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User-friendly Database Querying for Decision-Making in Drug Discovery

Friday, 15 April 2016 21:05
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Chris Leeding

This poster was presented by Chris Leeding, Ed Champness, Chris Mills*, Andrew Lemon*, Ashley Fenwick$ and Matt Segall at BioIT World Expo and Meeting in April 2016.

* - The Edge Software Consultancy Ltd

$ - Zoetis Inc.

Abstract:

One of the key challenges in drug discovery is ensuring that project leaders and decision makers have access to the latest and most relevant data for their projects. While database architects can skillfully develop systems to search large volumes of complex data at high speed, end users typically don’t have the necessary technical knowledge to set up queries and easily extract relevant results. In this paper, we will describe the development of a graphical tool for user-friendly creation, sharing and execution of structured database queries. This is seamlessly linked with a comprehensive software environment, in which the resulting data can be used to guide effective decisions in the selection and design of high quality compounds for drug discovery and other chemistry fields.

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When Two are not Enough: Lead optimization beyond matched pairs

Wednesday, 21 October 2015 12:37
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Matt Segall

This article, co-authored with Noel O'Boyle and Roger Sayle of NextMove Software, was published in Drug Discovery World, Fall 2015 and discusses how matched series analysis goes beyond matched molecular pairs to identify more relevant chemical substitutions with which to improve target potency.

Example suggestions from Matsy

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The Challenges of Making Decisions Using Uncertain Data

Tuesday, 07 April 2015 16:12
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Matt Segall

We've recently submitted this paper on the challenges of using uncertain experimental data to make confident decisions on the selection of compounds. We consider how the uncertainties in data can be translated into probabilities, when choosing between compounds or making selections based on property criteria, to avoid making inappropriate decisions, wasting effort and missing valuable opportunities.

Uncertainties in data
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Improving the Plausibility of Success in Drug Discovery with the Use of Inefficient Metrics

Friday, 27 March 2015 13:46
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Michael Shultz
Dr Mike Shultz,Novartis, gave this presentation at the "Guiding Optimal Compound Design and Development Symposium" held in Cambridge, MA, USA on 19 March 2015.

Shultz talk

You can download this presentation as a PDF.


Development of a Drug Discovery Simulation Laboratory Exercise in the Pharmaceutical Sciences Graduate Program Curriculum

Friday, 27 March 2015 13:31
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Chase Smith
Dr Chase Smith, Massachusetts College of Pharmacy and Health Sciences, gave this presentation at the "Guiding Optimal Compound Design and Development Symposium" held in Cambridge, MA, USA on 19 March 2015.

Abstract
The development of a 5-week long laboratory exercise that simulates an early stage drug discovery program and hit-to-lead optimization for use in an introductory course in the Pharmaceutical Sciences program at MCPHS University (Worcester/Manchester) will be discussed. Using the ADME QSAR module of the Stardrop™ software package, the students were introduced to triaging primary antimalarial screening data, evaluating calculated drug like properties and finally the selection of a hit series. The students then embarked on a hit-to-lead optimization through a decision making process involving improvement of the calculated drug like properties, improvement of metabolic stability using the web based SMARTCyp© algorithms and the calculation of analog activity using a QSAR model from the mobile application SAR Table©.

You can download this presentation as a PDF.


Knowledge-based Small Molecule and Antibody Design Strategies

Monday, 24 March 2014 15:53
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Mark Swindells
Mark Swindells gave this presentation at the International Symposium on Compound Design Technologies held in Tokyo and Osaka, Japan on 19 and 20 March 2014.

Abstract
Some of the most productive design methods of the past 30 years have been knowledge-based. The best known is surely homology modelling, now a standard tool in most pharmaceutical and agrochemical companies.

The rapid increase in DNA, protein sequence and 3D structure data, so called big data, as well as newer initiatives to put small molecule and bio-assay data into the public domain, have opened up a wealth of new predictive opportunities, but complex commercial systems cannot keep pace with these developments.

Based on our translational research approach, we take research software from universities and young biotech and license their technology-advanced software directly customers or work with clients to tailor to their specific research efforts.

The presentation will cover:

  • knowledge-based fragments
  • evolution-seeded drug design
  • applications of the Chembl knowledge-base for drug discovery
  • antibody humanness score for biomolecule ADME/Tox assessment.

