Matthew D. Segall

19 papers Journal 19
YearRankTypeTitle / Venue / Authors
2026 J jnl
Comparing massively-multitask regression algorithms for drug discovery.
J. Comput. Aided Mol. Des.
Eric J. Martin, Xiang-wei Zhu, Patrick Riley, Steven Kearnes, Ekaterina A. Sosnina, Li Tian, Chi-Ming Che, Zijian Wang, Ying Wei, Thomas M. Whitehead, Gareth John Conduit, Matthew D. Segall
2025 J jnl
Improving Predictions of Molecular Properties with Graph Featurisation and Heterogeneous Ensemble Models.
CoRR
Michael L. Parker, Samar Mahmoud, Bailey Montefiore, Mario Öeren, Himani Tandon, Charlotte Wharrick, Matthew D. Segall
2025 J jnl
Improving Predictions of Molecular Properties with Graph Featurization and Heterogeneous Ensemble Models.
J. Chem. Inf. Model.
Michael L. Parker, Samar Mahmoud, Bailey Montefiore, Mario Öeren, Himani Tandon, Charlotte Wharrick, Matthew D. Segall
2023 J jnl
Predicting Regioselectivity of Cytosolic Sulfotransferase Metabolism for Drugs.
J. Chem. Inf. Model.
Mario Öeren, Sylvia C. Kaempf, David J. Ponting, Peter A. Hunt, Matthew D. Segall
2021 J jnl
Imputation of sensory properties using deep learning.
J. Comput. Aided Mol. Des.
Samar Mahmoud, Benedict W. J. Irwin, Dmitriy Chekmarev, Shyam Vyas, Jeff Kattas, Thomas M. Whitehead, Tamsin Mansley, Jack Bikker, Gareth John Conduit, Matthew D. Segall
2021 J jnl
Predicting reactivity to drug metabolism: beyond P450s - modelling FMOs and UGTs.
J. Comput. Aided Mol. Des.
Mario Öeren, Peter J. Walton, Peter A. Hunt, David J. Ponting, Matthew D. Segall
2020 J jnl
Practical Applications of Deep Learning To Impute Heterogeneous Drug Discovery Data.
J. Chem. Inf. Model.
Benedict W. J. Irwin, Julian R. Levell, Thomas M. Whitehead, Matthew D. Segall, Gareth John Conduit
2020 J jnl
Predicting pKa Using a Combination of Semi-Empirical Quantum Mechanics and Radial Basis Function Methods.
J. Chem. Inf. Model.
Peter A. Hunt, Layla Hosseini-Gerami, Tomas Chrien, Jeffrey Plante, David J. Ponting, Matthew D. Segall
2019 J jnl
Imputation of Assay Bioactivity Data Using Deep Learning.
J. Chem. Inf. Model.
Thomas M. Whitehead, Benedict W. J. Irwin, Peter A. Hunt, Matthew D. Segall, Gareth John Conduit
2018 J jnl
WhichP450: a multi-class categorical model to predict the major metabolising CYP450 isoform for a compound.
J. Comput. Aided Mol. Des.
Peter A. Hunt, Matthew D. Segall, Jonathan D. Tyzack
2016 J jnl
Predicting Regioselectivity and Lability of Cytochrome P450 Metabolism Using Quantum Mechanical Simulations.
J. Chem. Inf. Model.
Jonathan D. Tyzack, Peter A. Hunt, Matthew D. Segall
2015 J jnl
The challenges of making decisions using uncertain data.
J. Comput. Aided Mol. Des.
Matthew D. Segall, Edmund J. Champness
2012 J jnl
Can we really do computer-aided drug design?
J. Comput. Aided Mol. Des.
Matthew D. Segall
2011 J jnl
Applying Medicinal Chemistry Transformations and Multiparameter Optimization to Guide the Search for High-Quality Leads and Candidates.
J. Chem. Inf. Model.
Matthew D. Segall, Edmund J. Champness, Chris Leeding, Ryan H. Lilien, Ramgopal R. Mettu, Brian W. Stevens
2010 J jnl
Gaussian Processes for Classification: QSAR Modeling of ADMET and Target Activity.
J. Chem. Inf. Model.
Olga Obrezanova, Matthew D. Segall
2010 J jnl
Making priors a priority.
J. Comput. Aided Mol. Des.
Matthew D. Segall, Andrew Chadwick
2008 J jnl
Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility.
J. Comput. Aided Mol. Des.
Olga Obrezanova, Joelle M. R. Gola, Edmund J. Champness, Matthew D. Segall
2007 J jnl
Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties.
J. Chem. Inf. Model.
Olga Obrezanova, Gábor Csányi, Joelle M. R. Gola, Matthew D. Segall
1999 J jnl
Visualizing VHF Ultrasound of the Human Cornea.
IEEE Computer Graphics and Applications
Matthew D. Segall, Dan Z. Reinstein, Neil F. Johnson