4 citations · 11 across the 13 of their papers we have counts for
5 papers · 1 filter
UniGEM: A Unified Approach to Generation and Property Prediction for Molecules
Shikun Feng, Yuyan Ni, Yan Lu +3
Molecular generation and molecular property prediction are both crucial for drug discovery, but they are often developed independently. Inspired by recent studies, which demonstrat…
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…
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis
Shikun Feng, Jiaxin Zheng, Yinjun Jia +4
Molecular representation learning is pivotal for various molecular property prediction tasks related to drug discovery. Robust and accurate benchmarks are essential for refining an…
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning
Shikun Feng, Yuyan Ni, Minghao Li +4
Recently, a noticeable trend has emerged in developing pre-trained foundation models in the domains of CV and NLP. However, for molecular pre-training, there lacks a universal mode…
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…