Shihang Wang

Welcome to talk with me

wshshou@163.com

Macao, China

I’m Shihang Wang (王世航), a PhD student of Macau Polytechnic University (MPU) in 2025, supervised by Xiaojun Yao(姚小军). I received my master degree at ShanghaiTech University in 2025, supervised by Fang Bai (白芳) and Shenghua Gao (高盛华). I received my B.S. degree at Shanghai Ocean University (SHOU) in 2022, supervised by Sihua Peng (彭司华).

My research interests center around the application of artificial intelligence algorithms in drug design, with a particular focus on molecular representation, molecular properties prediction, molecular generation and phenotypic-based drug discovery. Additionally, I am dedicated to advancing computational methods for molecular design in order to find new avenues for breakthroughs in drug development.

My research papers were published at APSB, APS, Journal of Cheminformatics, JCIM, etc., and I have served as a reviewer of TPAMI. I’m looking for like-minded partners. If you have any fancy ideas, please feel free to contact me! My WeChat ID is SeanMC795.

作为一名合格的ENFJ，我会尽我所能与合作者建立长久可靠、互惠互利的合作关系！

news

Jul 08, 2025	PhenoModel has been accepted by Acta Pharmacologica Sinica B (APSB)!
Jun 28, 2025	I published my first review article in npjDD about molecular representation!
Jun 25, 2025	We published a research article in JCIM about PROTAC synthetic accessibility prediction!
May 15, 2025	I passed the defense of my master's thesis and received my master of Science degree!
May 12, 2025	We published a research article in APS about NMDAR lead discovery!

selected publications

JMV

Molecular evolutionary characteristics of SARS-CoV-2 emerging in the United States

Shihang Wang, Xuanyu Xu, Cai Wei, and 7 more authors

Journal of medical virology, 2022

DOI Bib HTML

@article{wang2022molecular,
  title = {Molecular evolutionary characteristics of SARS-CoV-2 emerging in the United States},
  author = {Wang, Shihang and Xu, Xuanyu and Wei, Cai and Li, Sicong and Zhao, Jingying and Zheng, Yin and Liu, Xiaoyu and Zeng, Xiaomin and Yuan, Wenliang and Peng, Sihua},
  journal = {Journal of medical virology},
  volume = {94},
  number = {1},
  pages = {310--317},
  year = {2022},
  publisher = {Wiley Online Library},
  doi = {10.1002/jmv.27331},
}

J Cheminfo
DeepSA: a deep-learning driven predictor of compound synthesis accessibility

Shihang Wang, Lin Wang, Fenglei Li, and 1 more author

Journal of Cheminformatics, 2023

Abs DOI Bib HTML PDF

DeepSA is proposed to predict synthesis accessibility of compounds, and has a much higher early enrichment rate in discriminating molecules that are difficult to synthesize. This helps users to select less expensive molecules for synthesis, thus reducing the time for drug discovery and development. You can use DeepSA on a webserver at https://bailab.siais.shanghaitech.edu.cn/deepsa
@article{wang2023deepsa, title = {DeepSA: a deep-learning driven predictor of compound synthesis accessibility}, author = {Wang, Shihang and Wang, Lin and Li, Fenglei and Bai, Fang}, journal = {Journal of Cheminformatics}, volume = {15}, number = {1}, pages = {103}, year = {2023}, publisher = {Springer}, doi = {10.1186/s13321-023-00771-3}, drugai = {https://mp.weixin.qq.com/s/LyvFQ74dHtEG1MO2j1LXbA}, zhiyaobang = {https://mp.weixin.qq.com/s/WRVKF9LvC4t_ISyhrNbybQ}, }
bioRxiv
PhenoScreen: A Dual-Space Contrastive Learning Framework-based Phenotypic Screening Method by Linking Chemical Perturbations to Cellular Morphology

Shihang Wang, Qilei Han, Weichen Qin, and 8 more authors

bioRxiv, 2024

Abs Bib PDF Video

PhenoModel is a cutting-edge molecular representation foundation model that integrates cell phenotype data to enhance the characterization of molecules. By leveraging the rich information embedded in cell images, PhenoModel significantly improves performance in various drug discovery tasks, particularly in virtual screening. Based on PhenoModel, we developed an active compound screening pipeline called PhenoScreen to further screen other molecules with similar activities but novel scaffolds according to the known active compounds.
@article{wang2024phenoscreen, title = {PhenoScreen: A Dual-Space Contrastive Learning Framework-based Phenotypic Screening Method by Linking Chemical Perturbations to Cellular Morphology}, author = {Wang, Shihang and Han, Qilei and Qin, Weichen and Wang, Lin and Yuan, Junhong and Zhao, Yiqun and Ren, Pengxuan and Zhang, Yunze and Tang, Yilin and Li, Ruifeng and others}, journal = {bioRxiv}, pages = {2024--10}, year = {2024}, publisher = {Cold Spring Harbor Laboratory}, }