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Advances in deep generative models shed light on de novo molecule generationwith desired properties. However, molecule generation targeted for dual proteintargets still faces formidable challenges including protein 3D structure datarequisition for model training, auto-regressive sampling, and modelgeneralization for unseen targets. Here, we proposed DiffDTM, a novelconditional structure-free deep generative model based on a diffusion model fordual targets based molecule generation to address the above issues.Specifically, DiffDTM receives protein sequences and molecular graphs as inputsinstead of protein and molecular conformations and incorporates an informationfusion module to achieve conditional generation in a one-shot manner. We haveconducted comprehensive multi-view experiments to demonstrate that DiffDTM cangenerate drug-like, synthesis-accessible, novel, and high-binding affinitymolecules targeting specific dual proteins, outperforming the state-of-the-art(SOTA) models in terms of multiple evaluation metrics. Furthermore, we utilizedDiffDTM to generate molecules towards dopamine receptor D2 and5-hydroxytryptamine receptor 1A as new antipsychotics. The experimental resultsindicate that DiffDTM can be easily plugged into unseen dual targets togenerate bioactive molecules, addressing the issues of requiring insufficientactive molecule data for training as well as the need to retrain whenencountering new targets.

DiffDTM: A conditional structure-free framework for bioactive molecules generation targeted for dual proteins