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.
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.
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.
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.
In this webinar, we demonstrate intuitive workflows for 3D ligand-based drug design
Innovative predictive methods support virtual screening and compound design in the absence of 3D structure data.
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.
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.
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.
To better understand conformational propensities, global strain energies were estimated for 156 protein-macrocyclic peptide cocrystal structures.
In this webinar, we demonstrate how Cerella™ (AI drug discovery software) highlights new opportunities and guides more efficient compound optimisation.
In this webinar, we demonstrated how to generate virtual libraries by applying tractable, robust chemical reactions to readily available building blocks in a highly flexible and user-friendly environment.
In this webinar, we present a flexible and intuitive framework in which similarity relationships can be interactively navigated to quickly interpret the results, identify important structure-activity relationships (SAR) and use that SAR in new compound design.
This webinar describes example applications of multi-parameter optimisation to find high-quality lead compounds.
We report a new method for X-ray density ligand fitting and refinement that is suitable for a wide variety of small-molecule ligands, including macrocycles.
This webinar focused on the design of visualisation software for translational toxicology, with particular reference to the challenges that the many different sources of toxicology data pose.
Using the DUD-E+ benchmark, we explore the impact of using a single protein pocket or ligand for virtual screening compared with using ensembles of alternative pockets, ligands, and sets thereof.
This study aimed to create a model for predicting pKa using a semi-empirical quantum mechanics (QM) approach combined with machine learning (ML).
ForceGen is both faster and more accurate than the best of all tested methods on a very large, independently curated benchmark of 2859 PDB ligands. In this study, the primary results are on macrocycles, including results for 431 unique examples from four separate benchmarks.
