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Latest Publications & Presentations


Combining quantum and QSAR methods for prediction of acid dissociation constants

Tuesday, 09 May 2017 10:52

Layla Hosseini-Gerami1,2, Rasmus Leth1, Peter Hunt1, Matthew Segall1.

1 Optibrium Limited, Cambridge, UK
2 University of Leeds, Leeds, UK

This presentation was given at the ACS Spring 2017 National Meeting & Exposition held in San Diego, USA.

Abstract

The equilibrium between charged and neutral species has an important impact on a wide variety of properties relevant to pharmaceutical and agrochemical compound design and development. The accurate prediction of the pKa of any centre in a molecule would be of value in all stages of research from synthetic planning to biological activity and on to formulation and delivery. We will present our efforts to model the pKa of any hydrogen in a compound, based on ab initio density functional theory, semi empirical Hamiltonians and empirical quantitative structure activity relationships. We will compare these approaches and illustrate how they can be combined to balance speed, accuracy and transferability.
Prediction of acid dissociation constants

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Supporting Compound Optimisation in Not-for-Profit and Academic Research

Tuesday, 09 May 2017 10:41

Matthew Segall1, Tamsin Mansley1, Peter Hunt1, Kelly Chibale2, Tanya Paquet2, James Duffy3
1 Optibrium Limited, Cambridge, UK
2 University of Cape Town, Cape Town, South Africa
3 Medicines for Malaria Ventures, Geneva, Switzerland

This presentation was given at the ACS Spring 2017 National Meeting & Exposition held in San Diego, USA.

Abstract

The not-for-profit and academic sectors have become important sources of novel drug candidates, particularly for neglected and developing world diseases or niche indications. Drug discovery projects in these sectors are often conducted on a collaborative basis, pooling resources and experience across multiple research groups and using contract research organisations as appropriate. Several software platforms have been developed to facilitate the secure sharing of data across organisations, but in this talk we will discuss software approaches that focus on using these data to guide decisions regarding the selection and design of high quality compounds.

Malaria Infection Cycle

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Poster: 3D Modelling for the Masses: A universal Interface for Easy Access to Expertly Prepared 3D Models

Tuesday, 09 May 2017 10:34

This poster by Fayzan Ahmed, Tamsin Mansley, Chris Leeding, Edmund Champness, Peter Hunt & Matthew Segall was presented at the ACS Spring 2017 National Meeting & Exposition held in San Diego, USA.

Abstract

To prioritise new compound ideas efficiently in a discovery project, compound structures and all their associated data must be brought together to guide optimisation of high quality, potent compounds. Predictions based on 2-dimensional (2D) and 3-dimensional (3D) structures are typically generated in an arsenal of modelling tools, often restricted to expert users due to their complexity. This means that computational scientists spend time running routine calculations, distracting them from the more scientifically challenging tasks, such as preparation and validation of protein docking models, where their expertise is most valuable. The delay in feedback to the medicinal chemists also forms a barrier to rigorous assessment of new compound ideas prior to synthesis.

We will present an integrated, generic Pose Generation Interface which links expertly prepared docking and 3D alignment models, from a variety of applications, with a comprehensive environment for data visualisation, analysis and predictive modelling. Poses and scores are automatically retrieved for visualisation and analysis, alongside predictions from models of absorption, distribution, metabolism, excretion and toxicity (ADMET). This enables medicinal chemists to evaluate multiple iterations of designs on-the-fly and quickly understand structure-activity relationships, identify potential liabilities and design new compounds with the highest chance of success. While their colleagues use the 3D models they have built and validated, the computational scientists can focus on the next round of expert computational design and model building. This approach supports collaboration between computational and synthetic chemists, helping to share the results of 3D modelling studies with all decision makers.
Pose Generation Interface

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Confidently Targeting High Quality Hits from High-Throughput Screening

Tuesday, 09 May 2017 10:25

Matt Segall gave this presentation at the ACS Spring 2017 National Meeting & Exposition held in San Diego, USA.

Abstract

When analysing the results from a high throughput screening (HTS) campaign the goal is to identify diverse hit series with high activity, structure-activity relationships (SAR) that indicate the opportunity for further optimisation and good ‘lead like’ properties. The common practise is to apply filters to these large datasets, for example an activity threshold or simple properties such as molecular weight, logP, numbers of hydrogen bond donors and acceptors or the presence of substructures that may indicate non-specific binding. However, this process draws artificially harsh distinctions between compounds, given the inherent variability in HTS data and the low correlation between simple properties and the ultimate in vivo disposition of a compound. This leads to selection of ‘false positives’, i.e. active compounds that are not good starting points for further optimisation and ‘false negatives’, i.e. potentially good compounds that have been inappropriately rejected. We will illustrate how a true multi-parameter approach enables appropriate weight to be given to these data to confidently identify high quality, potent hits while avoiding missed opportunities.

