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Latest Publications & Presentations


Poster: Intuitive Workflow to Enumerate and Explore Large Virtual Libraries

Friday, 20 April 2018 13:13

This poster by Matthew D Segall, Aishling Cooke, James Chisholm, Edmund Champness, Peter Hunt and Tamsin Mansley was presented at the ACS National Spring Meeting 2018 in New Orleans.

Abstract

Enumeration of a virtual library based on cores or scaffolds of interest helps to quickly explore potential substituents around hit or lead series and prioritise strategies that are most likely to yield high quality compounds. In this poster, we will describe a seamless workflow, beginning with a search of commercially available building blocks. These can then be ‘clipped’ to generate the corresponding R-groups for enumeration of virtual libraries, using a flexible and visual approach based on defining substitution points around a substructure search of the building blocks. This flexibility means that chemists are not restricted to a limited number of pre-defined patterns for reagent clipping and can adapt to many different reaction schemes, while the visual interface makes it intuitive and easy to use.

R-group clipping

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Capturing and applying knowledge to guide compound optimisation

Friday, 20 April 2018 12:38

Matt Segall gave this presentation at the ACS National Spring Meeting 2018 in New Orleans.

Abstract

Compound design requires a combination of knowledge and expertise from different perspectives: understanding of structure-activity relationships (SAR), based on data from previously studied compounds; expertise from diverse fields to define the multi-parameter optimisation (MPO) objectives of a project; and knowledge of synthetic strategies that may be applicable to create the next rounds of compounds for investigation. All of these forms of knowledge can be captured and applied computationally: Machine learning methods can generate quantitative structure-activity relationship (QSAR) models to predict the properties of novel, virtual compounds; MPO methods capture the desired property criteria for a successful compound for a specific project and rigorously prioritise ideas for consideration; and, optimisation strategies can be captured as structural transformations that reflect steps made in previous chemistry projects.

Activity Landscape

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Translating Methods from Pharma to Fragrances and Flavours

Friday, 20 April 2018 12:25

Tamsin Mansley gave this presentation at the ACS National Spring Meeting 2018 in New Orleans.

Abstract

The pharma sector has generated a wealth of experience in cheminformatics methods that are used in the optimisation of small, ‘drug like’ molecules. While there are differences in the chemistries used to develop flavors and fragrances and the optimisation objectives of these projects, many computational methods can be translated from pharma to guide the design and selection of compounds in this context and improve efficiency and productivity. The properties that describe molecules in these fields are typically different, but both disciplines have the goal of quickly targeting compounds with a balance of properties for the project’s objectives.

In the presentation Tamsin discusses approaches to compound selection and design, including chemical space analysis, property prediction and multi-parameter optimisation, comparing and contrasting datasets and models from pharma with those in flavors and fragrances. This is illustrated by case studies to build and apply robust QSAR models predicting relevant properties, design and prioritisation of new compound ideas and analysis of chemical spaces for selection of compounds, using fragrances and flavors datasets.

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Pistoia Alliance AI/Deep Learning Projects and Community

Wednesday, 07 March 2018 09:19

At our Drug Discovery Consultants' Day in March 2018, Nick Lynch gave an overview of the Pistoia Alliances' projects and community on AI and Deep Learning, including discussions around best practices and data quality.

You can download his slides as a PDF

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Imputation of Protein Activity Data using Deep Learning

Wednesday, 07 March 2018 09:07

At Optibrium's 2018 Drug Discovery Consultants' Day, Dr Gareth Conduit from University of Cambridge and Intellegens Ltd. described their deep learning methods for predicting compound activities against protein targets based on sparse training data and presented early results of a collaboration with Optibrium.

You can download his slides as a PDF.

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Deep Learning and Chemistry

Wednesday, 07 March 2018 08:58

At our 2018 Drug Discovery Consultants' Day, Professor Bobby Glen of the University of Cambridge gave an excellent overview of developments in deep learning and its application to chemistry.

