Chemical language models for generating compounds with triple-target activity

Polypharmacology-based drug discovery relies on compounds with multi-target activity that are identified in screening and target profiling assays or using computational methods. Contemporary design of multi-target compounds is advanced by deep generative modeling. Dual-target compounds (DT-CPDs) are known for having a large number of target combinations, providing a sound basis for machine learning. By contrast, only comparably small numbers of triple-target compounds (TT-CPDs) are available, covering a very limited target space. Here, we investigate how this data restriction might be overcome to enable generative design of new TT-CPDs. Therefore, a transformer model is pre-trained to generate DT-CPDs from corresponding single-target compounds and used as a base model for triple-target fine-tuning. For different target combinations, the resulting models correctly reproduce known TT-CPDs not encountered during fine-tuning. Feature importance analysis explains the predictions and reveals structural motifs implicated in target selectivity or triple-target activity, thus providing a chemically intuitive rationale for the approach.