KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction
arXiv:2506.13196v5 Announce Type: replace Abstract: Accurate prediction of protein-ligand binding affinity is critical for drug discovery. While recent deep learning approaches have demonstrated promising results, they often rely solely on structural features of proteins and ligands, overlooking their valuable biochemical knowledge associated with binding affinity. To address this limitation, we propose KEPLA, a novel deep learning framework that explicitly integrates prior knowledge from Gene Ontology and ligand properties to enhance prediction performance. KEPLA takes protein sequences and l
The continuous advancements in AI and deep learning, particularly in graph neural networks and knowledge representation, are enabling more sophisticated approaches to biochemical problems.
Improved protein-ligand binding affinity prediction accelerates drug discovery and development, making the process more efficient and potentially leading to new breakthroughs in therapeutics.
This framework offers a more accurate and knowledge-enhanced method for a crucial step in drug discovery, potentially reducing the time and cost associated with identifying viable drug candidates.
- · Pharmaceutical companies
- · Biotechnology sector
- · AI-driven drug discovery platforms
- · Patients with unmet medical needs
- · Traditional high-throughput screening methods
- · Drug discovery models solely reliant on structural features
More efficient drug lead optimization and reduced failure rates in preclinical stages.
A faster pipeline for novel therapeutics entering clinical trials, addressing a broader range of diseases.
Potential for personalized medicine approaches to leverage improved binding prediction for patient-specific drug design.
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Read at arXiv cs.LG