Optibrium

In this article Olga describes how we extend the application of Gaussian Processes technique to classification problems. We explore two approaches, an intrinsic Gaussian Processes classification technique and a probit treatment of the Gaussian Processes regression method. Here we describe the basic concepts of the methods and apply these techniques to building category models of blood-brain barrier penetration and hERG inhibition. We also compare performance of Gaussian Processes for classification to other known computational  methods, namely decision trees, bagging and probit PLS.

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.

Young Shin and his colleagues at Genentech presented this poster at the ISSX North American Regional meeting in Baltimore, MD in October 2009.

COMPARISON OF METASITE AND STARDROP PREDICTION OF CYP3A4, CYP2C9 AND CYP2D6 V. Sashi Gopaul, Young Shin, Hoa Le, Matthew Baumgardner, Cornelis Hop and Cyrus Khojasteh Drug Metabolism & Pharmacokinetics, Genentec, Inc, South San Francisco, CA, USA, 94080

Metabolite identification studies play an important role in determining the sites of metabolic liability of new chemical entities (NCEs) in drug discovery. However, generating these complex and detailed studies in a highthroughput environment is often a challenge. Therefore, the use of in silico tools that can predict the sites of metabolism of an NCE could enhance the drug design process. In this study we compare the utility of MetaSite and Stardrop, two predictive softwares available for this purpose... 
READ MORE

This article was published in Chemistry & Biodiversity, Volume 6, Issue 11, Pages 2144 - 2151

Abstract

ADMET models, whether in silico or in vitro, are commonly used to ‘profile’ molecules, to identify potential liabilities or filter out molecules expected to have undesirable properties. While useful, this is the most basic application of such models. Here we will demonstrate how models may be used to go ‘beyond profiling’ to guide key decisions in drug discovery. For example, selection of chemical series to focus resources with confidence or design of improved molecules targeting structural modifications to improve key properties....

Dan Ortwine gave this presentation as part of a workshop at the ISSX North American meeting in Baltimore, MD, USA, 2009.

The workshop, titled Effectively Using In Vitro Data, In Silico Models and Data Mining at Early Stage Drug Development had the following introduction:

At the early stages of drug discovery there are large numbers of compounds synthesized to examine the chemical landscape needed for discovering the lead compounds. Chemical diversity plays a key role at this stage to increase the probability of success when narrowing down to clinical candidates. In this workshop, we will discuss a number of currently available in vitro assays and in silico tools and how they are complementary. We hope by the end of the workshop to provide a platform for decision making purposes in early drug discovery.

Defining a property profile is subjective and often leads to lengthy, interdisciplinary discussions about the criteria and their relevance. For example, is it worth sacrificing some potency to gain additional metabolic stability or solubility? However, a question that is rarely asked is, “What impact would that trade-off have on the final outcome?”, particularly given the underlying uncertainty.

This poster was displayed at the ISSX North American meeting in Baltimore, USA, 2009.

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?"

In this presentation Olga Obrezanova describes  an automated process for building QSAR models (now available as part of StarDrop as the Auto-Modeller!). Olga goes on to demonstrate the effectiveness of the process by carrying out comparisons of this technique with traditional "hand-on" modelling approaches for blood-brain barrier penetration and aqueous solubility.

This presentation was given at the Zing Computational Chemistry Conference in March 2009.

The current paradigm of  drug discovery utilising chemical library synthesis coupled with high throughput screening technologies often gives rise to a situation whereby drug discovery programmes are compound rich although poor in ADME properties. As such, the ADME properties of compounds require optimisation, through the phases of lead optimisation, prior to progression for clinical development increasing the cost and duration of the process. A prime driver of drug discovery is therefore the early identification of compounds from diverse chemical spaces with optimal ADME properties.

This poster was displayed at the ISSX international meeting in Japan, 2007.

