Perspectives on generative chemistry – potential and reality
Have advances in AI and deep learning reached a threshold whereby generative chemistry methods are redefining drug design? This webinar…
Optibrium expands operations in North America for AI and computational drug discovery technologies
Establishment of Optibrium Inc. bolsters ongoing strategic Company growth and strenghtens direct US customer support. Dr Tamsin Mansley promoted to President of Optibrium Inc. to lead growing US-based team and business operations.
Predicting regioselectivity of AO, CYP, FMO and UGT metabolism using quantum mechanical simulations and machine learning
This paper describes the prediction of the regioselectivity of metabolism by AOs, FMOs and UGTs for humans and CYPs for three preclinical species.
Transformative insights from your data in CDD vault with Cerella AI
In this webinar, learn about Cerella’s unique AI methods, see examples of its successful application throughout the drug discovery process and watch a demonstration of how CDD Vault and Cerella connect to seamlessly integrate with your workflows.
Inspyra matched series analysis
Explore ways to use the Inspyra Panel, in combination with Matched Series Analysis (MSA).
Exploring optimisation strategies with Inspyra
This worked example uses Inspyra™ to interactively explore optimisation strategies to achieve a selective inhibitor of DPP-4 with appropriate physicochemical properties.
Perfecting the use of imperfect QSAR models
In this webinar, we examine the effective use of QSAR modelling in drug discovery and discuss a variety of pain points for medicinal chemists in knowing when a model can be trusted and how to avoid common pitfalls.
Reaction-based library enumeration
In this example, we are going to use the reaction-based library enumeration feature in StarDrop’s Nova module to generate a library of virtual compounds. This will be based on pre-defined sets of reagents that will be used to generate products using well-known reactions.
Ligand-based drug design using Surflex eSim 3D
This worked example uses StarDrop’s Surflex eSim3D module to assess a small library of compounds for their similarity to known Heat Shock Protein 90 (HSP90) ligands. ideo archive.
Automatic generation of new compound ideas
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.
R-group clipping of reagents for library enumeration
In this example, using StarDrop’s R-group clipping tool, we will quickly transform chemical building blocks into their corresponding substituents, ready to enumerate a virtual library in StarDrop’s Nova module.
SeeSAR pose – 3D analysis of virtual libraries
This worked example explores ways to assess and design compounds in 3D using the SeeSAR Pose module.
SeeSAR affinity – binding affinity and torsion angle analysis of virtual libraries
This worked example explores ways to assess the binding affinity of docked compounds.
Fast follower: optimising P450 metabolic stability
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.
R-group analysis
This example looks at R-group analysis of chemical series to identify key functionalities which influence potency.
MPO explorer: sensitivity analysis
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.
MPO explorer: automatically building a scoring profile with rule induction
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 CNS data set.
Guided multi-parameter optimisation of 2D and 3D SAR
This SeeSAR and ADME QSAR worked example uses a combination of 2D and 3D methods to understand and optimise a virtual library of Heat Shock Protein 90 (HSP90) inhibitors.
Automatic QSAR model building and validation
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.