Statistics, Machine Learning and Deep Learning

You can download his slides as a PDF.

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Preprint: WhichP450

Tuesday, 13 February 2018 16:26

This paper appears in J. Comput.-Aided Mol. Des. and describes the underlying methods and validation of the new model predicting the most likely Cytochrome P450 isoforms responsible for metabolism of a compound in StarDrop's P450 module.

WhichP450 and regioselectivity prediction

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Discovery Decisions - Collaborating in Data Management

Monday, 22 January 2018 11:58

This paper appeared in the Winter 2018 edition of EBR.

Abstract

From initial hit to development candidate, drug discovery is an iterative process. At each stage, the latest results are reviewed in the context of all the project data, to choose compounds for progression or identify key structure-activity relationships (SAR) that guide the design of new compounds for synthesis. These activities are usually supported by software for data analysis, visualisation and predictive modelling.

However, obstacles remain to the effective use of such software: different applications are often used for each function; scientists may use one to retrieve data from their database, another to visualise their results and a third for predicting properties of new compounds they are considering for synthesis. Just moving and reformatting data for each software application can be time consuming and error-prone. Furthermore, scientists need to learn multiple user interfaces, each with a different ‘look and feel’. Some software, for example visualising protein-ligand interactions in 3 dimensions, may be available only to expert computational chemists, leading to delays while waiting for an expert to be available and the potential for important details to be ‘lost in translation’.

In this article, we will discuss the requirements for a platform to overcome these challenges and support effective decision-making from data to design. Bringing together all of the information revealed by different analyses may reveal new insights and will foster collaboration between different disciplines, leading to more rapid progress and higher quality compounds.

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Driving Discovery - Predicting P450 Metabolism

Monday, 22 January 2018 11:45

This paper appeared in the Autumn 2017 edition of EBR.

Abstract

Cytochrome P450 (P450) enzymes are responsible for almost 80% of drug metabolism in humans, and metabolism by P450s may lead to several issues for potential new drugs including: low bioavailability, rapid clearance, drug-drug interactions leading to toxicity or lack of efficacy, bioactivation to form reactive or toxic metabolites and variable metabolism in the patient population due to genetic polymorphisms.

In this article, we will discuss some of the questions that a drug discovery team may wish to ask in order to address or, ideally, avoid these issues. For example: Is a compound a substrate for a P450 enzyme and, if so, which isoform? For a compound that is metabolized by a P450, what sites are vulnerable to metabolism, what metabolites will be formed and what strategies could be explored to reduce the rate of metabolism?

In vitro experiments using liver microsomes or hepatocytes can be used address these questions, although more detailed studies are time consuming and expensive. Therefore, computational, or in silico, predictions can be used to supplement experimental data or prioritise compounds for more detailed studies. Furthermore, in silico methods can help to guide the design of new compounds to overcome issues, exploring many optimisation strategies before the medicinal chemist chooses which compounds to synthesise and test. We will describe the state of the art of computational approaches for predicting P450 metabolism and identify areas for future development.

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Poster: Improved quantum mechanical model of P450-mediated aromatic oxidation

Wednesday, 25 October 2017 13:35

Nick Foster presented this poster at the 21st North America ISSX Meeting 2017 held in Providence, USA.

