8 citations · 28 across the 21 of their papers we have counts for
5 papers · 1 filter
Protein-ligand binding representation learning from fine-grained interactions
Shikun Feng, Minghao Li, Yinjun Jia +2
The binding between proteins and ligands plays a crucial role in the realm of drug discovery. Previous deep learning approaches have shown promising results over traditional comput…
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method
Yuyan Ni, Shikun Feng, Wei-Ying Ma +2
While molecular pre-training has shown great potential in enhancing drug discovery, the lack of a solid physical interpretation in current methods raises concerns about whether the…
DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
Bowen Gao, Bo Qiang, Haichuan Tan +6
Virtual screening, which identifies potential drugs from vast compound databases to bind with a particular protein pocket, is a critical step in AI-assisted drug discovery. Traditi…
UniMAP: Universal SMILES-Graph Representation Learning
Shikun Feng, Lixin Yang, Yanwen Huang +3
Molecular representation learning is fundamental for many drug related applications. Most existing molecular pre-training models are limited in using single molecular modality, eit…
ProFSA: Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment
Bowen Gao, Yinjun Jia, Yuanle Mo +4
Pocket representations play a vital role in various biomedical applications, such as druggability estimation, ligand affinity prediction, and de novo drug design. While existing ge…