This article was published in QSAR & Combinatorial Science, Volume 25, Issue 12, Pages 1172 - 1180 (DOI
10.1002/qsar.200610093)

Abstract
In this article, we review recent developments in the prediction of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties by Quantitative Structure – Activity Relationships (QSAR). We consider advances in statistical modelling techniques, molecular descriptors and the sets of data used for model building and changes in the way in which predictive ADMET models are being applied in drug discovery. We also discuss the current challenges that remain to be addressed. While there has been progress in the adoption of non-linear modelling techniques such as Support Vector Machines (SVM) and Bayesian Neural Networks (BNNs), the full advantages of these "machine learning" techniques cannot be realised without further developments in molecular descriptors and availability of large, high-quality datasets. The largest pharmaceutical companies have developed large in-house databases containing consistently measured compound properties. However, these data are not yet available in the public domain and many models are still based on small "historical" datasets taken from the literature. Probably, the largest remaining challenge is the full integration of predictive ADMET modelling in the drug discovery process. Until in silico models are applied to make effective decisions in a multi-parameter optimisation process, the full value they could bring will not be realised.

This is a preprint of the article published in J Comput Aided Mol Des. 2008 Jun-Jul;22(6-7):431-40. Epub 2008 Feb 14.

Abstract

In this article, we present an automatic model generation process for building QSAR models combined with Gaussian Processes, a powerful machine learning modeling method. We describe the stages of the process that ensure  models are built and validated within a rigorous framework: descriptor calculation, splitting data into training, validation and test sets, descriptor filtering, application of modeling techniques and selection of the best model. We apply this automatic process to data sets of blood-brain barrier penetration and aqueous solubility data sets and compare the resulting automatically generated models with ‘manually’ built models using external test sets. The results demonstrate the effectiveness of the automatic model generation process for two types of data sets commonly encountered in building ADME QSAR models, a small set of in vivo data and a large set of physico-chemical data.

In this presentation, Alan Beresford discusses drug discovery and considers how ADME models fit into the process as a step necessary for helping to manage the numbers game. Alan describes how, by using appropriate interpretation of ADME model results, it is possible to credibly include them within a traditional screening cascade.

This presentation was given at the MedChem USA Conference in 2007.

The rapid design-test-redesign cycles of modern drug discovery and the demand for fast model (re)building whenever data becomes available have given rise to a trend to develop computational algorithms for automatic model generation. Automatic modelling processes allow computational scientists to explore large numbers of modelling approaches very efficiently and make QSAR/QSPR model building accessible to non-experts.

This posrter was displayed at MedChem ADMET Eurpoe, 2008.

In silico predictive models are now widely used to predict a range of molecular properties and help prioritise molecule for synthesis. However, a common criticism often levelled at predictive models is that they offer few clues regarding why a molecule is predicted to have a certain property. By definition, models encode relationships between molecular structure and properties, but interpreting and visualising this information to design better molecules has been almost impossible. This is particularly true of models built with modern ‘machine learning’ techniques such as artificial neural networks (ANN), Gaussian processes (GP) or support-vector machines (SVM). The models that these techniques create have commonly been described as ‘black box.’

This poster was presented at the MedChem Europe meeting, 2007.

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.

In this presentation Olga Obrezanova talks about Gaussian Processes - a powerful computational method  for QSAR modelling. Olga starts by describing the main ideas of this technique.

We are mostly interested in the application of this technique to predictive modelling of ADME properties. The importance of optimising ADME properties of potential drug molecules is now widely recognised. Considering the ADME properties early in the drug discovery process can reduce the costs of the drug development and decrease the attrition rate of drug candidates. We have developed new techniques for finding parameters of the Gaussian Processes method which I will present. I will also show examples of application of these techniques to ADME and QSAR datasets and compare Gaussian Processes methods with other known techniques. The demand of modern drug discovery for fast model (re)building whenever new data becomes available gave rise to a trend to develop computational algorithms for automatic model generation. I will demonstrate how we use Gaussian Processes in an automatic modelling process. (The purpose of such algorithms is to save scientists' time, explore more modelling possibilities and make the process of QSAR model building accessible to non-experts.)

This presentation was given at the American Chemical Society conference in Boston, 2007.