[Paper], [Bohrium App]
This repository contains the official implementation of S-MolSearch, a novel semi-supervised contrastive learning framework for molecular search, as presented in our NeurIPS 2024 paper.
Overview of the S-MolSearch framework
S-MolSearch leverages inverse optimal transport to integrate limited labeled data with extensive unlabeled data, significantly enhancing the accuracy and efficiency of molecule searches in virtual screening.
We will release the code ASAP.
If you find this work useful, please cite our paper:
@inproceedings{
zhou2024smolsearch,
title={S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search},
author={Gengmo Zhou and Zhen Wang and Feng Yu and Guolin Ke and Zhewei Wei and Zhifeng Gao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=wJAF8TGVUG}
}