2025-26

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Browsing 2025-26 by Author "Nishchitha B"

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    Utilizing 2d And 3d Convolutional Neural Networks For Predicting Protein-Ligand Binding Affinity
    (2026-05-26) Supriya A; Munilakshmi B; Nishchitha B
    he accurate prediction of protein-ligand binding affinity remains a cornerstone challenge in computational drug discovery, directly influencing hit identification, lead optimization, and compound prioritization. Traditional experimental methods such as isothermal titration calorimetry and surface plasmon resonance, while accurate, are hindered by high costs and low throughput. This paper presents a comprehensive investigation of 2D and 3D Convolutional Neural Network (CNN) architectures for protein-ligand binding affinity prediction. We systematically evaluate multiple CNN-based approaches, including 2D CNNs operating on molecular graphs and ligand images, 3D CNNs processing voxelized protein-ligand complexes, and hybrid architectures combining both paradigms. Using the PDBbind v2020 dataset comprising 19,443 protein-ligand complexes and the CASF-2016 core set for benchmarking, we demonstrate that 3D-CNN models achieve superior performance with Pearson correlation coefficients of 0.82-0.86 and RMSE values of 1.27-1.00 on the CASF-2016 benchmark. Hybrid attention-based architectures such as HAC-Net and CGDeepAff further improve performance, achieving Pearson's R of 0.846-0.855 and Spearman's ρ of 0.843-0.861. Our results reveal that 3D spatial representations capture critical geometric complementarity features that 2D approaches miss, while 2D methods offer superior computational efficiency for high-throughput screening. We also identify key challenges including data quality limitations, model interpretability concerns, and generalization to novel protein targets. This paper concludes by outlining future research directions, emphasizing the potential of multi-modal architectures, attention mechanisms, and geometric deep learning for advancing binding affinity prediction toward clinical implementation