Avid M. Afzal

AstraZeneca (United Kingdom) — United Kingdom

Computer Science · Physical Sciences

13h-index553citations35works0.02yr avg
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Research Topics

Computational Drug Discovery Methods(26), Protein Structure and Dynamics(9), Metabolomics and Mass Spectrometry Studies(5), Microbial Natural Products and Biosynthesis(3), Bioinformatics and Genomic Networks(3)

Publications35 total

A survey of Bayesian statistical methods in biomarker discovery and early clinical development
Communications in Statistics Case Studies Data Analysis and Applications·2023
Flexible Fitting of PROTAC Concentration–Response Curves with Changepoint Gaussian Processes
SLAS DISCOVERY·2021· 8 cited
Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty
Journal of Cheminformatics·2021· 19 citedOpen Access
Probabilistic Random Forest Improves Bioactivity Predictions Close to the Classification Threshold by Taking into Account Experimental Uncertainty
2021· 1 citedOpen Access
Probabilistic Random Forest Improves Bioactivity Predictions Close to the Classification Threshold by Taking into Account Experimental Uncertainty
2021· 3 citedOpen Access
Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions
Journal of Chemical Information and Modeling·2021· 48 cited
Towards understanding antimicrobial activity, cytotoxicity and the mode of action of dichapetalins A and M using in silico and in vitro studies
Toxicon·2021· 6 cited
Systematic Analysis of Protein Targets Associated with Adverse Events of Drugs from Clinical Trials and Postmarketing Reports
Chemical Research in Toxicology·2020· 24 cited
New Associations between Drug-Induced Adverse Events in Animal Models and Humans Reveal Novel Candidate Safety Targets
Chemical Research in Toxicology·2020· 15 citedOpen Access
Flexible fitting of PROTAC concentration-response curves with changepoint Gaussian Processes
2020· 3 citedOpen Access
Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Protein–Ligand Predictions
Journal of Chemical Information and Modeling·2020· 21 cited
A Bayesian neural network for toxicity prediction
Computational Toxicology·2020· 43 cited
Multitask Bioactivity Predictions Using Structural Chemical and Cell Morphology Information
2020· 2 citedOpen Access
Multitask Bioactivity Predictions Using Structural Chemical and Cell Morphology Information
2020· 2 citedOpen Access
Systematic analysis of protein targets associated with adverse events of drugs from clinical trials and post-marketing reports
2020· 3 citedOpen Access
A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions
2020· 2 citedOpen Access
A Bayesian neural network for toxicity prediction
2020· 3 citedOpen Access
A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions
2020· 3 citedOpen Access
A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions
2020· 4 citedOpen Access
Understanding Conditional Associations between ToxCast <i>in Vitro</i> Readouts and the Hepatotoxicity of Compounds Using Rule-Based Methods
Chemical Research in Toxicology·2019· 7 cited
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Frequent Co-authors

Andreas Bender(24), Lewis Mervin(14), Ola Engkvist(13), Ian P. Barrett(8), Fredrik Svensson(7), Maria‐Anna Trapotsi(6), Hamse Y. Mussa(5), Robert C. Glen(4), Elizaveta Semenova(4), Stanley E. Lazic(4), Noé Sturm(3), Richard E. Turner(3), Ines Smit(3), Dominic P. Williams(2), Chad H. G. Allen(2), Thierry Hanser(2), Ulf Norinder(2), Kathryn A. Giblin(2), Nigel Greene(2), David P. Leader(2)