S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search
<|reference_start|>S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search: 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%.<|reference_end|>
@article{zhou2024s-molsearch:,
    title={S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive
  Molecule Search},
    author={Gengmo Zhou, Zhen Wang, Feng Yu, Guolin Ke, Zhewei Wei, Zhifeng Gao},
    journal={arXiv preprint arXiv:2409.07462},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07462},
    primaryClass={q-bio.BM cs.LG}
}