5 citations · 5 across the 2 of their papers we have counts for
8 papers
Deciphering Scientific Reasoning Steps from Outcome Data for Molecule Optimization
Zequn Liu, Kehan Wu, Shufang Xie +5
Emerging reasoning models hold promise for automating scientific discovery. However, their training is hindered by a critical supervision gap: experimental outcomes are abundant, w…
Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
Qizhi Pei, Zhimeng Zhou, Kaiyuan Gao +6
The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and…
MolChord: Structure-Sequence Alignment for Protein-Guided Drug Design
Wei Zhang, Zekun Guo, Yingce Xia +4
Structure-based drug design (SBDD), which maps target proteins to candidate molecular ligands, is a fundamental task in drug discovery. Effectively aligning protein structural repr…
Nature Language Model: Deciphering the Language of Nature for Scientific Discovery
Yingce Xia, Peiran Jin, Shufang Xie +43
Foundation models have revolutionized natural language processing and artificial intelligence, significantly enhancing how machines comprehend and generate human languages. Inspire…
FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation
Kaiyuan Gao, Qizhi Pei, Gongbo Zhang +3
Molecular docking is a pivotal process in drug discovery. While traditional techniques rely on extensive sampling and simulation governed by physical principles, these methods are…
MIN: Multi-channel Interaction Network for Drug-Target Interaction with Protein Distillation
Shuqi Li, Shufang Xie, Hongda Sun +4
Traditional drug discovery processes are both time-consuming and require extensive professional expertise. With the accumulation of drug-target interaction (DTI) data from experime…