JIANSHENG WU

About JIANSHENG WU

JIANSHENG WU, With an exceptional h-index of 16 and a recent h-index of 11 (since 2020), a distinguished researcher at Nanjing University of Posts and Telecommunications, specializes in the field of Machine Learning, Bioinformatics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Vina-GPU 2.1: towards further optimizing docking speed and precision of AutoDock Vina and its derivatives

Effectiveness Analysis of Multiple Initial States Simulated Annealing Algorithm, A Case Study on the Molecular Docking Tool AutoDock Vina

Vina-GPU 2.0: further accelerating AutoDock Vina and its derivatives with graphics processing units

An open unified deep graph learning framework for discovering drug leads

Vina-FPGA: A hardware-accelerated molecular docking tool with fixed-point quantization and low-level parallelism

Metric learning for domain adversarial network

Disclosing incoherent sparse and low-rank patterns inside homologous GPCR tasks for better modelling of ligand bioactivities

Transfer learning with molecular graph convolutional networks for accurate modeling and representation of bioactivities of ligands targeting GPCRs without sufficient data

JIANSHENG WU Information

University

Position

Associate Professor of School of Geographic and Biological Information

Citations(all)

914

Citations(since 2020)

445

Cited By

1521

hIndex(all)

16

hIndex(since 2020)

11

i10Index(all)

23

i10Index(since 2020)

13

Email

University Profile Page

Google Scholar

JIANSHENG WU Skills & Research Interests

Machine Learning

Bioinformatics

Top articles of JIANSHENG WU

Vina-GPU 2.1: towards further optimizing docking speed and precision of AutoDock Vina and its derivatives

bioRxiv

2023/11/5

Effectiveness Analysis of Multiple Initial States Simulated Annealing Algorithm, A Case Study on the Molecular Docking Tool AutoDock Vina

Available at SSRN 4120348

2022

Vina-GPU 2.0: further accelerating AutoDock Vina and its derivatives with graphics processing units

Journal of chemical information and modeling

2023/3/20

An open unified deep graph learning framework for discovering drug leads

arXiv preprint arXiv:2301.03424

2022/12/6

Vina-FPGA: A hardware-accelerated molecular docking tool with fixed-point quantization and low-level parallelism

IEEE Transactions on Very Large Scale Integration (VLSI) Systems

2022/11/4

Metric learning for domain adversarial network

Frontiers of Computer Science

2022/10

Disclosing incoherent sparse and low-rank patterns inside homologous GPCR tasks for better modelling of ligand bioactivities

Frontiers of Computer Science

2022/8

Jiansheng Wu
Jiansheng Wu

H-Index: 18

Haifeng Hu
Haifeng Hu

H-Index: 16

Transfer learning with molecular graph convolutional networks for accurate modeling and representation of bioactivities of ligands targeting GPCRs without sufficient data

Computational Biology and Chemistry

2022/6/1

Jiansheng Wu
Jiansheng Wu

H-Index: 18

Haifeng Hu
Haifeng Hu

H-Index: 16

Accelerating autodock vina with gpus

Molecules

2022/5/9

AFSE: towards improving model generalization of deep graph learning of ligand bioactivities targeting GPCR proteins

Briefings in Bioinformatics

2022/5

Realvs: toward enhancing the precision of top hits in ligand-based virtual screening of drug leads from large compound databases

Journal of chemical information and modeling

2021/10/7

Predicting conversion from mci to ad combining multi-modality data and based on molecular subtype

Brain sciences

2021/5/21

Classification of mild cognitive impairment with multimodal data using both labeled and unlabeled samples

IEEE/ACM transactions on computational biology and bioinformatics

2021/1/20

Monitor and System Development of Road Transportation Public Opinion

南京师范大学学报 (工程技术版)[ISSN: 1006-6977/CN: 61-1281/TN]

2021

Homologous G protein-coupled receptors boost the modeling and interpretation of bioactivities of ligand molecules

Journal of chemical information and modeling

2020/2/10

See List of Professors in JIANSHENG WU University(Nanjing University of Posts and Telecommunications)

Co-Authors

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