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Latest Publications & Presentations


Practical Applications of Deep Learning to Imputation of Drug Discovery Data

Wednesday, 04 September 2019 15:03

Presented by Ben Irwin, on 28 August 2019 at the ACS National Meeting and Exposition in San Diego, USA

Presentation Overview

Problems with pharma data − Define solutions to these problems

Alchemite : A novel deep learning algorithm for imputation − Imputation = Filling in the blanks

Walkthrough deep learning imputation on a real project − Early screen data − Validation − Late stage models − Comparison with standard QSAR methods

Larger applications and future prospects

 

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Mechanism and Prediction of UGT Metabolism

Wednesday, 04 September 2019 09:10

Presented by Mario Oeren, on 27 August 2019 at the ACS National Meeting and Expo in San Diego, USA

Presentation Overview

UGT metabolism − A short overview

Mechanistic studiesAb initio − Semi empirical

QSAR models − Results from mechanistic studies − Steric and orientation descriptors

Conclusions

 

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Predicting pKa Using a Combination of Quantum and Machine Learning Methods

Thursday, 01 August 2019 14:22

This poster was presented at the 12th International ISSX Meeting, 28-31 July 2019

Peter Hunt, Layla Hosseini-Gerami, Tomas Chrien, Matthew Segall

The dissociation of a proton from a heteroatom has a significant impact on the charge distribution and interactions of a molecule. These influence many important molecular properties, including binding to target and off-target proteins, absorption, distribution, metabolism and excretion (ADME) and pharmacokinetic (PK) properties such as solubility, tissue or cellular distribution and permeability. Therefore, the ability to predict the propensity of a molecule to lose or gain a proton in water is crucial for the development of new chemical entities with desirable PK, ADME and binding properties.

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Predicting Routes, Sites and Products of Drug Metabolism

Wednesday, 31 July 2019 09:10

Presented by Matt Segall at 12th International ISSX Meeting 2019, Oregon, USA

Presentation Overview

  • Approaches to predicting metabolism − Empirical vs mechanistic
  • Predicting P450 metabolism − P450 regioselectivity − WhichP450
  • Beyond P450s − Flavin containing monooxygenases (FMO) − UDP glucuronosyltrasfreases (UGT)
  • Conclusions

     

    You can download the presentation slides as a PDF

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    A Single Deep Learning Model for Confident Imputation of Heterogeneous Drug Discovery Endpoints

    Wednesday, 24 July 2019 10:43

    This poster was presented at the Gordon Research Conference, Integrating Big Data and Macromolecular Protein Structures into Small Molecule Design; 14-19 July 2019

    Benedict Irwin, Julian Levell, Thomas Whitehead, Matthew Segall, Gareth Conduit

    We have previously described a novel deep learning method for data imputation, Alchemite™ (Whitehead et al J Chem Inf Model (2019) 59 pp 1197-1204). This accepts both molecular descriptors and sparse experimental data as inputs, to exploit the correlations between experimentally measured endpoints, as well as structure activity relationships (SAR). It has been demonstrated to outperform quantitative SAR (QSAR) models, including multi-target deep learning methods, on a challenging benchmark data set of compound bioactivities. Here we will describe the application and validation of this method on drug discovery data covering two projects and diverse endpoints, including activities in both biochemical and cellular assays and absorption, distribution, metabolism and elimination (ADME) endpoints.

    You can download the poster as a PDF

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    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.

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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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