Dr Hongbin Yang | Data-Driven Drug Discovery and Molecular Informatics

Research Associate

Dr Hongbin Yang was awarded the second junior CAMS fellowship in 2019. Hongbin started his post-doctoral research project focussed on computational toxicology in November under the academic mentorship of CAMS steering committee member Dr Andreas Bender. Hongbin will have access to the industry knowledge and resources of both AstraZeneca and GSK.

After completing a BSc at East China University of Science and Technology (ECUST) in Shanghai, Hongbin continued his studies there and was awarded a PhD entitled; In Silico Prediction of Chemical ADMET Properties via Statistics and Machine Learning Methods, during which Hongbin focused on structural alerts and QSAR techniques for toxicity prediction and toxicology research. After graduation, Hongbin had a short-term internship in WuXi AppTec (Shanghai), where he combined cheminformatics and deep learning techniques to design retro-synthesis plans.

Publications

AdmetSAR 2.0: Web-service for prediction and optimization of chemical ADMET properties

H Yang, C Lou, L Sun, J Li, Y Cai, Z Wang, W Li, G Liu, Y Tang

Bioinformatics

(2018)

35

1067

(doi: 10.1093/bioinformatics/bty707)

Identification of Nontoxic Substructures: A New Strategy to Avoid Potential Toxicity Risk.

H Yang, L Sun, W Li, G Liu, Y Tang

Toxicol Sci

(2018)

165

396

(doi: 10.1093/toxsci/kfy146)

Multiclassification Prediction of Enzymatic Reactions for Oxidoreductases and Hydrolases Using Reaction Fingerprints and Machine Learning Methods

Y Cai, H Yang, W Li, G Liu, PW Lee, Y Tang

Journal of Chemical Information and Modeling

(2018)

58

1169

(doi: 10.1021/acs.jcim.7b00656)

Corrigendum: In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

H Yang, L Sun, W Li, G Liu, Y Tang

Frontiers in Chemistry

(2018)

6

129

(doi: 10.3389/fchem.2018.00129)

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts.

H Yang, L Sun, W Li, G Liu, Y Tang

Frontiers in Chemistry

(2018)

6

30

(doi: 10.3389/fchem.2018.00030)

In silico estimation of chemical aquatic toxicity on crustaceans using chemical category methods

Q Cao, L Liu, H Yang, Y Cai, W Li, G Liu, PW Lee, Y Tang

Environmental Science: Processes & Impacts

(2018)

20

1234

(doi: 10.1039/c8em00220g)

In silico prediction of chemical genotoxicity using machine learning methods and structural alerts

D Fan, H Yang, F Li, L Sun, P Di, W Li, Y Tang, G Liu

Toxicology research

(2017)

7

211

(doi: 10.1039/c7tx00259a)

In Silico Prediction of Compounds Binding to Human Plasma Proteins by QSAR Models.

L Sun, H Yang, J Li, T Wang, W Li, G Liu, Y Tang

Chemmedchem

(2017)

13

572

(doi: 10.1002/cmdc.201700582)

In silico prediction of pesticide aquatic toxicity with chemical category approaches

F Li, D Fan, H Wang, H Yang, W Li, Y Tang, G Liu

Toxicology research

(2017)

6

831

(doi: 10.1039/c7tx00144d)

Evaluation of Different Methods for Identification of Structural Alerts Using Chemical Ames Mutagenicity Data Set as a Benchmark.

H Yang, J Li, Z Wu, W Li, G Liu, Y Tang

Chemical Research in Toxicology

(2017)

30

1355

(doi: 10.1021/acs.chemrestox.7b00083)