Abstract

The Cytochrome P450 enzymes (P450s) are a large family of monooxygenases involved in the metabolism of drugs via oxidative reactions such as C-H bond hydroxylation, epoxidation and heteroatom oxidation. It has become increasingly important, within drug development, to develop computer based methods to study and accurately predict P450-mediated metabolism of drugs. We recently published a method that uses quantum mechanical simulations to predict the regioselectivity and lability of cytochrome P450 metabolism . This method uses AM1 calculations and Brønsted relationships to estimate the activation energies for the reaction mechanisms leading to P450 metabolism. This model provides accurate predictions of the regioselectivity of metabolism with faster calculation time than ab initio DFT calculations. However, we have continued to investigate opportunities to further improve the accuracy of the semi empirical methods for some oxidative mechanisms such as aromatic oxidation. In the present study, we model the transition state in the reaction coordinate prior to the intermediates formed during aromatic and aliphatic hydroxylation . The ab initio DFT level of theory is used to model these reactions for a range of P450 3A4 substrates, for which experimental data on relative reaction rates are available. A transition state search is performed to calculate accurate activation energies that correlate well with the experimental data. Subsequently, semi-empirical QM methods are used in a similar transition state search to establish a relationship to these DFT based energies. A correlation between the energetics of DFT and semi-empirical QM methods has been established and this correlation has, in turn, been used to develop an improved predictive model for aromatic oxidation, that can provide a fast and increasingly accurate prediction for the P450 mediated metabolism of drugs.

(1) Tyzack, J.; Hunt, P.; Segall, M. J. Chem. Inf. Model. 2016, 56, 2180-2193.

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

Wednesday, 25 October 2017 13:24

This poster by Matthew Segall1, Tamsin Mansley1, Peter Hunt1, Kelly Chibale2, Tanya Paquet2, James Duffy3 was presented at the RSC Cambridge Medicinal Chemistry Meeting held in Cambridge, UK.

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.

Given the limitations of the resources available to projects in these sectors, it important to quickly focus on chemical series and leads with the best possible chance of success downstream. Enabling this requires a combination of capabilities including visualisation and analysis of project data, interpretation of structure-activity relationships and predictive modelling to guide the design of new compounds for synthesis and testing. In this talk, we will describe the underlying methods and illustrate how they can be linked to platforms for sharing of data, to facilitate collaborative approaches to drug optimisation.

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Integrated Cheminformatics to Guide Drug Discovery

Wednesday, 25 October 2017 13:20

Ed Champness gave this presentation at the ACS Fall 2017 National Meeting & Exposition held in Washington DC, USA.

Abstract

A plethora of cheminformatics approaches have been developed to support drug discovery. These include methods for: analysis of structure-activity relationships (SAR), such as matched molecular pair analysis (MMPA), activity cliff detection and R-group decomposition; prediction of properties, including potency, physicochemical and absorption, distribution, metabolism and excretion (ADME), using quantitative structure-activities (QSAR) models; integration of data from public domain and proprietary sources; multi-parameter optimisation to identify high quality compounds with a balance of the properties required for success; and selection of compounds, considering both quality and diversity to avoid missed opportunities.

No single method is likely to provide a solution to the challenges of drug discovery. However, judicious use of cheminformatics 'tools', used in combination, can make a significant difference to the productivity and efficiency of drug discovery. One approach is to link multiple cheminformatics algorithms in a 'pipeline', but this can limit the ability of the medicinal chemist to interact with the process. Instead, seamless integration of methods in an intuitive and highly visual environment can maximise the synergy between the expert chemist’s knowledge and the algorithms’ abilities to process complex data.

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Improved quantum mechanical model of P450-mediated aromatic oxidation

Wednesday, 25 October 2017 13:17

Rasmus Leth gave this presentation at the ACS Fall 2017 National Meeting & Exposition held in Washington DC, USA.

Abstract

The Cytochrome P450 enzymes (P450s) are a large family of monooxygenases involved in the metabolism of drugs via oxidative reactions such as C-H bond hydroxylation, epoxidation and heteroatom oxidation. It has become increasingly important, within drug development, to develop computer based methods to study and accurately predict P450-mediated metabolism of drugs. We recently published a method that uses quantum mechanical simulations to predict the regioselectivity and lability of cytochrome P450 metabolism . This method uses AM1 calculations and Brønsted relationships to estimate the activation energies for the reaction mechanisms leading to P450 metabolism. This model provides accurate predictions of the regioselectivity of metabolism with faster calculation time than ab initio DFT calculations. However, we have continued to investigate opportunities to further improve the accuracy of the semi empirical methods for some oxidative mechanisms such as aromatic oxidation. In the present study, we model the transition state in the reaction coordinate prior to the intermediates formed during aromatic and aliphatic hydroxylation . The ab initio DFT level of theory is used to model these reactions for a range of P450 3A4 substrates, for which experimental data on relative reaction rates are available.

