Exploring the origin of multi-target activity of small molecules and designing new multi-target compounds are highly topical issues in pharmaceutical research. We have investigated the ability of a generative neural network to create multi-target compounds. Data sets of experimentally confirmed multi-target, single-target, and consistently inactive compounds were extracted from public screening data considering positive and negative assay results. These data sets were used to fine-tune the REINVENT generative model via transfer learning to systematically recognize multi-target compounds, distinguish them from single-target or inactive compounds, and construct new multi-target compounds. During fine-tuning, the model showed a clear tendency to increasingly generate multi-target compounds and structural analogs. Our findings indicate that generative models can be adopted for de novo multi-target compound design.

探索小分子多靶点活性的起源并设计新的多靶点化合物是药物研究中的热门话题。我们研究了生成神经网络创建多目标化合物的能力。考虑到阳性和阴性测定结果，从公共筛选数据中提取实验证实的多靶点、单靶点和始终无活性的化合物的数据集。这些数据集用于通过迁移学习微调 REINVENT 生成模型，以系统地识别多目标化合物，将它们与单目标或非活性化合物区分开来，并构建新的多目标化合物。在微调期间，该模型显示出越来越多地生成多目标化合物和结构类似物的明显趋势。