• Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning
  Mario Öeren, Peter J. Walton, James Suri, David J. Ponting, Peter A. Hunt, and Matthew D. Segall
  Journal of Medicinal Chemistry (2022) 65(20) pp, 14066-14081
  DOI: 10.1021/acs.jmedchem.2c01303
• Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure
  Olga Obrezanova, Anton Martinsson, Tom Whitehead, Samar Mahmoud, Andreas Bender, Filip Miljković, Piotr Grabowski, Ben Irwin, Ioana Oprisiu, Gareth Conduit, Matthew Segall, Graham F. Smith, Beth Williamson, Susanne Winiwarter, and Nigel Greene
  Molecular Pharmaceutics (2022) 19(5), pp. 1488-1504
  DOI: 10.1021/acs.molpharmaceut.2c00027
• An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials
  Edwin G. Tse, Laksh Aithani, Mark Anderson, Jonathan Cardoso-Silva, Giovanni Cincilla, Gareth J. Conduit, Mykola Galushka, Davy Guan, Irene Hallyburton, Benedict W. J. Irwin, Kiaran Kirk, Adele M. Lehane, Julia C. R. Lindblom, Raymond Lui, Slade Matthews, James McCulloch, Alice Motion, Ho Leung Ng, Mario Öeren, Murray N. Robertson, Vito Spadavecchio, Vasileios A. Tatsis, Willem P. van Hoorn, Alexander D. Wade, Thomas M. Whitehead, Paul Willis, and Matthew H. Todd*
  J. Med. Chem. (2021) 64(22) pp. 16450-16463
• Imputation of Sensory Properties Using Deep Learning
  Samar Mahmoud, Benedict Irwin, Dmitriy Chekmarev, Shyam Vyas, Jeff Kattas, Thomas Whitehead, Tamsin Mansley, Jack Bikker, Gareth Conduit, Matthew Segall
  J. Comput. Aided Mol. Des. (2021) 35(11) pp. 1125-1140
• Deep Imputation on Large-Scale Drug Discovery Data
  Benedict W J Irwin, Thomas M Whitehead, Scott Rowland, Samar Y. Mahmoud, Gareth J Conduit, Matthew D Segall
  Applied AI Lett. (2021) 2(3) p. e31 DOI: 10.1002/ail2.31
• Guiding Drug Optimisation Using Deep Learning Imputation and Compound Generation
  Benedict W J Irwin, Alex Wade, Matthew D Segall
  International Pharm. Ind. (2020) 12(2) pp. 28-31
• Data Imputation through Deep Learning
  Matthew Segall*, Benedict Irwin*, Thomas Whitehead†, Samar Mahmoud*, Greg Shields*, Graham Turner*, Alex Elliott*, Stefan-Bogdan Marcu*, Robert Parini†, Edmund Champness*, Gareth Conduit†‡
  *Optibrium Ltd., † Intellegens Ltd., ‡Cavendish Laboratory
  Innovations in Pharmaceutical Technology (2020) Autumn/Winter pp. 42-46
• Practical Applications of Deep Learning to Impute Heterogeneous Drug Discovery Data
  Benedict W J Irwin, Julian Levell, Thomas M Whitehead, Matthew D Segall, Gareth J Conduit
  J. Chem. Inf. Model. 2020, 60, 6, 2848–2857
• Predicting Reactivity to Drug Metabolism: Beyond P450s—modelling FMOs and UGTs.
