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Guided Multi-Parameter Optimisation of 2D and 3D SAR

Wednesday, 22 June 2016 08:56
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Matt Segall

This example uses a combination of 2D and 3D methods to understand and optimise a virtual library of Heat Shock Protein 90 (HSP90) inhibitors. The library, created by a de novo design process, is based around an amide substitution on a beta resorcylic acid core. The objective in this example is to use the SeeSAR™ module to develop an understanding of the 3D structure-activity relationships (SAR) and then use multi-parameter optimisation to further develop the absorption, distribution, metabolism and excretion (ADME) and physicochemical properties of a potent inhibitor without losing efficacy.

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Matched Series Analysis

Tuesday, 09 June 2015 00:00
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Edmund Champness

The objective in this worked example is to identify new derivatives that are likely to improve activity at their target, given the SAR already generated on a project. This example uses a publically available set of Human Chymotrypsin Ki data and searches the ChEMBL pIC50 knowledge base (generated by NextMove Software) to find matched series that indicate new substitutions with a high likelihood of improving the binding at Chymotrypsin.

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Matched Series for SAR Analysis

Tuesday, 09 June 2015 00:00
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Edmund Champness

This worked example considers a publically available set of PPARγ pIC50 data which contains a number of chemical series. Matched Series Analysis is used to identify when a substitution from one series has a high likelihood of improving the binding of another series at PPARγ. This approach is then further applied to help analyse the project’s SAR to generate hypotheses regarding binding modes and identify possible anomalous measurements for further investigation.

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MPO Explorer: Automatically Building a Scoring Profile with Rule Induction

Thursday, 30 January 2014 00:00
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Edmund Champness

In this example we will use the Profile Builder in StarDrop’s MPO Explorer module to derive a multi-parameter scoring profile, based on a data set initially described by Wager et al. [ACS Chem. Neurosci. 1 p. 435 (2010)]. Wager et al. used this data set to develop a multi-parameter optimisation method for selection of compounds intended for CNS indications. Wager's ‘CNS MPO score’ is calculated as the sum of the values of desirability functions for six physicochemical parameters, calculated logP (clogP), calculated logD at pH 7.4 (clogD), molecular weight (MW), topological polar surface area (TPSA), number of hydrogen bond donors (HBD) and the pKa of the most basic center (pKa), resulting in a value between 0 and 6. The authors compared the CNS MPO score for a set of 119 marketed drugs for CNS targets with 108 Pfizer CNS candidates and found that 74% of the marketed drugs achieved a desirability score of  4 compared with only 60% of the Pfizer candidates. The scoring profile derived by MPO Explorer will contain one or more rules that indicate combinations of properties that significantly increase the chances of identifying a drug and we will compare this with the results of the Wager et al. CNS MPO score.

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MPO Explorer: Sensitivity Analysis

Thursday, 30 January 2014 00:00
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Edmund Champness

In this example, we will use the Sensitivity Analysis tool in StarDrop’s MPO Explorer module to check if the ranking of compounds in a data set is sensitive to any of the criteria or importance values in a scoring profile. This is important to consider because, if the compounds we would choose are very sensitive to a property criterion, we should carefully consider if this criterion is appropriate to avoid missing potentially valuable opportunities. This analysis can also help to determine if different values for a criterion would have an impact on the strategy for a project, for example if there is a disagreement regarding the most appropriate criterion to use. If the ranking of compounds is essentially unchanged within a reasonable range of values, this means that the scoring profile can be used with confidence that the ultimate selection will be robust to the chosen criteria.

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Scaffold Hopping with Library Enumeration

Wednesday, 29 January 2014 00:00
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Edmund Champness

In this example we are going to use the library enumeration feature in StarDrop’s Nova module, in combination with R-group analysis, to generate a virtual library representing a potential new lead series. This will be based on a previous series and explore the impact of a change of scaffold and variations in a side chain, while retaining the substituents at two key positions.