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    Development of a Structure Generator to Explore a Target Area on Chemical Spaces

    Monday, 24 March 2014 15:37
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    Matt Segall
    Dr Kimita Funatsu gave this presentation at the International Symposium on Compound Design Technologies held in Tokyo and Osaka, Japan on 19 and 20 March 2014.

    Abstract
    On the first stage of development of new drugs, various lead compounds with high activity are required. To design such compounds, we focus on chemical spaces defined by structural descriptors. New compounds close to areas around which highly active compounds exist will show the same degree of activity. Therefore we have been developing a new system of structure generation for searching a target area in chemical spaces. First, highly active compounds are manually selected as initial seeds. Then, those seeds are entered to our generator and structures slightly different from the structures of the seeds are generated and pooled. Next seeds are selected from the new structure pool with the scores based on distance from target on the map. In this study, we used GVK data of ligand-binding affinity to verify the advantage of our generator. Visualization of the chemical space and structure generation were performed, and then the outputs were compared with test data. As a result, our generator could produce many structures similar to the test data, which exist near the target area. This result shows that exploration of the target area on the chemical space was performed.

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    ChEMBL an Open Data Resource of Medicinal Chemistry and Patent Data

    Monday, 24 March 2014 15:15
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    John Overington
    John Overington gave this presentation at the International Symposium on Compound Design Technologies held in Tokyo and Osaka, Japan on 19 and 20 March 2014.

    Abstract
    The link between biological and chemical worlds is of critical importance in many fields, not least that of healthcare and chemical safety assessment. A major focus in the integrative understanding of biology are genes/proteins and the networks and pathways describing their interactions and functions; similarly, within chemistry there is much interest in efficiently identifying drug-like, cell-penetrant compounds that specifically interact with and modulate these targets. The number of genes of interest is of the range of 105 to 106, which is modest with respect to plausible drug-like chemical space – 1020 to 1060. We have built a public database linking chemical structures (~10^^6) to molecular targets (~10^^4), covering molecular interactions and pharmacological activities and Absorption, Distribution, Metabolism and Excretion (ADME) properties – ChEMBL (http://www.ebi.ac.uk/chembl) in an attempt to map the general features of molecular properties and features important for both small molecule and protein targets in drug discovery. We have then used this empirical kernel of data to extend analysis across the human genome, and to large virtual databases of compound structures. Recently we have added large scale text mined chemical structures from patents to our resources (http://www.surechembl.org).

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    Protein-Protein Interactions and Inhibitors

    Thursday, 29 November 2012 08:48
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    Alan Naylor

    Alan Naylor led a stimulating discussion on drug discovery targeting inhibition of protein-protein interactions at our 2012 Drug Discovery Consultants' Day. This raised many important questions including the potential need to consider compounds outside of 'Lipinski-compliant' space, while noting that some traditional small molecule inhibitors of PPIs, with good oral properties, have recently been discovered.

    For details of the questions addressed, you can download a copy of Alan's slides as a PDF.


    Preprint: Considering the Impact of ‘Drug-like’ Properties on the Chance of Success

    Tuesday, 23 October 2012 16:23
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    Matt Segall

    This paper was published in Drug Discovery Today 18(13-14), pp. 659-666 (DOI 10.1016/j.drudis.2013.02.008). In the paper we review the strengths and weaknesses of different definitions of 'drug-like' properties and measures of 'drug-likeness.' We propose an alternative metric the Relative Drug Likelihood (RDL) that identifies the properties with the greatest impact on a compound's likelihood of success for a drug discovery objective.

     

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    Relative Drug Likelihood: Going Beyond Drug-Likeness

    Tuesday, 04 September 2012 00:00
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    Matt Segall

    Matt gave this presentation at the ACS National Fall Meeting 2012.

    Abstract

    Many approaches have been used to characterise compounds as 'drug-like' or not based on the similarity of simple properties of a compound, e.g. molecular weight, to those of known drugs. However, having a 'similar' property to known drugs does not necessarily mean that a compound is more likely to become a drug. We propose an extension to 'drug likeness' approaches, based on an assertion that a desirable value of a property is one that increases the probability of identifying a drug. Using Bayesian approaches we can estimate the relative likelihood of a compound being a drug by comparing the distributions of properties for drugs and non-drugs. We will demonstrate that this offers improved performance for the identification of drugs and provides insights into which characteristics provide the greatest discrimination between successful drugs and unsuccessful drug discovery compounds.