  Öeren M, Walton PJ, Hunt PA, Ponting DJ, Segall MD
  J. Comput.-Aided Mol. Des. (2021) 35(4) pp. 541-555
• Predicting pK_a Using a Combination of Semi-Empirical Quantum Mechanics and Radial Basis Function Methods
  Peter A. Hunt, Layla Hosseini-Gerami, Tomas Chrien, Jeffrey Plante, David John Ponting, and Matthew Segall
  J. Chem. Inf. Model. (2020) 60(6) pp. 2989-2997
• Imputation Versus Prediction: Applications in Machine Learning for Drug Discovery
  Benedict W J Irwin, Samar Mahmoud, Thomas M Whitehead, Gareth J Conduit & Matthew D Segall
  Future Drug Discovery, Vol. 2, No. 2 DOI: 10.4155/fdd-2020-0008
• Capturing and Applying Knowledge to Guide Compound Optimisation
  Segall MD, Mansley TE, Hunt P, Champness EJ
  Drug Discovery Today (2019) 24(5) pp. 1074-1080
• Imputation of Assay Bioactivity Data Using Deep Learning
  Whitehead TM*, Irwin BWJ, Hunt P, Segall MD, Conduit GJ** (*Intellegens, **Cavendish Laboratory)
  J. Chem. Inf. Model. (2019) 59(3) pp. 1197-1204
• AI Advances Healthcare Research
  Segall MD
  Scientific Computing World, December 2018, pp. 21-23
• High-Quality Hits from High-Throughput Screens
  Mansley TE, Hunt PA, Champness EJ and Segall MD
  Genetic Engineering & Biotechnology News, 15 October 2018, pp. 12-14
• WhichP450: a multi-class categorical model to predict the major metabolising CYP450 isoform for a compound
  Hunt PA, Segall MD, Tyzack JD
  J. Comp.-Aided Mol. Des. (2018) 32(4), pp. 537-546
• Discovery Decisions – Collaborating in Data Management
  Segall MD and Leeding C
  European Biopharmaceutical Review, January, 2018, pp 66-69
• Driving Discovery – Predicting P450 Metabolism
  Segall MD
  European Biopharmaceutical Review, October, 2017, pp 20-24
• Synergy between man and machine – the future of drug development
  Segall MD
  Scientific Computing World, 13 April 2017
• Practical applications of Matched Series Analysis: SAR transfer, binding mode suggestion, and data point validation
  Hunt PA, Segall MD, O’ Boyle N*, Sayle R* (*NextMove Software Ltd)
  Future Med Chem. (2017) 9(2), pp 153–168
• Predicting Regioselectivity and Lability of Cytochrome P450 Metabolism Using Quantum Mechanical Simulations
  Tyzack JD, Hunt PA and Segall MD
  J. Chem. Inf. Model. (2016) 56(11), pp 2180–2193
• Avoiding Missed Opportunities by Analyzing the Sensitivity of Our Decisions
  Segall MD, Yusof I, and Champness EJ
  J. Med. Chem, (2016) 59(9) pp. 4267-4277
• When Two are not Enough: Lead optimization beyond matched pairs
  O’Boyle N*, Sayle R*, Segall MD. (* NextMove Software)
  Drug Discovery World, Fall 2015, pp. 55-59
• Breaking free from chemical spreadsheets
  Segall MD, Champness EJ, Leeding C, Chisholm J, Hunt P, Elliott A, Garcia-Matrinez H, Foster NW, Dowling S
  Drug Discovery Today (2015) 20(9) pp. 1093-1103
• The challenges of making decisions using uncertain data
  Segall MD, Champness EJ
  Journal of Computer-Aided Molecular Design (2015) 29(9), pp. 809-816
• Developing Chemistry Apps in the Cloud
  Clark JD*, Leeding C, Champness EJ (* Integrated Chemistry Design Inc.)