Quantum mechanical models of P450-mediated aromatic oxidation

A transition state search is performed to calculate accurate activation energies that correlate well with the experimental data. Subsequently, semi-empirical QM methods are used in a similar transition state search to establish a relationship to these DFT based energies. A correlation between the energetics of DFT and semi-empirical QM methods has been established and this correlation has, in turn, been used to develop an improved predictive model for aromatic oxidation, that can provide a fast and increasingly accurate prediction for the P450 mediated metabolism of drugs.

(1) Tyzack, J.; Hunt, P.; Segall, M. J. Chem. Inf. Model. 2016, 56, 2180-2193.

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Poster: Closing the loop between synthesis and design: Balancing optimisation of potency with selectivity

Wednesday, 25 October 2017 13:10

This poster by Peter Hunt, Tamsin Mansley, Edmund Champness, Nicholas Foster & Matthew Segall was presented at the ACS Fall 2017 National Meeting & Exposition held in Washington DC, USA.

Abstract

Drug discovery is a multi-parameter optimisation (MPO) process, in which the goal is to simultaneously optimise target potency, selectivity and a broad range of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties, prioritising those compounds most likely to succeed against a project’s objectives. However, the ultimate goal is not simply to select from those compounds already available, but to design new compounds with an improved balance of properties.

De novo design approaches typically result in more in silico compound ideas than can reasonably be synthesised and tested. Assessment of these virtual compounds therefore requires development and use of in silico models which predict potency, or other properties, based upon information derived from the known structure-activity relationships (SAR). These predictive models can be used in an MPO assessment of selectivity, optimising for high potency at one receptor and low potency at others.

We present a truly MPO approach to de novo design, using Probabilistic Scoring and quantitative structure-activity relationship (QSAR) models to generate and prioritise high quality compounds ideas. This approach enables simultaneous optimisation of the virtual compounds for high potency with selectivity over multiple receptors, whilst also considering a balanced ADMET profile. It is exemplified with optimisation of selective dipeptidyl peptidase (DPP) inhibitors.

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Docking - old hat or hats off

Thursday, 01 June 2017 08:17

Dr Christian Lemmen, BioSolveIT, gave this presentation at the "Streamlining Drug Discovery" symposiums held in San Diego, CA, USA on 21 April 2017 and Cambridge UK on 18 May 2017.

Abstract
20 years ago - 1996 - is when the first FlexX docking publication appeared, which is still cited on a broad average more than once every week. So is docking an old hat, or are there new developments still that should be rewarded with our hats off? We will demonstrate that the latter is the case. These 20 years have been 20 years of active research improving the basic strategy piece by piece, improving results and enlarging the applicability domain quite a bit.
In this presentation we focus mainly on one aspect of docking, namely the fact that in vast majority of cases the active site is actually not empty but provided as a protein ligand complex. Most docking applications throw out a most valuable piece of information, namely the bound ligand, before the docking is performed. This is meant not to introduce any bias and of course necessary if you do virtual screening of compound data bases - as we all did 20 years ago. However, here we want to focus on other applications: the optimization of a compound, the evolution of a fragment binder, or the SAR analysis of a comparatively narrow compound series.
In all these cases it would be stupid to throw away the information of the bound ligand instead of giving the search algorithm a hint as where to start the search. We developed a novel template based docking strategy that does exactly that, it calculates common substructures between the bound ligand and the newly designed compound and uses the fragments' binding modes as seed points for the compound placement. Not only does this lead to highly accurate placements, but it is also much faster compared to a generic docking. We describe the algorithm as well as several application cases.
REFERENCES
FlexX, JMB, 261, 470–489, 1996.
SeeSAR v6, BioSolveIT GmbH, www.biosolveit.de/SeeSAR

You can download this presentation as a PDF.

