A Unified Conditional Diffusion Framework for Dual Protein Targets Based Bioactive Molecule Generation

Advances in deep generative models shed light on de novo molecule generation with desired properties. However, molecule generation targeted for dual protein targets still faces formidable challenges including insufficient protein 3D structure data requisition for conditioned model training, inflexibility of auto-regressive sampling, and model generalization to unseen targets. Here, this study proposed DiffDTM, a novel unified structure-free deep generative framework based on a diffusion model for dual-target based molecule generation to address the above issues. Specifically, DiffDTM receives representations of protein sequences and molecular graphs pretrained on large-scale datasets as inputs instead of protein and molecular conformations and incorporates an information fusion module to achieve conditional generation in a one-shot manner. We perform comprehensive multi-view experiments to demonstrate that DiffDTM can generate drug-like, synthesis-accessible, novel, and high-binding affinity molecules targeting specific dual proteins, outperforming the state-of-the-art (SOTA) models in terms of multiple evaluation metrics. Furthermore, DiffDTM could directly generate molecules toward dopamine receptor D2 and 5- hydroxytryptamine receptor 1A as new antipsychotics. Experimental comparisons highlight the generalizability of DiffDTM to easily adapt to unseen dual targets and generate bioactive molecules, addressing the issues of insufficient active molecule data for model training when new targets are encountered.