Reaction-based library enumeration webinar
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 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.
This peer-reviewed article, published in the Journal of Medicinal Chemistry, describes how identifying sensitive criteria can highlight new avenues for exploration, and assist us in avoiding missed opportunities
In this webinar we discussed how you can ensure that you don’t miss valuable opportunities due to the criteria used…
Try Matched Series Analysis in this follow-along tutorial
We present an approach that uses structural information known prior to a particular cutoff-date to make predictions on ligands whose bounds structures were determined later. The knowledge-guided docking protocol was tested on a set of ten protein targets using a total of 949 ligands.
We present a hybrid structure-guided strategy that combines molecular similarity, docking, and multiple-instance learning such that information from protein structures can be used to inform models of structure–activity relationships.
To compare chemical structures, we can look at a number of 2D and 3D characteristics. In this paper, a group of 358 drugs with overlapping pharmacology were assessed for chemical similarity, using a new framework.
Summary In this study, our researchers combined an automatic model generation process for building QSAR models with the Gaussian Processes…
In this article, Olga describes how we extend the application of Gaussian Processes technique to classification problems. These computational techniques…