进行了虚拟筛选，并从中选取10种开展实验测试。其中8种对重组Mpro表现出显著抑制活性，半数抑制浓度（IC₅₀）介于0.18~18.82 μM之间。此外还开展了细胞实验，以评估其细胞毒性、溶血性与抗病毒特性。最终筛选得到一款极具潜力的抗冠状病毒Mpro先导化合物，该化合物可剂量依赖性地抑制病毒感染，且对人MRC-5细胞毒性极低。