分子生成模型在从头开始设计分子方面表现出了有希望的能力,在预定的蛋白质口袋中具有高结合亲和力。与传统的基于结构的药物设计策略提供潜在的协同作用。然而,这些模型的生成过程是随机的,配体和蛋白质之间的原子相互作用信息被忽略。另一方面,配体具有与称为热点的残基结合的高倾向。热点残基贡献了大部分结合自由能,并且已被认为是设计分子的吸引人的靶标。在这项工作中,我们建立了一个相互作用提示引导扩散模型,InterDiff来应对挑战。我们的模型涉及四种原子相互作用,并表示为可学习的向量嵌入。这些嵌入作为单个残基指导分子生成过程的条件。全面的计算机模拟实验表明,我们的模型可以以可指导的方式生成具有所需配体-蛋白质相互作用的分子。此外,我们在两种现实的基于蛋白质的治疗剂上验证了InterDiff。结果表明,与已知的靶向药物相比,InterDiff可以产生具有更好或相似结合模式的分子。
Molecular generative models have exhibited promising capabilities in designing molecules from scratch with high binding affinities in a predetermined protein pocket, offering potential synergies with traditional structural-based drug design strategy. However, the generative processes of such models are random and the atomic interaction information between ligand and protein are ignored. On the other hand, the ligand has high propensity to bind with residues called hotspots. Hotspot residues contribute to the majority of the binding free energies and have been recognized as appealing targets for designed molecules. In this work, we develop an interaction prompt guided diffusion model, InterDiff to deal with the challenges. Four kinds of atomic interactions are involved in our model and represented as learnable vector embeddings. These embeddings serve as conditions for individual residue to guide the molecular generative process. Comprehensive in silico experiments evince that our model could generate molecules with desired ligand-protein interactions in a guidable way. Furthermore, we validate InterDiff on two realistic protein-based therapeutic agents. Results show that InterDiff could generate molecules with better or similar binding mode compared to known targeted drugs.