3 citations · 18 across the 14 of their papers we have counts for
6 papers · 1 filter
Elastic Information Bottleneck
Yuyan Ni, Yanyan Lan, Ao Liu +1
Information bottleneck is an information-theoretic principle of representation learning that aims to learn a maximally compressed representation that preserves as much information…
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…
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…
Fractional Denoising for 3D Molecular Pre-training
Shikun Feng, Yuyan Ni, Yanyan Lan +2
Coordinate denoising is a promising 3D molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. Theoretically, the objec…
Multimodal Molecular Pretraining via Modality Blending
Qiying Yu, Yudi Zhang, Yuyan Ni +4
Self-supervised learning has recently gained growing interest in molecular modeling for scientific tasks such as AI-assisted drug discovery. Current studies consider leveraging bot…