Matthew Segall

Optibrium (United Kingdom) — United Kingdom

Computer Science · Physical Sciences

30h-index30.1kcitations97works0.12yr avg
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Research Topics

Computational Drug Discovery Methods(62), Pharmacogenetics and Drug Metabolism(23), Analytical Chemistry and Chromatography(19), Machine Learning in Materials Science(16), Metabolomics and Mass Spectrometry Studies(15)

Publications97 total

Comparing massively-multitask regression algorithms for drug discovery
Journal of Computer-Aided Molecular Design·2026
Improving Predictions of Molecular Properties with Graph Featurization and Heterogeneous Ensemble Models
Journal of Chemical Information and Modeling·2025· 1 citedOpen Access
Comparing Massively-Multitask Regression Algorithms for Drug Discovery
2025Open Access
Addressing common metabolism problems in drug discovery with in silico methods
Drug Metabolism and Pharmacokinetics·2025
Comparing Massively-Multitask Regression Algorithms for Drug Discovery
2025Open Access
Comparing Massively-Multitask Regression Algorithms for Drug Discovery
2025Open Access
Beyond the Hype: The Reality of Implementing AI to Drive Drug Discovery Success
GEN Biotechnology·2024
From quantum mechanics to metabolism pathways
Drug Metabolism and Pharmacokinetics·2024
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules
Journal of Chemical Theory and Computation·2023· 25 citedOpen Access
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules
2023· 1 citedOpen Access
Predicting routes of phase I and II metabolism based on quantum mechanics and machine learning
Xenobiotica·2023· 4 cited
Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecule
2023· 2 citedOpen Access
Predicting Regioselectivity of Cytosolic Sulfotransferase Metabolism for Drugs
Journal of Chemical Information and Modeling·2023· 8 cited
Multi-omics HeCaToS dataset of repeated dose toxicity for cardiotoxic & hepatotoxic compounds
Scientific Data·2022· 11 citedOpen Access
Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning
Journal of Medicinal Chemistry·2022· 21 cited
Replenishing the malaria drug discovery pipeline: Screening and hit evaluation of the MMV Hit Generation Library 1 (HGL1) against asexual blood stage Plasmodium falciparum, using a nano luciferase reporter read-out
SLAS DISCOVERY·2022· 11 citedOpen Access
Replenishing the malaria drug discovery pipeline: Screening and hit evaluation of the MMV Hit Generation Library 1 (HGL1) against asexual blood stage <i>Plasmodium falciparum</i> , using a nano luciferase reporter read-out
2022· 1 citedOpen Access
Prediction of In Vivo Pharmacokinetic Parameters and Time–Exposure Curves in Rats Using Machine Learning from the Chemical Structure
Molecular Pharmaceutics·2022· 70 citedOpen Access
Imputation of sensory properties using deep learning
Journal of Computer-Aided Molecular Design·2021· 14 citedOpen Access
Imputation of Sensory Properties Using Deep Learning
2021Open Access
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Frequent Co-authors

M. C. Payne(12), Chris J. Pickard(8), Olga Obrezanova(6), Thomas M. Whitehead(6), Peter Hunt(6), G. J. Conduit(6), Benedict Irwin(5), Edmund J. Champness(5), Samar Mahmoud(4), Stewart J. Clark(3), P. J. Hasnip(3), Matt Probert(3), Rajiv Shah(3), Joelle M. R. Gola(3), Iskander Yusof(3), David J. Ponting(3), R N Boyes(3), S. W. Ellis(3), Mario Öeren(3), Keith Refson(2)