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Publications & Presentations



Latest Publications & Presentations

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


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.


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.


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


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.


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


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


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.

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.

You can download this presentation as a PDF.


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.


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.


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.

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.

You can download this presentation as a PDF.


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.

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.

You can download this presentation as a PDF.


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.

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.

You can download this presentation as a PDF.


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.

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.


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.



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.


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.


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.


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


Closing the Loop Between Synthesis and Design

Monday, 21 March 2016 15:46

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


Chemists frequently draw upon their experience and chemical intuition to make sense of complex project data and select new compounds to synthesize. However, drug discovery projects increasingly demand greater efficiencies with shorter timelines and lower costs, putting medicinal chemists under pressure. Additionally, the traditional divide between computational modellers and synthetic chemists is no longer clear and software must be easily accessible across disciplines; project teams need to quickly understand and predict structure-activity relationships (SAR), identify potential liabilities and design new compounds with the highest chance of success.

Synthesis to Design


When Two are not Enough: Lead optimization beyond matched pairs

Wednesday, 21 October 2015 12:37

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


Modeling ABC transporters as potential DILI targets

Tuesday, 25 August 2015 14:42

Matt Segall gave this presentation at the ACS Fall 2015 National Meeting & Exposition held in Boston, USA on 16th August 2015.


Predicting the interaction of compounds with targets associated with toxicity can provide inputs to hierarchical models integrating systems toxicology, physiologically-based pharmacokinetic (PBPK) models and organ simulations to predict compound interactions with adverse outcome pathways (AOP).
For example, MRP4 (Multi-drug resistance-associated protein 4 or ABCC4) mediates the transport of signalling molecules (such as cAMP and cGMP), prostaglandins and leukotrienes (PGE1, PGE2, LTB4) and can be inhibited by drugs such as Celecoxib, Probenecid, MK-571 and Sulfinpyrazone . BSEP (Bile salt export pump or ABC11) is localised in the cholesterol rich canalicular membranes of hepatocytes and its function is to eliminate unconjugated/conjugated steroidal acids from the hepatocyte into the bile. The loss of this transporter function is seen in the genetic disease progressive familial intrahepatic cholestasis type 2. Inhibition of both of these transporters MRP4 and BSEP has been identified as a risk factor in the development of cholestatic DILI (drug-induced liver injury) .
We have used the publically available data from ChEMBL to build categorical and continuous quantitative structure-activity relationship (QSAR) models in order to determine the molecular properties which contribute to activity at these transporters and compare these features with known hepatotoxic compounds. We have compared the results from these models with predictions from the Derek Nexus approach for knowledge-based prediction of hepatotoxicity . The resulting QSAR models, along with models of other toxicity-related targets, will form part of a hierarchy of molecular-, systems- and physiologically-based models to identify compounds with an increased risk of toxicity as part of the HeCaToS project .

You can download this presentation as a PDF.

Russel, F.G. et al. Trends Pharmacol. Sci. 29(4) pp. 200-7 (2008)
Kis, E. et al. Toxicol. in Vitro. 26(8), pp. 1294-9 (2012)
Greene, A. et al. SAR and QSAR in Environmental Research 10(2-3), pp. 299-314 (1999)


Data visualization: New directions or just familiar routes?

Tuesday, 25 August 2015 13:27

Ed Champness gave this presentation at the ACS Fall 2015 National Meeting & Exposition held in Boston, USA on 19th August 2015.


Data visualization tools make it very easy to represent our data graphically and present it in a way that clearly communicates patterns and trends. But, there is a risk that visualizations may be used, in practice, to confirm or justify our own hypotheses and biases. Instead, can data visualizations bring to light patterns in our data, drive new hypotheses and show us things we weren’t expecting? In this presentation we will look at a number of common data analyses and visualizations used within the drug discovery process. We will illustrate some of the ways that these approaches can be misleading, with examples showing how inappropriate use of data visualization can lead us to conclusions which aren’t necessarily supported by our data. We will discuss alternative, visual methods to guide our decisions in drug discovery and consider ways in which these can enable us to drive the analysis of data without introducing any of our own biases.
Data Visualisation: 5HT1a Example


Webinar: Quantum mechanical models of P450 metabolism

Thursday, 18 June 2015 00:00

Read the presentation "Quantum mechanical models of P450 metabolism to guide optimization of metabolic stability" from the Webinar on June 17, 2015. In this Jon Tyzack described the methodology underlying StarDrop P450 models and presents two case studies to demonstrate their applicability to drug discovery projects.

Example of P450 regioselectivity output


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