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

TL;DR

DiffDTM is a novel diffusion-based deep generative model that efficiently creates bioactive molecules targeting dual proteins using only protein sequences and molecular graphs, overcoming data and generalization challenges.

Contribution

The paper introduces DiffDTM, a structure-free, conditional diffusion model for dual protein-targeted molecule generation, eliminating the need for 3D structures and retraining on new targets.

Findings

Outperforms state-of-the-art models on multiple metrics

Generates drug-like, high-affinity molecules for dual targets

Successfully applied to dopamine D2 and 5-HT1A receptors

Abstract

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 protein 3D structure data requisition for model training, auto-regressive sampling, and model generalization for unseen targets. Here, we proposed DiffDTM, a novel conditional structure-free deep generative model based on a diffusion model for dual targets based molecule generation to address the above issues. Specifically, DiffDTM receives protein sequences and molecular graphs as inputs instead of protein and molecular conformations and incorporates an information fusion module to achieve conditional generation in a one-shot manner. We have conducted 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, we utilized DiffDTM to generate molecules towards dopamine receptor D2 and 5-hydroxytryptamine receptor 1A as new antipsychotics. The experimental results indicate that DiffDTM can be easily plugged into unseen dual targets to generate bioactive molecules, addressing the issues of requiring insufficient active molecule data for training as well as the need to retrain when encountering new targets.