Efficient and Explainable Virtual Screening of Molecules through Fingerprint-Generating Networks Integrated with Artificial Neural Networks.

ABSTRACT: A machine learning-based drug screening technique has been developed and optimized using a novel, stitched neural network architecture with trainable, graph convolution-based fingerprints as a base into an artificial neural network. The architecture is efficient, explainable, and performant as a tool for the binary classification of ligands based on a user-chosen docking score threshold. Assessment using two standardized virtual screening databases substantiated the architecture's ability to learn molecular features and substructures and predict ligand classes based on binding affinity values more effectively than similar contemporary counterparts. Furthermore, to highlight the architecture's utility to groups and laboratories with varying resources, experiments were carried out using rand

SUBMITTER: Monsia R 

PROVIDER: S-EPMC11822703 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature