An improved 3D quantitative structure-activity relationships (QSAR) of molecules with CNN-based partial least squares model

• Published in: Artificial intelligence in the life sciences Vol. 3; p. 100065
• Main Authors: Huo, Xuxiang, Xu, Jun, Xu, Mingyuan, Chen, Hongming
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2023; Elsevier
• ISSN: 2667-3185; 2667-3185
• DOI: 10.1016/j.ailsci.2023.100065

Ligand-based virtual screening plays an important role for cases in which protein structures are not available. Among ligand-based methods, accurate and fast prediction of protein-ligand binding affinity is crucial for reducing computational cost and exploring the chemical search space efficiently. Here we proposed a CNN-based method, termed as L3D-PLS for building the quantitative structure-activity relationships without target structures. In L3D-PLS, a CNN module was designed for extracting the key interaction features from the grids around aligned ligands, and a partial least square (PLS) model fits the binding affinity with the extracted features of the pre-trained CNN module. In 30 publicly available pre-aligned molecular datasets, L3D-PLS outperformed the traditional CoMFA method. This results highlight that L3D-PLS can be useful for lead optimization based on small datasets which is often true in drug discovery compaign.