A pre-trained self-attentive message passing neural network (P-SAMPNN) model was developed based on our anti-osteoclastogenesis dataset for virtual screening purpose. Validation processes proved that P-SAMPNN model was significantly superior to the other base line models. A commercially available natural product library was virtually screened by the P-SAMPNN model and resulted in confirmed 5 hits from 10 selected virtual hits. Among the confirmed hits, compounds AP-123/40765213 and AE-562/43462182 are the nanomolar inhibitors against osteoclastogenesis with a new scaffold. Further studies indicate that AP-123/40765213 and AE-562/43462182 significantly suppress the mRNA expression of RANK and downregulate the expressions of osteoclasts-related genes Ctsk, Nfatc1, and Tracp. Our work demonstrated that P-SAMPNN method can guide phenotype-based drug discovery.

基于我们的抗破骨细胞生成数据集，开发了一种预训练的自注意消息传递神经网络（P-SAMPNN）模型，用于虚拟筛选。验证过程证明，P-SAMPNN模型明显优于其他基线模型。通过P-SAMPNN模型虚拟筛选了市售的天然产物库，并从10个选定的虚拟命中中确认了5个命中。在已确认的成功案例中，化合物AP-123 / 40765213和AE-562 / 43462182是使用新型支架对抗破骨细胞生成的纳摩尔抑制剂。进一步的研究表明，AP-123 / 40765213和AE-562 / 43462182显着抑制RANK的mRNA表达，并下调破骨细胞相关基因Ctsk，Nfatc1和Tracp的表达。