Matthew D Segall (Matthew Segall, M D Segall)

Publication Activity (10 Years)

Years Active: 2019-2023
Publications (10 Years): 12

Top Topics

Molecular Docking

Machine Learning

Top Venues

Journal of chemical theory and computation

Journal of chemical information and modeling

Molecular pharmaceutics

Journal of medicinal chemistry

This page only lists publications with an associated author ORCID identifier.

Publications

Elena Gelžinytė, Mario Öeren, Matthew D Segall, Gábor Csányi
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. Journal of chemical theory and computation 20 (1) (2023)
Elena Gelžinytė, Mario Öeren, Matthew D Segall, Gábor Csányi
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. Journal of chemical theory and computation 20 (1) (2023)
Mario Öeren, Sylvia C Kaempf, David J Ponting, Peter A Hunt, Matthew D Segall
Predicting Regioselectivity of Cytosolic Sulfotransferase Metabolism for Drugs. Journal of chemical information and modeling (2023)
Elena Gelžinytė, Mario Öeren, Matthew D Segall, Gábor Csányi
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. Journal of chemical theory and computation (2023)
Elena Gelžinytė, Mario Öeren, Matthew D Segall, Gábor Csányi
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. Journal of chemical theory and computation (2023)
Elena Gelžinytė, Mario Öeren, Matthew D Segall, Gábor Csányi
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. Journal of chemical theory and computation 20 (1) (2023)
Mario Öeren, Peter J Walton, James Suri, David J Ponting, Peter A Hunt, Matthew D Segall
Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning. Journal of medicinal chemistry 65 (20) (2022)
Olga Obrezanova, Anton Martinsson, Tom Whitehead, Samar Mahmoud, Andreas Bender, Filip Miljković, Piotr Grabowski, Ben Irwin, Ioana Oprisiu, Gareth Conduit, Matthew D Segall, Graham F Smith, Beth Williamson, Susanne Winiwarter, Nigel Greene
Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure. Molecular pharmaceutics 19 (5) (2022)
Samar Mahmoud, Benedict Irwin, Dmitriy Chekmarev, Shyam Vyas, Jeff Kattas, Thomas Whitehead, Tamsin Mansley, Jack Bikker, Gareth Conduit, Matthew D Segall
Imputation of sensory properties using deep learning. Journal of computer-aided molecular design 35 (11) (2021)
Benedict W J Irwin, Julian R Levell, Thomas M Whitehead, Matthew D Segall, Gareth J Conduit
Practical Applications of Deep Learning To Impute Heterogeneous Drug Discovery Data. Journal of chemical information and modeling 60 (6) (2020)
Peter A Hunt, Layla Hosseini-Gerami, Tomas Chrien, Jeffrey Plante, David J Ponting, Matthew D Segall
Predicting pKa Using a Combination of Semi-Empirical Quantum Mechanics and Radial Basis Function Methods. Journal of chemical information and modeling 60 (6) (2020)
Thomas M Whitehead, B W J Irwin, Peter A Hunt, Matthew D Segall, G J Conduit
Imputation of Assay Bioactivity Data Using Deep Learning. Journal of chemical information and modeling 59 (3) (2019)