In this section we have provided some tutorials that explore the capabilities of StarDrop and the optional modules.
For each tutorial we have provided links to download all the files required to walk-though the example yourself and in many cases there is also an explanatory video.
This worked example uses a combination of 2D and 3D methods to understand and optimise a virtual library of Heat Shock Protein 90 (HSP90) inhibitors. 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.
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.
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 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.
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.
During this example we will consider three compounds from a lead series which we would like to try to evolve into a candidate. The compound has a good profile of ADME properties but insufficient inhibition of the target, the Serotonin transporter. In this example we will use StarDrop’s Nova module to generate new ideas for compounds to improve the potency while maintaining the balance of other properties.
In this example we look at R-Group analysis of chemical series to identify key functionalities which influence potency. One example looks at a series of analogues of Buspirone and a second uses a multi-scaffold R-group analysis to analyse SAR across two scaffolds with equivalent substitution points.
This example explores the application of the Auto-Modeller module to build a QSAR model of potency against the Muscurinic Acetylcholine M5 receptor, based on public domain Ki data. The resulting model is applied to novel compound to predict their properties and visualise the SAR.
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.
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. . 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.
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.
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.
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.
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.