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Continuing the public benefit of the Carcinogenic Potency Database (CPDB)

Thursday, 01 June 2017 08:08

Dr Nik Marchetti, Lhasa Ltd, gave this presentation at "Streamlining Drug Discovery" symposium held in Cambridge UK on 18 May 2017.

Abstract
CPDB is a now-discontinued database created and maintained by Dr. Lois Swirsky Gold, the Director of the CPDB project. For almost 30 years, it was a reference point for long-term animal cancer tests and for risk assessment in humans. It also went beyond just cataloguing studies, but it also presented calculated TD50 values for over 1,500 chemicals. Lhasa Limited has built on the work initiated by Gold and released a free and online carcinogenicity database based on the CPDB. By doing so, Lhasa is committing to maintaining the database , while also bringing in over 30 years of experience in predictive software and structure searchable databases. Moreover, Lhasa has revised the original data from CPDB, grouping studies with the same active ingredient but different formulation, giving more details and information to the final user. Finally, Lhasa recalculated the TD50 value for all chemicals, based on Gold's method, assuring consistency between the previous database and the current one.

You can download this presentation as a PDF.

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Lead Identification: Where Science Meets Art

Tuesday, 30 May 2017 13:20

Dr Mehran Jalaie, Pfizer, gave this presentation at "Streamlining Drug Discovery" symposium held in San Diego, CA, USA on 21 April 2017.

Abstract
Picking desirable bioactive compounds for therapeutic projects has been an “artful skill” for decades relying on intuition and previous experience of individuals involved in compound selections on active projects. However, as the frequency of novel targets rises, previous experience on unrelated targets becomes increasingly less likely to be helpful. In recent years, several teams have developed many virtual screening techniques to assist in hit identification, with scoring functions and statistical analysis providing scientific backing and supplementation to designer’s intuitions.
In this presentation, we will share the results of virtual screening campaigns that successfully identified hits and lead series for several projects. The success and efficiency of the lead identification efforts for these projects were driven by availability, sophistication, streamlining of the tools, processes, and the incorporation of best practice methodologies.

You can download this presentation as a PDF.

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Resolving the question of on- or off-target toxicity - a case study

Tuesday, 30 May 2017 13:08

Dr Joachim Rudolph, Genentech, gave this presentation at "Streamlining Drug Discovery" symposium held in San Diego, AA, USA on 21 April 2017.

Abstract
The question of whether toxicity caused by small molecules is due to on-target or off-target pharmacological effects is frequently encountered in drug discovery programs and of decision making significance. Genetically engineered mouse models are widely used to assess the safety consequences of depleting a target, but have limitations in predicting the toxic effects of target-related small molecule drug action. Diverse chemical analogs were designed/selected to investigate the root cause of cardiovascular toxicity encountered in a program aimed at discovering inhibitors of pan-Group I p21-activated kinases (PAK1, 2, and 3) for use in breast cancer. Mouse tolerability studies with these compounds revealed persistent toxicity, a correlation of minimum toxic concentrations and PAK1/2 mediated cellular potencies, and absence of toxicity with structural analogs devoid of PAK1/2 activity. Our data suggest that the toxicity results from the inhibition of PAK2, which may be enhanced by PAK1 inhibition, and caution against continued pursuit of pan-Group I PAK inhibitors in drug discovery. Carefully selected sets of small molecule tool compounds have significant value in probing the safety of drug targets, and their use in tolerability experiments is highly complementary to gene knockout studies.

You can download this presentation as a PDF.

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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

You can download this presentation as a PDF.

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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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