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    Can We Really Do Computer-aided Drug Design?

    Tuesday, 04 September 2012 00:00
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    Matt Segall

    Matt gave this presentation at the ACS National Spring Meeting 2012.

    Abstract

    We will explore the accuracy of current computational methods in drug discovery, including 2D and 3D QSAR, docking, pharmacophore, molecular dynamics and quantum mechanical approaches. Based on this, we will address the question of whether we are truly operating in a drug design paradigm. We will compare this with the application of computational methods to the discovery of new drugs. From this alternative perspective, computational methods can add significant value to guide decisions about which chemistry to pursue and which can be rejected with confidence; focussing resources on the chemistry that is most likely to succeed, while avoiding missed opportunities. This is particularly important in the multi-parameter optimisation of high quality drug candidates that require a balance of many properties to succeed downstream.

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    Overcoming Psychological Barriers to Good Decision-making in Drug Discovery

    Saturday, 24 March 2012 00:00
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    Matt Segall

    Matt gave this presentation at the Keystone Symposium, Addressing the Challenges of Drug Discovery 2011.

    Abstract

    Better individual and team decision-making could enhance drug discovery performance. Reproducible biases effecting human decision making, known as cognitive biases, have been understood by psychologists for at least half a century. These threaten objectivity and balance and so are credible causes for continuing unpleasant surprises in late development and high operating costs of compound discovery. We will consider the risks to R&D decision-making for four of the most common and insidious cognitive biases: confirmation bias, poor calibration, availability bias and an excess focus on certainty. We will suggest approaches for overcoming these, such as strategies adapted from evidence-based medicine and computational tools that seek to guide the decision making process. These include methods for multi-parameter optimisation that encourage objective consideration of all of the available information and explicit consideration of the impact of uncertainty in drug discovery.

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    Making Priors a Priority

    Friday, 29 April 2011 00:00
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    Matt Segall

    Matt gave this presentation at the ACS Spring meeting 2011 in Anaheim.

    Abstract

    When we build a predictive model of a drug property we rigorously assess its predictive accuracy, but we are rarely able to address the most important question, “How useful will the model be in making a decision in a practical context?” To answer this requires an understanding of the prior probability distribution and hence prevalence of negative outcomes due to the property. We will illustrate the importance of the prior to assess the utility of a model to select or eliminate compounds for further investigation. A better understanding of the prior probabilities of adverse events due to key factors will improve our ability to make good decisions in drug discovery, finding higher quality molecules more efficiently. As the data necessary to estimate these priors does not include proprietary compound structures, this presents an opportunity for collaboration to improve the basis for good decision-making for all.

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    Making Priors a Priority

    Friday, 29 October 2010 07:36
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    Matt Segall

    This article was published in J. Comp. Aided Mol. Design (DOI 10.1007/s10822-010-9388-7) and discusses a critical issue that the community needs to address address in order to use the predictive models that we build to the greatest effect.

    Abstract

    When we build a predictive model of a drug property we rigorously assess its predictive accuracy, but we are rarely able to address the most important question, “How useful will the model be in making a decision in a practical context?” To answer this requires an understanding of the prior probability distribution (“the prior”) and hence prevalence of negative outcomes due to the property being assessed. In this perspective, we illustrate the importance of the prior to assess the utility of a model in different contexts: to select or eliminate compounds, to prioritise compounds for further investigation using more expensive screens, or to combine models for different properties to select compounds with a balance of properties. In all three contexts, a better understanding of the prior probabilities of adverse events due to key factors will improve our ability to make good decisions in drug discovery, finding higher quality molecules more efficiently.

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    Issues in the Interpretation, Understanding, and Use of Drug Discovery Data

    Thursday, 30 September 2010 00:00
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    Terry Stouch

    Dr Terry Stouch gave this presentation as part of the first StarDrop User Group Meeting and Workshop at the ACS Fall meeting 2010 in Boston.

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    Poster: Maximising compound value by making good decisions:

    Monday, 27 September 2010 00:00
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    Matt Segall

    Matt presented this poster at MipTec in September 2010.

