S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search

• URL: http://arxiv.org/abs/2409.07462v1
• Date: Tue, 27 Aug 2024 14:51:11 GMT
• Title: S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search
• Authors: Gengmo Zhou, Zhen Wang, Feng Yu, Guolin Ke, Zhewei Wei, Zhifeng Gao,
• Abstract summary: We propose S-MolSearch, the first framework to leverage molecular 3D information and affinity information in contrastive learning for virtual screening. S-MolSearch efficiently processes both labeled and unlabeled data, training molecular structural encoders while generating soft labels for the unlabeled data. It surpasses both structure-based and ligand-based virtual screening methods for enrichment factors across 0.5%, 1% and 5%.
• Score: 30.071862398889774
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Virtual Screening is an essential technique in the early phases of drug discovery, aimed at identifying promising drug candidates from vast molecular libraries. Recently, ligand-based virtual screening has garnered significant attention due to its efficacy in conducting extensive database screenings without relying on specific protein-binding site information. Obtaining binding affinity data for complexes is highly expensive, resulting in a limited amount of available data that covers a relatively small chemical space. Moreover, these datasets contain a significant amount of inconsistent noise. It is challenging to identify an inductive bias that consistently maintains the integrity of molecular activity during data augmentation. To tackle these challenges, we propose S-MolSearch, the first framework to our knowledge, that leverages molecular 3D information and affinity information in semi-supervised contrastive learning for ligand-based virtual screening. Drawing on the principles of inverse optimal transport, S-MolSearch efficiently processes both labeled and unlabeled data, training molecular structural encoders while generating soft labels for the unlabeled data. This design allows S-MolSearch to adaptively utilize unlabeled data within the learning process. Empirically, S-MolSearch demonstrates superior performance on widely-used benchmarks LIT-PCBA and DUD-E. It surpasses both structure-based and ligand-based virtual screening methods for enrichment factors across 0.5%, 1% and 5%.

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