2 citations · 3 across the 5 of their papers we have counts for
5 papers
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction
Yuyan Ni, Shikun Feng, Xin Hong +5
Deep learning methods have been considered promising for accelerating molecular screening in drug discovery and material design. Due to the limited availability of labelled data, v…
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion
Bowen Gao, Minsi Ren, Yuyan Ni +5
In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score. However, further study sh…
Contextual Molecule Representation Learning from Chemical Reaction Knowledge
Han Tang, Shikun Feng, Bicheng Lin +4
In recent years, self-supervised learning has emerged as a powerful tool to harness abundant unlabelled data for representation learning and has been broadly adopted in diverse are…
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…