Area of research
Computational Theory and Mathematics · Molecular Biology
Research interest
Research interests include Computational Drug Discovery Methods, Machine Learning in Materials Science, Metabolomics and Mass Spectrometry Studies, and Spectroscopy and Chemometric Analyses.
ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support
Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction
Combined BET and MEK Inhibition synergistically suppresses melanoma by targeting YAP1
Application of extension theory in the optimization of agricultural machinery production processes
Deep learning for drug repurposing: Methods, databases, and applications
Machine learning in accelerating microsphere formulation development
A novel ribosomal protein S6 kinase 2 inhibitor attenuates the malignant phenotype of cutaneous malignant melanoma cells by inducing cell cycle arrest and apoptosis
ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties
MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction
An overview of variable selection methods in multivariate analysis of near-infrared spectra
Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis
ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database
A bootstrapping soft shrinkage approach for variable selection in chemical modeling
Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery
The model adaptive space shrinkage (MASS) approach: a new method for simultaneous variable selection and outlier detection based on model population analysis