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Addressing Toxicity Risk in Multi-Parameter Optimisation

Tuesday, 28 January 2014 00:00
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Edmund Champness

In this example we will illustrate how knowledge-based predictions of toxicity can be used within a MPO environment to guide the selection and design of compounds with a good balance of properties and reduced risk of toxicity. We will explore a library of compounds with COX2 inhibition data, with the goal of identifying a high quality lead series, using StarDrop’s Probabilistic Scoring to integrate experimental data, predicted ADME properties from the ADME QSAR module and predictions of toxicity risk from the Derek Nexus module.

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Fast Follower: Optimising P450 Metabolic Stability

Wednesday, 16 October 2013 21:07
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Edmund Champness

In this example we will explore the feasibility of pursuing a fast-follower for Buspirone, a 5-HT1A ligand used as an anti-anxiolytic therapeutic, which has a known liability due to rapid metabolism by CYP3A4. This example illustrates the use of StarDrop’s P450 metabolism models to explore structural modifications with the aim of identifying potent analogues with significantly improved stability with respect to CYP3A4.

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Comparing Chemical Series with Probabilistic Scoring

Wednesday, 16 October 2013 21:05
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Matt Segall

This example is taken from a project in which screening of a diverse library resulted in hits from multiple chemistries. Without the resources to follow-up all of the hit chemistries, the project team wished to focus on a small number of series which were most likely to yield high quality leads with appropriate physicochemical and ADME properties.

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Multi-Parameter Optimisation of 3D SAR

Wednesday, 16 October 2013 21:02
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Edmund Champness

In this example we will explore the multi-parameter optimisation of a series of CDK2 inhibitors, combining a 3D insight into the structure-activity-relationship (SAR) gained from StarDrop’s torch3D™ module and predictions of ADME and physicochemical properties, using StarDrop’s unique Probabilistic Scoring approach.

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Guiding Selection and Design in Hit-to-Lead

Wednesday, 16 October 2013 20:50
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Edmund Champness

The objective in this example is to identify one or more high quality chemistries for progression to detailed in vitro and in vivo studies, based on initial screening data for potency; ideally the compounds chosen for progression should not only be potent, but also have appropriate ADME properties to result in a high quality lead series. We will also use StarDrop to explore potential modification of one of the existing compounds to improve its properties.

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Automatic Generation of New Compound Ideas

Wednesday, 16 October 2013 20:50
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Edmund Champness

The objective in this tutorial is to evolve the chemistry around three lead compounds which have good ADME properties but which have insufficient inhibition of the target, the Serotonin transporter. Using Nova and Probabilistic Scoring we look at how we can generate new ideas for compounds to improve the potency while maintaining the balance of other properties.

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Automatic QSAR Model Building and Validation

Wednesday, 16 October 2013 20:50
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Edmund Champness

This tutorial walks through the process of building and validating a QSAR model of Muscarinic Acetlycholine M5 receptor potency using StarDrop's Auto-Modeller.

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Hit List Triage with Card View

Wednesday, 16 October 2013 20:50
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Edmund Champness

This example uses StarDrop's Card View to explore the results from a kinase project in which a large screening campaign has resulted in a hit list. The project team wish to evaluate the list to identify the chemotypes within and focus their resources on a small number of series that have demonstrable SAR at the target and are most likely to yield high quality leads with appropriate physicochemical and ADME properties.

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R-Group Analysis

Wednesday, 16 October 2013 20:50
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Edmund Champness

The objective in this example is to take a look at an R-Group analysis of two data sets to identify functionalities which are influencing potency. In the second half we consider an example where we have different series, based around multiple scaffolds, where the substitution points are equivalent because they share a similar binding pose. In these cases, we would like to identify common patterns for substitutions at equivalent positions or look for the effect of the core replacements on compound properties. To do this we show how multi-scaffold R-Group analysis can be performed in StarDrop.

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