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Latest Publications & Presentations


N- and S-Oxidation Model of the Flavin-containing Monooxygenases

Wednesday, 03 July 2019 14:44

This poster was presented at the Eighth Joint Sheffield Conference on Chemoinformatics; 17-19 June 2019

Peter Walton, Mario Öeren, Peter Hunt, Matthew Segall

Existing computational models of drug metabolism are heavily focused on predicting oxidation by cytochrome P450 (CYP) enzymes, because of their importance in phase I drug metabolism, reactive metabolite formation, and drug-drug interactions. Due, in part, to the success of these models, new drug candidates are typically well-optimised with respect to CYP metabolism However, novel metabolites are observed due to other, less-studied, enzyme families such as the flavin containing monooxygenases (FMOs) are found in multiple tissues, including the liver, and have five active isoforms (FMO 1-5). In common with CYPs, FMOs are responsible for phase I, oxidative metabolism and catalyse a variety of reaction types, including N- and S-oxidation, demethylation, desulphuration and Bayer-Villiger oxidation.

The objective of this study was to elucidate the reaction mechanism of FMO-mediated oxidation to inform the development of models to predict the metabolism of novel substrates.

You can download the poster as a PDF

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Drug Discovery Today: Capturing and Applying Knowledge to Guide Compound Optimisation

Wednesday, 19 June 2019 10:42

This article was published in Drug Discovery Today; V24 No.5 May 2019

Matthew Segall, Tamsin Mansley, Peter Hunt, Edmund Champness

Successful drug discovery requires knowledge and experience across many disciplines, and no current 'artificial intelligence' (AI) method can replace expert scientists. However, computers can recall more information than any individual or team and facilitate the transfer of knowledge across disciplines. Here, we discuss how knowledge relating to chemistry and the biological and physicochemical properties required for a successful compound can be captured. Furthermore, we illustrate how, by combining and applying this knowledge computationally, a broader range of optimisation strategies can be rigorously explored, and the results presented in an intuitive way for consideration by the experts.

You can download the Drug Discovery Today article as a PDF

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Imputing Compound Activities Based on Sparse and Noisy Data

Monday, 08 April 2019 10:35

Presented by Matt Segall at ACS 2019, Orlando, Florida

Thomas Whitehead†, Matthew Segall*, Benedict Irwin*, Peter Hunt*, Gareth Conduit† (*Optibrium Ltd., †Intellegens)

Presentation

New results show the increase in accuracy by focussing on the most confident results as a reduction in RMSE, instead of increase in R^2, following feedback from earlier presentations; and we also illustrate the application of the Alchemite™ model to virtual compounds, i.e. based only on molecular descriptors. This shows it is equivalent in performance to a conventional multi-target DNN, but also retains the ability to focus the most accurate results based on the confidence in the model predictions.

Learn more about Alchemite, a novel deep learning algorithm. Unlike many deep learning methods, this approach is capable of being trained using sparse and variable input data, typical of those available in drug discovery. This enables Alchemite to learn from correlations between experimental endpoints, as well as between molecular descriptors and protein activities, to more accurately impute the missing activities.

You can download the presentation slides as a PDF

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N- and S-Oxidation Model of the Flavin-containing MonoOxygenases

Wednesday, 27 March 2019 15:32

At the American Chemical Society National Meeting and Expo in Orlando, Florida; Peter Walton presented his research entitled ‘N- and S-Oxidation model of the Flavin-containing Monooxygenases’. The presentation covers the work he and his colleagues have undertaken to determine how the Flavin-containing MonoOxygenase group of enzymes work to metabolise compounds. Extensive computational tests support their theory concerning the reaction mechanism and the results can be used to predict the likely metabolites of a wide variety of drugs.

You can download the slides here as a PDF

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New UK Collaborative Uses AI to Predict Missing Data Points in Compound Data

Wednesday, 13 March 2019 16:52

A new UK collaboration focuses on taking sparse data – data where a significant amount of points are missing from the complete sets – or “noisy” data – data where a significant amount of variables could contribute to issues and changes in results – and making predictive models that fill in missing points with degrees of certainty and without having to undergo costly experimentation.

