27 citations · 29 across the 8 of their papers we have counts for
9 papers
A Systematic Evaluation of Molecule Generation Models for De Novo Drug Design: From Benchmarks to Practical Insights
Xinrui Xu, Xueer Wang, Dan Luo +2
Molecule generation has emerged as a powerful computational tool for de novo drug design, enabling the exploration of chemical space beyond the limits of conventional virtual scree…
Learning Molecular Representations from Cellular Phenotypes with Structure Preservation
Xuan Lin, Jingyu Sheng, Tengfei Ma +2
Phenotypic drug discovery enables the discovery of functional relationships between molecular structures and cellular responses. However, existing multimodal representation learnin…
CryoProt: A Protein Pretraining Framework with Cross-Box Interactions on Cryo-EM Density Maps
Dan Luo, Xuan Lin, Peng Zhou +4
Despite the growing availability of cryo-electron microscopy (cryo-EM) density maps, effectively leveraging them for protein representation remains challenging. First, current meth…
A Triple-Modal Contrastive Learning Framework with Sequence, Graph, and 3D Features for Drug-Target Interaction Prediction
Le Xu, Xi Zhang, Dan Luo +2
Accurate prediction of drug-target interactions (DTI) is critical for drug discovery. Existing methods often rely on single-modal representations (e.g., sequences or graphs) or com…
Enhancing Chemical Reaction and Retrosynthesis Prediction with Large Language Model and Dual-task Learning
Xuan Lin, Qingrui Liu, Hongxin Xiang +2
Chemical reaction and retrosynthesis prediction are fundamental tasks in drug discovery. Recently, large language models (LLMs) have shown potential in many domains. However, direc…
SDN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion
Tengfei Ma, Yujie Chen, Liang Wang +3
Inductive Knowledge Graph Completion (KGC) aims to infer missing facts between newly emerged entities within knowledge graphs (KGs), posing a significant challenge. While recent st…
Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language Models
Xuan Lin, Long Chen, Yile Wang +2
Large language models (LLMs) are widely applied in various natural language processing tasks such as question answering and machine translation. However, due to the lack of labeled…
Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development
Tengfei Ma, Xuan Lin, Tianle Li +8
Large Language Models (LLMs) have recently demonstrated remarkable performance in general tasks across various fields. However, their effectiveness within specific domains such as…
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction
Tengfei Ma, Yujie Chen, Wen Tao +9
Molecular interaction prediction plays a crucial role in forecasting unknown interactions between molecules, such as drug-target interaction (DTI) and drug-drug interaction (DDI),…