Mapping this information across the chemical diversity of the compounds explored in an HTS campaign, by clustering or visualisation of a ‘chemical space’, helps to find ‘hot spots’ representing high quality series of compounds for further investigation while also considering diverse chemistries to provide potential backup series. Finally, exploring the SAR within these series then helps to identify further opportunities for optimisation. We will show how this can all be achieved in a high visual and intuitive way, to move quickly and confidently from initial HTS hits to high quality lead series.
HTS Network

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Data visualization: Saying it all in a bite-sized chunk

Tuesday, 09 May 2017 10:19

Ed Champness gave this presentation at the ACS Spring 2017 National Meeting & Exposition held in San Diego, USA.

Abstract

We often use the term “data visualisation” to refer to the creation of plots that enable us to represent tables of numbers in an easily-digestible form and yet we use many visual approaches for representing compounds, targets, assay results, model predictions, etc. The combination of these varied representations, containing many dimensions of data, presents us with an interesting challenge as we seek to understand what they are telling us and then convey our conclusions to others. Something to keep in mind is that while we may keep our social and professional lives separate, the boom in social media is a pretty clear indicator about the way we like to consume information, and there’s no reason this should change just because we walk through the office door. While it is easy to simplify the overall picture by simply trimming off the detail, how can we ensure that the “bite-sized visualisation” we ultimately create is an appropriate reflection of the underlying information such that it won’t inappropriately bias our decisions? We will illustrate some of the ways that we can achieve this and discuss visual methods to guide our decisions in drug discovery.

You can download this presentation as a PDF.

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Poster: BSEP, MRP, AND DILI... Just a bad hand at Scrabble?

Tuesday, 31 January 2017 17:46

This poster was presented by Nick Foster at the 11th International ISSX Meeting, Busan, Korea in June 2016

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Crystal structures, binding interactions, and ADME evaluation of brain penetrant N-substituted indazole-5-carboxamides as subnanomolar, selective monoamine oxidase B and dual MAO-A/B inhibitors

Thursday, 26 January 2017 11:07

This paper was recently accepted by EurJMedChem and describes the use of SeeSAR and StarDrop's ADME models to guide the design of potent, selective MAO-A and -B inhibitors with appropriate ADME properties for the treatment of Parkinson's disease and other neurological disorders.

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Practical Applications of Matched Series Analysis

Tuesday, 06 December 2016 17:52

This paper, co-authored with our colleagues at NextMove Software, has just been accepted for publication in Future Med. Chem. and explores applications of Matched Series Analysis to SAR transfer, binding mode suggestion, and data point validation.

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Avoiding Missed Opportunities by Analysing the Sensitivity of our Decisions

Wednesday, 19 October 2016 00:00

We've just submitted this article that describes how we can consider the impact of the compound selection criteria we choose on the decisions we make. Identifying sensitive criteria can highlight new avenues for exploration and potential missed opportunities.

Assessing the sensitivity of a criterion in a property profile

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Predicting interactions of compounds and metabolites with toxicity-associated targets

Monday, 05 September 2016 08:44

Peter Hunt gave this presentation at the ACS Fall 2016 National Meeting & Exposition held in Philadelphia, USA.

Abstract

We describe the development of quantitative structure activity relationship (QSAR) models based on activity data from the ChEMBL database, to predict the interaction of compounds with protein targets associated with adverse outcome pathways and toxicities. However, systemic exposure to a compound will also result in the formation of metabolites, which themselves may be the cause of a toxic response. Therefore, we have developed an integrated system linking models that predict the enzymes responsible for metabolism of a parent compound and the resulting metabolites with QSAR models of target interactions. The combination of these models can predict potential toxicities resulting directly or indirectly from exposure to the parent compound. The initial implementation is focused on metabolism by Cytochrome P450 enzymes, but forms a framework that may be extended to other metabolic pathways and additional QSAR models of toxicity

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Preprint: Predicting Regioselectivity and Lability of P450 Metabolism

Tuesday, 23 August 2016 13:51

This recently submitted preprint describes the underlying methods, validation and example applications of the most recent models of Cytochrome P450 metabolism in StarDrop's P450 module.

P450 regioselectivity and metabolic landscape

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Medicinal Chemistry is an art, when you don’t understand the data

Tuesday, 03 May 2016 16:18

Dr Jeremy Edmunds, Abbvie, gave this presentation at "Streamlining Drug Discovery and Development" held in Cambridge, MA, USA on 11 April 2016.

Abstract
When one considers the considerable expense that is associated with developing a drug, it is clearly the responsibility of the chemist to ensure that they are preparing the most optimal compound. To achieve this we have focused our efforts within Abbvie medicinal chemistry toward excellence in design and excellence in synthesis. Here he describes the trials and tribulations of this approach.

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

Friday, 15 April 2016 21:05

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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Speeding up and improving the Identification of a potent B2 agonist as a growth promoter for cattle

Friday, 15 April 2016 16:46

Dr Ashley Fenwick, Zoetis, gave this presentation at "Streamlining Drug Discovery and Development" held in Cambridge, MA, USA on 11 April 2016.

Abstract
Being spun out of a large pharmaceutical company and losing access to a full suite of programs and aids to support drug discovery, Zoetis has had to build its infrastructure from scratch. A monumental task, but also a once in a life time opportunity to change how we do things. By looking back at a project completed prior to the separation, the advantages offered by the suit of programs and solutions we now have in place becomes apparent and paints an encouraging picture of the future.