    Abstract:

    People are notoriously poor at making good decisions based on complex, uncertain data when there is a lot at stake. In drug discovery, poor decisions can mean wasting effort in synthesizing and testing compounds that fail or throwing out perfectly good compounds in error, reducing the opportunities to find new, valuable therapies. However, making good decisions in this context is challenging for several reasons: the need to balance multiple, often conflicting criteria for a successful drug; the abundance of data available on many properties; and the uncertainty in the relevance and accuracy of the available data, particularly in early discovery.

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    Medicinal chemists are people too: And that's a problem

    Tuesday, 31 August 2010 00:00
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    Mike Moyer

    Dr Mikel Moyer gave this presentation as part of the first StarDrop User Group Meeting and Workshop at the ACS Fall meeting 2010 in Boston.

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    Overcoming psychological barriers to good discovery decisions

    Wednesday, 07 July 2010 21:36
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    Matt Segall

    This paper was published by Andrew Chadwick and Matthew Segall in Drug Discovery Today, 2010, 15 (13/14), pp. 561-569.

    Abstract

    Better individual and team decision-making should enhance R&D performance.  Reproducible biases affecting human decision making, known as cognitive biases, are well understood by psychologists. These threaten objectivity and balance and so are credible causes for continuing unpleasant surprises in Development, and high operating costs.  For four of the most common and insidious cognitive biases, we consider the risks to R&D decision-making and contrast current practice with use of evidence-based medicine by healthcare practitioners.  Feedback on problem solving performance in simulated environments could be one of the simplest ways to help teams improve their selection of compounds and effective screening sequences. Computational tools that encourage objective consideration of all of the available information may also contribute.

    The published article can be accessed at http://dx.doi.org/10.1016/j.drudis.2010.05.007 or you can download a preprint free of charge.


    A rational approach to risk reduction

    Friday, 25 June 2010 00:00
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    Andrew Chadwick

    Dr Andrew Chadwick, Consultant (Life Sciences and Healthcare) at Tessella gave this presentation on "Rational Approach to Risk Reduction: What can discovery screening planners learn from volcanos and dust detection?", on Wednesday 6th June 2010, covering the following topics:

    • The purpose of screening and the principles of value-adding screening plans
    • The importance of finding the right, tailored screening plan for each project
    • Risk perception and the pitfalls of cognitive biases for decision-making
    • Metrics that should guide the choice, sequence, combinations and cut-offs for tests
    • Ways of balancing the important factors (downstream consequences of error, cost and time for screening, and predictive performance)
    • The need to overcome the challenge of uncertain inputs
    • What is the right balance between exploitation and exploration of product options and technology performance?
    • Effective approaches to supporting learning and continuous improvement

    Andrew gave this presentation at the 11th Annual Drug Discovery Leaders Summit, June 9-10th, 2010, Montreux, Switzerland.

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    Visual analyses for guiding compound selection and design

    Friday, 26 March 2010 14:56
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    Ed Champness

    In this presentation Ed Champness considers the decision-making challenges faced by drug discovery scientists and presents some visual analyses that can be used to help answer some of the common questions that are asked.

    Ed gave this presentation at the ACS Spring meeting 2010 in San Francisco.

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    Article: Why is it still drug discovery?

    Thursday, 15 October 2009 10:07
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    Matt Segall

    Matthew Segall (while still in the ADMET Division, BioFocus DPI), explored the balance between luck and judgement in drug discovery. As Matt put it "The vision of an in silico design process for drug molecules is certainly attractive, so
    why has this goal yet to be realised, despite an enormous effort over the past 10 years?"

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    Xemistry Core Competencies

    Tuesday, 13 October 2009 14:01
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    Wolf Ihlenfeldt

    In this presentation Wolf Ihlenfeldt describes some of Xemistry's core competencies.

    Xemistry is one of Optibrium's partners, providing the CACTVS toolkit which is used to manage the underlying computational chemistry behind StarDrop.

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    The journey from Drug Discovery to Drug Design: How far have we travelled?

    Tuesday, 13 October 2009 13:17
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    Matt Segall

    In this presentation, Matt Segall talks about the differences between "design" and "discovery" and considers two different analogies for the drug design process - the development of the Boeing 777 and card counting in blackjack. Using the latter, Matt discusses how we can appropriately use uncertain information to guide decisions amd how we can interpret in silico data to guide compound design. Finally, Matt gives an illustrative example of putting this theory into practise in a case study during which the aim was to focus resources in a hit-to-lead/lead optimisation study.

    This presentation was given at the SMI In Silico ADMET conference in 2007.

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