You can download the article here as a PDF

You can link to Rx Data here

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SBDD From a Diversified NP-Inspired Chemical Space

Wednesday, 13 March 2019 11:44

At the 2019 Streamlining Drug Discovery Symposium in Frankfurt, Didier Roche from Edelris presented 'SBDD From a Diversified NP-Inspired Chemical Space'.

You can download the presentation slides here as a PDF

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Turning High Quality Data Into Actionable Insights

Friday, 22 February 2019 14:33

At the 2019 Streamlining Drug Discovery Symposium in Frankfurt, Rosalind Sankey (Elsevier) presented 'Helping Medicinal Chemists Identify New Opportunities during Lead ID and Optimisation - Turning High Quality Data into Actionable Insights'.

You can download the presentation slides here as a PDF

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Capturing and Applying Knowledge to Guide Compound Optimisation

Friday, 22 February 2019 13:23

This article was published in the online edition of Drug Discovery Today; February 2019

Matthew Segall, Tamsin Mansley, Peter Hunt, Edmund Champness

Abstract

Successful drug discovery requires knowledge and experience across many disciplines and no current ‘artificial intelligence’ method can replace expert scientists. However, computers can recall much more information than any individual or team and facilitate transfer of knowledge across disciplines. We’ll discuss how knowledge relating to chemistry and the biological and physicochemical properties required for a successful compound can be captured. Furthermore, we’ll illustrate how, by combining and applying this knowledge computationally, a much broader range of optimisation strategies can be rigorously explored, and the results presented in an intuitive way for consideration by the experts.

You can download the article as a PDF

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Are You Additive? SAR Approaches for Small Molecule Drug Discovery

Thursday, 21 February 2019 11:10

At the 2019 Streamlining Drug Discovery Symposium in Frankfurt, Christian Kramer gave this insightful presentation 'Are You Additive? SAR Approaches for Small Molecule Drug Discovery'.

Optibrium Community Members can download this presentation

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Imputation of Assay Bioactivity Data using Deep Learning

Thursday, 14 February 2019 10:24

This paper was printed in the Journal of Chemical Information and Modeling.

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

Abstract

We describe a novel deep learning neural network method and its application to impute assay pIC50 values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity data as input, typical of that found in public and commercial databases, enabling it to learn directly from correlations between activities measured in different assays.

In two case studies on public domain data sets we show that the neural network method outperforms traditional quantitative structure-activity relationship (QSAR) models and other leading approaches. Furthermore, by focussing on only the most confident predictions the accuracy is increased to R2 > 0.9 using our method, as compared to R2 = 0.44 when reporting all predictions.

You can download this paper as a PDF

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AI Advances Healthcare Research

Monday, 10 December 2018 14:23

Harnessing AI for drug discovery applications will significantly speed the identification of promising drug candidates, believes Matt Segall, CEO at Optibrium. The UK-based firm, together with partners Intellegens and Medicines Discovery Catapult, recently received a grant from Innovate UK to help fund a £1 million project focussed on combining Optibrium’s existing StarDrop software for small molecule design, optimisation and data analysis, and Intellegen’s deep learning platform Alchemite.

The aim is to develop a novel, deep learning AI-based method for predicting the ADMET (absorbtion, distribution, metabolism, excretion and toxicity) properties of new drugs candidates. Ultimately, the platform could help to guide the selection and design of more effective, safer compounds earlier in the discovery process...

 

You can link to the Scientific Computing World article here

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Managing Internal and External Chemistry for Efficient Drug Delivery

Friday, 30 November 2018 13:50

At the 2018 Streamlining Drug Discovery Symposium, USA, David Hollinshead (Elixir Software) and Andrew Griffin (Praxis Precision Medicines) presented Managing Internal and External Chemistry for Efficient Drug Delivery.

You can download the slides here as a PDF

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Electrostatic Complementarity as a New Approach to Visualize and Predict Activity

Thursday, 29 November 2018 15:54

At the 2018 Streamlining Drug Discovery Symposium, Sylvie Sciammetta presented Electrostatic Complementarity as a New Approach to Visualize and Predict Activity.