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Cheminformatics from the end-user perspective: Past, present and future

Friday, 15 April 2016 16:41

Dr Paul Greenspan, Takeda, gave this presentation at "Streamlining Drug Discovery and Development" held in Cambridge, MA, USA on 11 April 2016.

Abstract
Over the course of my 25 year career as a medicinal chemist, cheminformatics (or what we now call cheminformatics) has evolved from rudimentary chemistry databases, to highly sophisticated software suites with ever more powerful means of visualizing and analyzing large chemistry-rich datasets. At the same time, the proliferation of data generation across a wide array of biological and physical parameters, and the availability of ever larger compound collections, has created an explosion in the volume and breadth of data that is available to the drug designer. With both of these trends likely to continue, we are persistently confronted with a fundamental question: How do we make the best use of all of the data that we have at our disposal? My presentation will attempt to review this evolution from the perspective of an end-user, highlighting the opportunities and challenges that we still face as we seek to continually refine the quality of our decision-making in choosing what molecules to make.

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Structure Guided Design and Optimization of Selective Kinase Inhibitors from Fragment Starting Points

Friday, 15 April 2016 16:37

Dr Steve Woodhead, Takeda, gave this presentation at "Streamlining Drug Discovery and Development" held in San Francisco, CA, USA on 14 April 2016.

Abstract
Over recent years the kinome has provided a rich source of druggable therapeutic targets, with over 25 kinase inhibitors now on the market and many more undergoing clinical evaluation. That said, there remain significant challenges to overcome in kinase drug discovery. For example, poor physicochemical properties and non-mechanism based toxicity, often arising from broader kinome activity, are frequently responsible for attrition during development. Accordingly, specificity for the desired therapeutic target and well optimized physicochemical and pharmaceutical properties are crucial for increasing the overall likelihood of success.

Fragment Based Drug Discovery (FBDD) has firmly established itself as a productive approach to the discovery of small molecule drugs and, when supported by X-ray crystallography, can offer a unique platform from which to optimize molecules with both attractive physicochemical property profiles and a high degree of specificity for the target of interest. This presentation will describe the use of FBDD and iterative structure based design to deliver selective small molecule inhibitors for two kinase targets, whilst maintaining desirable physicochemical properties.

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TB Alliance Drug Discovery and Development: Harnessing Global Resources to Address a Global Disease

Friday, 15 April 2016 16:24

Dr Chris Cooper, TB Alliance, gave this presentation at "Streamlining Drug Discovery and Development" held in San Francisco, CA, USA on 14 April 2016.

Abstract
TB is a leading cause of mortality and morbidity globally, killing 1.4 million people every year,1 and robbing millions more of health, hope, and prosperity. Current TB regimens are highly inadequate requiring 6-24 months to complete treatment. Protracted treatment times result in poor adherence and consequently promote the development of multi-drug resistant (MDR) and extensively drug resistant (XDR) TB. Treatment options for drug resistant TB are complex, toxic, and expensive, with less than 10% of MDR TB patients receiving proper care, and of those, more than a third failing to be fully cured.

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Webinar: The Magic Behind SeeSAR

Thursday, 07 April 2016 12:31

Read the presentation "The Magic Behind SeeSAR™: Visual, Interactive 3D Lead Optimisation for Anyone" from the joint BioSolveIT/Optibrium Webinar on April 6, 2016. In this presentation Marcus Gastreich of BioSolveIT described the technology underlying their HYDE scoring function and SeeSAR. This also included worked examples to demonstrate how visually informed lead optimisation can save you considerable time, leading to compounds with an improved profile.

SeeSAR

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Bridging the dimensions: Seamless integration of 3D structure-based design and 2D structure-activity relationships to guide medicinal chemistry

Wednesday, 23 March 2016 16:46

Matt Segall gave this presentation at the ACS Spring National Meeting & Exposition held in San Diego, USA on 13th March 2016.

Abstract

The effective use of software can have a major impact on timelines and innovation in drug discovery. However, the traditional split between computational modellers and synthetic chemists has been blurred and software must be accessible across disciplines to quickly understand and predict structure-activity relationships (SAR). There has been a similar divide between tools for three-dimensional (3D) structure-based design and those for analysis of SAR based on a two-dimensional (2D) compound structure. Seamless integration between these approaches would enable all of the available structural knowledge to be used to guide the efficient design of high quality, active compounds.

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Gaussian processes: We demand rigorously defined areas of uncertainty and doubt

Wednesday, 23 March 2016 16:34

Ed Champness gave this presentation at the ACS Spring National Meeting & Exposition held in San Diego, USA on 16th April 2016.

Abstract

A quantitative structure-activity relationship (QSAR) model is a mathematical function of molecular descriptors. The parameters of this function are found by maximizing the fit of this function to the observed activities of a training set of compounds, using a statistical or machine learning method. Following validation of the resulting model, most methods for estimation of the uncertainty in a prediction focus measures of the ‘domain of applicability’ or ‘distance to model’ to identify new compounds that differ significantly from the training set and hence for which the confidence in a prediction will be low.

Gaussian processes

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