Concept-Driven Deep Learning for Enhanced Protein-Specific Molecular Generation

• URL: http://arxiv.org/abs/2503.08160v1
• Date: Tue, 11 Mar 2025 08:21:57 GMT
• Title: Concept-Driven Deep Learning for Enhanced Protein-Specific Molecular Generation
• Authors: Taojie Kuang, Qianli Ma, Athanasios V. Vasilakos, Yu Wang, Qiang, Cheng, Zhixiang Ren,
• Abstract summary: We propose a novel fragment-based molecular generation framework tailored for specific proteins.<n>Our approach significantly improves synthetic feasibility and binding affinity, with a 4% increase in drug-likeness and a 6% improvement in synthetic feasibility.
• Score: 28.09898110053281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, deep learning techniques have made significant strides in molecular generation for specific targets, driving advancements in drug discovery. However, existing molecular generation methods present significant limitations: those operating at the atomic level often lack synthetic feasibility, drug-likeness, and interpretability, while fragment-based approaches frequently overlook comprehensive factors that influence protein-molecule interactions. To address these challenges, we propose a novel fragment-based molecular generation framework tailored for specific proteins. Our method begins by constructing a protein subpocket and molecular arm concept-based neural network, which systematically integrates interaction force information and geometric complementarity to sample molecular arms for specific protein subpockets. Subsequently, we introduce a diffusion model to generate molecular backbones that connect these arms, ensuring structural integrity and chemical diversity. Our approach significantly improves synthetic feasibility and binding affinity, with a 4% increase in drug-likeness and a 6% improvement in synthetic feasibility. Furthermore, by integrating explicit interaction data through a concept-based model, our framework enhances interpretability, offering valuable insights into the molecular design process.

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