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Department of Chemistry

 

Dr Hongbin Yang

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 here 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

In Silico Prediction of Human Renal Clearance of Compounds Using Quantitative Structure-Pharmacokinetic Relationship Models
J Chen, H Yang, L Zhu, Z Wu, W Li, Y Tang, G Liu
– Chemical research in toxicology
(2020)
acs.chemrestox.9b00447
(DOI: 10.1021/acs.chemrestox.9b00447)
Prediction of the allergic mechanism of haptens via a reaction-substructure-compound-target-pathway network system.
P Di, Z Wu, H Yang, W Li, Y Tang, G Liu
– Toxicol Lett
(2019)
317,
68
(DOI: 10.1016/j.toxlet.2019.09.019)
Insights into mechanisms and severity of drug-induced liver injury via computational systems toxicology approach
Y Peng, Z Wu, H Yang, Y Cai, G Liu, W Li, Y Tang
– Toxicology letters
(2019)
312,
22
(DOI: 10.1016/j.toxlet.2019.05.005)
In silico prediction of chemical reproductive toxicity using machine learning.
C Jiang, H Yang, P Di, W Li, Y Tang, G Liu
– Journal of applied toxicology : JAT
(2019)
39,
844
(DOI: 10.1002/jat.3772)
Insights into pesticide toxicity against aquatic organism: QSTR models on Daphnia Magna
L He, K Xiao, C Zhou, G Li, H Yang, Z Li, J Cheng
– Ecotoxicol Environ Saf
(2019)
173,
285
(DOI: 10.1016/j.ecoenv.2019.02.014)
In silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning
L Liu, H Yang, Y Cai, Q Cao, L Sun, Z Wang, W Li, G Liu, PW Lee, Y Tang
– Toxicol Res (Camb)
(2019)
8,
341
(DOI: 10.1039/c8tx00331a)
In Silico Prediction of Endocrine Disrupting Chemicals Using Single-Label and Multilabel Models.
L Sun, H Yang, Y Cai, W Li, G Liu, Y Tang
– J Chem Inf Model
(2019)
59,
973
(DOI: 10.1021/acs.jcim.8b00551)
Computational Prediction of Site of Metabolism for UGT-Catalyzed Reactions.
Y Cai, H Yang, W Li, G Liu, PW Lee, Y Tang
– Journal of chemical information and modeling
(2019)
59,
1085
(DOI: 10.1021/acs.jcim.8b00851)
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
(2019)
35,
1067
(DOI: 10.1093/bioinformatics/bty707)
ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness
L Guan, H Yang, Y Cai, L Sun, P Di, W Li, G Liu, Y Tang
– MedChemComm
(2019)
10,
148
(DOI: 10.1039/C8MD00472B)
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hy353@cam.ac.uk

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