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.
AI Solutions from hit to candidate
In this webinar, we explore the highlights of collaborative project results that demonstrate how every phase of the drug discovery process can be radically improved by applying proven AI technology. Providing scientists with insights on which to base decisions can identify valuable new opportunities and reduce the time and cost of AI drug discovery cycles.
We review case studies from collaborations with Constellation Pharmaceuticals, AstraZeneca, Genentech, the University of Dundee and Takeda Pharmaceuticals to validate the impact of applying AI to experimental data and illustrate dramatic improvements to their project outcomes.
Join Samar Mahmoud and Matt Segall for this fascinating deep dive into the revolution that AI is bringing to the challenges of sparse and noisy drug discovery data.
The launch of StarDrop 7.2 – introducing Inspyra™: webinar
In this webinar, we demonstrate how Inspyra™ creates a seamless blend of your expertise and unique AI that fits naturally within your workflow. It helps you to rigorously explore many optimisation strategies and quickly identify high-quality compounds for your projects.
Synergy and complementarity between focused machine learning and physics-based simulation in affinity prediction
We present results on the extent to which physics-based simulation (exemplified by FEP+) and focused machine learning (exemplified by QuanSA) are complementary for ligand affinity prediction.
Imputation of sensory properties using deep learning: webinar
In this webinar, we demonstrate how Augmented Chemistry®, a unique deep learning method, can learn from higher throughput data together with limited panel data to provide high-quality imputations for sensory properties.
Harnessing generative chemistry – how scientists can guide compound idea generation
Why have generative chemistry methods been unable to redefine modern drug discovery and compound idea generation?’ In this session we shed light on a typical shortcoming of generative methods related to prioritising promising over unsuitable directions for exploration.
3D ligand-based drug design: webinar
In this webinar, we demonstrate intuitive workflows for 3D ligand-based drug design
Optibrium releases 3D ligand-based design module for StarDrop drug discovery software
Innovative predictive methods support virtual screening and compound design in the absence of 3D structure data.
Data visualisation: saying it all in a bite-sized chunk
In this webinar, we look at how we can use data visualisation in an impactful and effective way to communicate many dimensions of information. We illustrate some of the ways that we can achieve this and discuss visual methods to guide our decisions in drug discovery.
eSim3D: electrostatic-field and surface-shape similarity for ligand-based drug design
In this webinar, we present eSim3D, a novel ligand-based drug design approach based on electrostatic-field and surface-shape similarity coupled with unique conformational search capabilities, offering unprecedented accuracy and performance.
A global deep learning model for global health drug discovery
In this webinar, we described the generation and validation of a ‘global’ model using deep learning imputation on a data set of 300,000 compounds and 500 experimental endpoints, targeting global health indications.
We demonstrated how this global model can be applied to individual optimisation projects, offering improved compounds design performance over ‘local’ project-specific models by learning across a broad chemical diversity.
Conformational strain of macrocyclic peptides in ligand–receptor complexes based on advanced refinement of bound-state conformers
To better understand conformational propensities, global strain energies were estimated for 156 protein-macrocyclic peptide cocrystal structures.
Cerella™: Reduce the time and cost of your discovery cycles with deep learning
In this webinar, we demonstrate how Cerella™ (AI drug discovery software) highlights new opportunities and guides more efficient compound optimisation.
