16 citations · 16 across the 8 of their papers we have counts for
8 papers
Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction
Peng-Fei Sun, Chuan-Xian Ren, Hong Yan
Accurate prediction of protein-ligand binding affinity is essential for structure-based drug discovery. Recent geometric deep learning methods have achieved promising performance b…
Curvature-Guided Geometric Representation for Protein-Ligand Binding Affinity Prediction
Shuai Li, Chuan-Xian Ren, Yuhao Li +4
Protein-ligand binding affinity (PLA) prediction is critical in drug discovery. Despite the notable advancements in machine learning-based approaches, existing methods struggle to…
Locality-aware Private Class Identification for Domain Adaptation with Extreme Label Shift
Chuan-Xian Ren, Cheng-Jun Guo, Hong Yan
Domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain with different distributions. In real-world scenarios, the label spaces of t…
CWFBind: Geometry-Awareness for Fast and Accurate Protein-Ligand Docking
Liyan Jia, Chuan-Xian Ren, Hong Yan
Accurately predicting the binding conformation of small-molecule ligands to protein targets is a critical step in rational drug design. Although recent deep learning-based docking…
Partial Domain Adaptation via Importance Sampling-based Shift Correction
Cheng-Jun Guo, Chuan-Xian Ren, You-Wei Luo +2
Partial domain adaptation (PDA) is a challenging task in real-world machine learning scenarios. It aims to transfer knowledge from a labeled source domain to a related unlabeled ta…
Bi-level Unbalanced Optimal Transport for Partial Domain Adaptation
Zi-Ying Chen, Chuan-Xian Ren, Hong Yan
Partial domain adaptation (PDA) problem requires aligning cross-domain samples while distinguishing the outlier classes for accurate knowledge transfer. The widely used weighting f…
Soft-Masked Semi-Dual Optimal Transport for Partial Domain Adaptation
Yi-Ming Zhai, Chuan-Xian Ren, Hong Yan
Visual domain adaptation aims to learn discriminative and domain-invariant representation for an unlabeled target domain by leveraging knowledge from a labeled source domain. Parti…
Towards Unsupervised Domain Adaptation via Domain-Transformer
Ren Chuan-Xian, Zhai Yi-Ming, Luo You-Wei +1
As a vital problem in pattern analysis and machine intelligence, Unsupervised Domain Adaptation (UDA) attempts to transfer an effective feature learner from a labeled source domain…