  Bio.IT World, February 2015
• Advances in Multi-parameter Optimisation Methods for de Novo Drug Design
  Segall MD
  Expert Opinion in Drug Discovery (2014) 9(7) pp. 803-817
• Alternative variables in drug discovery: promises and challenges
  Abad-Zapatero C*, Champness EJ, Segall MD. (* University of Illinois at Chicago)
  Future Med. Chem. (2014) 6(5) pp. 577-593
• Addressing toxicity risk when designing and selecting compounds in early drug discovery
  Segall MD, Barber C*. (* Lhasa Limited)
  Drug Discovery Today (2014) 19(5), pp. 688-693
• Finding the rules for successful drug optimisation
  Yusof I, Shah F*, Hashimoto T^$, Segall MD, Greene N*. (* Pfizer, ^$ Massachusetts Institute of Technology)
  Drug Discovery Today (2014) 19(5) pp. 680-687
• Using Bioiosteric transformations for compound prediction
  Chisholm J, Segall MD, Champness EJ, Leeding C, Matinez H, Yusof I, Barnard J*, Hayward J* (* Digital Chemistry)
  Drug Discovery & Development, July/August 2013, pp. 14-15
• Finding High-Quality Leads in the Chemical Space
  Champness EJ, Segall MD
  Drug Discovery & Development, June 2013
• Considering the impact drug-like properties have on the chance of success
  Yusof I, Segall MD
  Drug Discovery Today (2013) 18 (13/14) pp. 659-666
• Applying multi-parameter optimisation in drug discovery
  Segall MD
  Express Pharma, January 16-31 2013, pp. 29-33
• Getting predictive with bioisosteres
  Bouley J
  Drug Discovery News (2013) 9(1) pp. 12-14
• An alternative view of drug-like properties
  Segall MD
  Drug Discovery News (2012) 8(12) p 11
• QED: Hopkins Algorithm Ranks the Beauty of Drug Chemistry
  Luchette M
  Bio-IT World, August 21 2012
• On balance
  Segall MD
  Scientific Computing World, Aug/Sept 2012, p. 30
• Applying Multi-Parameter Optimisation in Drug Discovery: Explore Broadly but Focus Quickly on High Quality Chemistry
  Segall MD
  IPI – International Pharmaceutical Industry (2012) 4(1) pp. 26-32
• Can we really do computer-aided drug design?
  Segall MD
  Comput.-Aided Mol. Des. (2012) 26(1) pp. 121-124
• Multi-Parameter Optimization: Identifying high quality compounds with a balance of properties
  Segall MD
  Curr. Pharm. Des. (2012) 18(9) pp. 1292-1310
• Multi-Parameter Optimisation in Drug Discovery
  Segall MD
  Innovations in Pharmaceutical Technology, 2011, 39 pp. 42-46
• Combining Chemists’ Expertise and a Computers Advanced Capabilities to Generate ‘Good’ Ideas
  Segall MD, Campness E, Leeding C, Lilien R*, Mettu R*, Stevens B* (* Cadre Research Labs)
  Drug Discovery World, Fall 2011, pp. 15-23
• Multi-parameter optimization: The delicate balancing act of drug discovery October 2011
  Segall MD
  Drug Discovery News Commentary, October 2011
• Applying Medicinal Chemistry Transformations and Multiparameter Optimization to Guide the Search for High-Quality Leads and Candidates
  Segall MD, Champness E, Leeding C, Lilien R*, Mettu R*, Stevens B* (* Cadre Research Labs)
  J. Chem. Inf. Model. (2011) 51(11) pp. 2967-2976
• The Risks of Subconcious Biases in Drug Discovery Decision-Making
  Segall MD, Chadwick A (Tessella)
  Future Med. Chem. (2011) 3(7), pp. 771-774
• Overcoming Bias in Drug Discovery
  Segall MD, Chadwick A (Tessella)
  Drug Discovery & Development (2011) 14(1) pp. 18-19
• Guiding effective decisions: an interview with Matthew Segall, CEO of Optibrium
  Warr W
  J. Comp. Aided Mol. Design (2011) 25(2) pp. 103-106
• Making Priors a Priority
  Segall MD
  J. Comp. Aided Mol. Design (2010) 24(12) pp. 957-960
• Visual Analyses for Guiding Compound Design and Selection
  Champness E
  Innovations in Pharmaceutical Technology (2010) 34 pp. 32-38
• Overcoming Human Cognitive Bias
  Segall MD
  Pharma Magazine, Sept/Oct 2010, p 66
• Improving R&D with Better Decision Making
  Segall MD, Champness E
  Genetic Engineering & Biotechnology News, September 1 2010 (Vol. 30, No. 15)
• Overcoming psychological barriers to good discovery decisions
  Chadwick A, Segall MD
  Drug Discovery Today (2010) 15 (13/14), pp. 561-569
• Gaussian Processes for Classification: QSAR Modeling of ADMET and Target Activity
  Obrezanova O, Segall MD
  J. Chem. Inf. Model. (2010) 50 (6), pp. 1053-1061
• Beyond Profiling: Using ADMET models to guide decisions.
