World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
13982
World Ranking
3422
National Ranking
459

Overview

Dong-Sheng Cao is affiliated with Central South University in China and has a significant publication record across multiple fields including biochemistry, genetics, molecular biology, and computer science. Their research spans diverse subfields such as molecular biology, computational theory and mathematics, materials chemistry, pharmacology, and organic chemistry.

The scientist's main research topics include computational drug discovery methods, machine learning applications in materials science and bioinformatics, protein structure and dynamics, bioinformatics and genomic networks, pharmacogenetics and drug metabolism, as well as microbial natural products and biosynthesis.

Frequent coauthors collaborating with Dong-Sheng Cao have been Tingjun Hou, Aiping Lü, Dejun Jiang, Chang-Yu Hsieh, and Xiangxiang Zeng. The scientist's work has appeared repeatedly in several prominent publication venues:

  • Briefings in Bioinformatics
  • Journal of Chemical Information and Modeling
  • Journal of Medicinal Chemistry
  • Journal of Cheminformatics
  • Nucleic Acids Research

Among recent notable papers authored or coauthored by Dong-Sheng Cao are:

  • "ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties," 2021, Nucleic Acids Research
  • "ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support," 2024, Nucleic Acids Research
  • "Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models," 2021, Journal of Cheminformatics
  • "A unified drug-target interaction prediction framework based on knowledge graph and recommendation system," 2021, Nature Communications
  • "InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein-Ligand Interaction Predictions," 2021, Journal of Medicinal Chemistry

Dong-Sheng Cao's work emphasizes computational methodologies for drug discovery and bioinformatics, integrating machine learning techniques to enhance predictive models and biological understanding. The research spans theoretical approaches and practical platforms, often focusing on molecular and protein-level interactions.

Best Publications

  • Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration.

    Hongdong Li;Yizeng Liang;Qingsong Xu;Dongsheng Cao

  • ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database

    Jie Dong;Jie Dong;Ning-Ning Wang;Zhi-Jiang Yao;Lin Zhang

  • Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

    Dejun Jiang;Zhenxing Wu;Chang-Yu Hsieh;Guangyong Chen

  • propy: a tool to generate various modes of Chou’s PseAAC

    Dong-Sheng Cao;Qing-Song Xu;Yi-Zeng Liang

  • An overview of variable selection methods in multivariate analysis of near-infrared spectra

    Yong-Huan Yun;Hong-Dong Li;Bai-Chuan Deng;Dong-Sheng Cao

  • TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models

    Zhi-Jiang Yao;Jie Dong;Yu-Jing Che;Min-Feng Zhu

  • ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation

    Jie Dong;Dong‑Sheng Cao;Hong‑Yu Miao;Shao Liu

  • protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences

    Nan Xiao;Dong-Sheng Cao;Min-Feng Zhu;Qing-Song Xu

  • ChemoPy: freely available python package for computational biology and chemoinformatics

    Dong-Sheng Cao;Qing-Song Xu;Qian-Nan Hu;Yi-Zeng Liang

  • ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting

    Ning-Ning Wang;Jie Dong;Yin-Hua Deng;Min-Feng Zhu

  • A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration.

    Yong-Huan Yun;Wei-Ting Wang;Min-Li Tan;Yi-Zeng Liang

  • A unified drug-target interaction prediction framework based on knowledge graph and recommendation system.

    Qing Ye;Chang-Yu Hsieh;Ziyi Yang;Yu Kang

  • From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

    Chao Shen;Junjie Ding;Zhe Wang;Dongsheng Cao

  • An efficient method of wavelength interval selection based on random frog for multivariate spectral calibration.

    Yong-Huan Yun;Hong-Dong Li;Leslie R. E. Wood;Wei Fan

  • A bootstrapping soft shrinkage approach for variable selection in chemical modeling

    Bai-Chuan Deng;Bai-Chuan Deng;Bai-Chuan Deng;Yong-Huan Yun;Dong-Sheng Cao;Yu-Long Yin;Yu-Long Yin

  • PROTAC-DB: an online database of PROTACs.

    Gaoqi Weng;Chao Shen;Dongsheng Cao;Junbo Gao

  • Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning

    Jike Wang;Jike Wang;Chang-Yu Hsieh;Mingyang Wang;Xiaorui Wang

  • A new strategy of outlier detection for QSAR/QSPR.

    Dong-Sheng Cao;Yi-Zeng Liang;Qing-Song Xu;Hong-Dong Li

  • Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.

    Zhenxing Wu;Minfeng Zhu;Yu Kang;Elaine Lai-Han Leung

  • PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions.

    Jie Dong;Jie Dong;Zhi-Jiang Yao;Lin Zhang;Feijun Luo

  • Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions

    Dong-Sheng Cao;Nan Xiao;Qing-Song Xu;Alex F. Chen

  • Model population analysis for variable selection

    Hong-Dong Li;Yi-Zeng Liang;Qing-Song Xu;Dong-Sheng Cao

  • The boosting: A new idea of building models

    Dong-Sheng Cao;Qing-Song Xu;Yi-Zeng Liang;Liang-Xiao Zhang

Frequent Co-Authors

Qing-Song Xu
Qing-Song Xu Central South University
Yong Wang
Yong Wang Central South University
Zhiyong Liu
Zhiyong Liu University of Science and Technology Beijing
Jin-Ming Yang
Jin-Ming Yang University of Kentucky
Han-Xiong Li
Han-Xiong Li City University of Hong Kong
Zixin Deng
Zixin Deng Shanghai Jiao Tong University
Yang Xiao
Yang Xiao Chongqing University
Xiangxiang Zeng
Xiangxiang Zeng Hunan University

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