Update Uni-Mol_30_12_2024.md (#215)

Author: RussellHu41

source/_posts/Uni-Mol_30_12_2024.md

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 - Uni-Mol
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-Virtual screening is a crucial technique in the early stage of drug discovery, aiming to identify potential drug candidate molecules from vast molecular libraries. Ligand - based virtual screening, such as molecule search, has drawn significant attention as it does not rely on specific protein site information. Recently, researchers Zhen Wang, Feng Yu, Guolin Ke, and Zhifeng Gao from DeepModeling, in collaboration with Professor Zhewei Wei from Renmin University of China and doctoral student Gengmo Zhou, published a paper titled "S - MolSearch: 3D Semi - supervised Contrastive Learning for Bioactive Molecule Search" at the top machine learning conference NeurIPS 2024. This paper introduced in detail a 3D semi - supervised learning framework called S - MolSearch for active molecule search. The S - MolSearch is designed based on the principle of inverse optimal transport and can effectively combine and utilize labeled and unlabeled data, demonstrating a remarkable improvement over existing virtual screening methods. Uni - Mol functions as a 3D molecule encoder and plays a central role in this process, showcasing its great potential in molecule representation and molecule similarity measurement and providing strong support for new drug discovery.
+Virtual screening is a crucial technique in the early stage of drug discovery, aiming to identify potential drug candidate molecules from vast molecular libraries. Ligand - based virtual screening, such as molecule search, has drawn significant attention as it does not rely on specific protein site information. Recently, researchers Zhen Wang, Feng Yu, Guolin Ke, and Zhifeng Gao from DP Technology, in collaboration with Professor Zhewei Wei from Renmin University of China and doctoral student Gengmo Zhou, published a paper titled "S - MolSearch: 3D Semi - supervised Contrastive Learning for Bioactive Molecule Search" at the top machine learning conference NeurIPS 2024. This paper introduced in detail a 3D semi - supervised learning framework called S - MolSearch for active molecule search. The S - MolSearch is designed based on the principle of inverse optimal transport and can effectively combine and utilize labeled and unlabeled data, demonstrating a remarkable improvement over existing virtual screening methods. Uni - Mol functions as a 3D molecule encoder and plays a central role in this process, showcasing its great potential in molecule representation and molecule similarity measurement and providing strong support for new drug discovery.
 
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