  Segall MD, Champness E, Obrezanova O, Leeding C
  Chemistry & Biodiversity (2009) 6(11) pp. 2144-2151
• Send for the Software Specialists
  Segall MD
  Pharmaceutical Executive Europe (August 2008) p 18
• Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility
  Obrezanova O, Gola JMR, Champness E, Segall MD
  J. Comp. Aided Mol. Design (2008) 22(6-7) pp. 431-440
• Why is it still Drug Discovery?
  Segall MD
  European Biopharamaceutical Review (May 2008)
• Gaussian processes: a method for automatic QSAR modeling of ADME properties
  Obrezanova O, Csanyi G, Gola JM, Segall MD
  J. Chem. Inf. Model. (2007) 47(5) pp. 1847-57
• ADMET property prediction: The state of the art and current challenges
  Gola JM, Obrezanova O, Champness E, Segall MD
  QSAR Comb. Sci. (2006) 25(12) pp. 1127-29
• Focus on success: using a probabilistic approach to achieve an optimal balance of compound properties in drug discovery
  Segall MD, Beresford AP, Gola JM, Hawksley D, Tarbit MH
  Expert Opin. Drug. Metab. Toxicol. (2006) 2(2) pp.325-37
• The design and preparation of metabolically protected new arylpiperazine 5-HT1A ligands
  Tandon M, O’Donnell MM, Porte A, Vensel D, Yang D, Palma R, Beresford A, Ashwell MA
  Bioorg. Med. Chem. Lett. (2004) 14(7) pp.1709-12
• In silico prediction of ADME properties: are we making progress?
  Beresford AP, Segall M, Tarbit MH
  Curr. Opin. Drug Discov. Devel. (2004) 7(1) pp.36-42
• Modeling aqueous solubility
  Butina D, Gola JM
  J. Chem. Inf. Comput. Sci. (2003) 43(3) pp. 837-41
• The emerging importance of predictive ADME simulation in drug discovery
  Selick HE, Beresford AP, Tarbit MH
  Drug Discovery Today. (2002) 7(2) pp. 109-16
• ADME/PK as part of a rational approach to drug discovery
  Eddershaw PJ, Beresford AP, Bayliss MK
  Drug Discovery Today. (2000) 5(9) pp. 409-414

 

Relevant publications by others

 

• In silico prediction of aqueous solubility
  Dearden JC
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  Jones JP, Mysinger M, Korzekwa KR
  Drug Metab. Dispos. (2002) 30(1) pp. 7-12
• Electronic models for cytochrome P450 oxidations
  Korzekwa KR, Grogan J, DeVito S, Jones JP
  Adv. Exp. Med. Biol. (1996) 387 pp. 361-9
• Predicting the regioselectivity and stereoselectivity of cytochrome P450-mediated reactions: structural models for bioactivation reactions
  Jones JP, Shou M, Korzekwa KR
  Adv. Exp. Med. Biol. (1996) 387 pp. 355-60
• Predicting the rates and regioselectivity of reactions mediated by the P450 superfamily
  Jones JP, Korzekwa KR
  Methods Enzymol. (1996) 272 pp. 326-35