You can download her slides here as a PDF

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Medicinal Chemist’s Relationship with Additivity

Wednesday, 28 November 2018 15:12

Medicinal Chemist’s Relationship with Additivity: Are we Taking the Fundamentals for Granted?

At the San Francisco, Streamlining Drug Discovery Symposium 2018, J. Guy Breitenbucher from UCSF gave this in-depth presentation.

You can download the presentation slides here as a PDF

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Bigfoot, the Loch Ness Monster, and Halogen Bonds

Thursday, 22 November 2018 10:37

At the 2018 Streamlining Drug Discovery Symposium in San Diego, David Lawson treated us to this illuminating presentation entitled Bigfoot, the Loch Ness Monster, and Halogen Bonds: Separating Rumors from Reality in Molecular Design.

You can download his slides here as a PDF

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A Novel Scoring Profile for the Design of Antibacterials Active Against Gram-Negative Bacteria

Friday, 16 November 2018 12:15

At the 2nd SCI/RSC Symposium on Antimicrobial Drug Discovery, 12-13 November 2018, Bailey Montefiore, Optibrium - Franca Klingler, BioSolveIT - Nicholas Foster, Optibrium presented 'A Novel Scoring Profile for the Design of Antibacterials Active Against Gram-Negative Bacteria'.

Introduction

The increasing occurrence of multidrug-resistant bacteria is one of the major global threats to human health. Design of new antibacterials is challenging because new compound classes often do not possess the unique physicochemical properties required to penetrate the gram-negative cell wall. It is accepted that the physicochemical properties of many drugs are similar and attempts have been made to characterise these ‘drug-like’ properties, such as Lipinski’s ‘rule of five’ for orally dosed drugs. However, antibiotics are a known exception to these rules. We compared antibiotics active against gram-negative bacteria with other classes of drug and compounds considered in medicinal chemistry projects to determine criteria for selection of compounds with a higher chance of success as a gram-negative antibacterial. These criteria are based on calculated properties, so can help to guide the design and selection of compounds in discovery projects.

You can download the poster presentation as a PDF

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Imputation of Protein Activity Data Using Deep Learning

Wednesday, 14 November 2018 15:44

At the US Symposia, Streamlining Drug Discovery 2018 in Cambridge MA, Matthew Segall from Optibrium and Tom Whitehead from Intellegens presented Imputation of Protein Activity Data Using Deep Learning.

You can download the complete slide presentation as a PDF

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WaterSwap to Assess Target Druggability

Monday, 12 November 2018 15:07

At the 2018 Streamlining Drug Discovery Symposium in San Diego and San Francisco, Adam Kallel gave an insightful presentation on WaterSwap to Assess Target Druggability.

You can download his slides as a PDF

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Using AI to Improve the Safety of New Drug Candidates

Thursday, 08 November 2018 08:37

On 18 October 2018, at the Streamlining Drug Discovery Symposium in Cambridge MA, Nigel Greene gave this fascinating presentation on Using AI to Improve the Safety of New Drug Candidates.

You can download his slides as a PDF

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Two Decades under the Influence of the Rule of Five and the Changing Properties of Approved Oral Drugs

Monday, 29 October 2018 09:06

This paper appeared in Journal of Medicinal Chemistry, September 13, 2018.

Abstract

Two decades have passed since the rule of five ushered in the concept of “drug-like” properties. Attempts to quantify, correlate, and categorize molecules based on Ro5 parameters evolved into the introduction of efficiency metrics with far reaching consequences in decision making by industry leaders and scientists seeking to discover new medicines. Examination of oral drug parameters approved before and after the original Ro5 analysis demonstrates that some parameters such as clogP and HBD remained constant while the cutoffs for parameters such as molecular weight and HBA have increased substantially over the past 20 years. The time dependent increase in the molecular weight of oral drugs during the past 20 years provides compelling evidence to disprove the hypothesis that molecular weight is a “drug-like” property. This analysis does not validate parameters that have not changed as being “drug-like” but instead calls into question the entire hypothesis that “drug-like” properties exist.

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