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.
From similarity to SAR
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.
Informatics for effective drug discovery
Learn how advances in informatics technology are inspiring a new generation of innovative products that streamline and enhance the efficiency and productivity of drug discovery software.
Predicting pharmacokinetic parameters and curves
Pharmacokinetics (PK) describes how the body affects a drug after administration. The concentration-time profile of a compound reflects its exposure,…
Deploying Cerella for active learning using deep learning imputation
In this webinar we demonstrated how this new platform provides interactive access to deep learning imputation to extract more value…
Multi-parameter optimisation in practice
This webinar describes example applications of multi-parameter optimisation to find high-quality lead compounds.
Large scale imputation of drug discovery data using deep learning
We presented a case study in which Alchemite was applied to a data set comprising approximately 700,000 compounds and 1,000…
AI-guided design of antimalarials with In vitro validation
In this webinar you can hear how a combination of generative chemistry and deep learning has impacted the design of…
Translational toxicology: data visualisation across phases
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.
Practical applications of deep learning to imputation of drug discovery data
In this webinar, we explore how the limitations of pharmaceutical data can impact conventional predictive model building. Our speakers, Julian Levell (Constellation pharmaceuticals), Ben Irwin and matt Segall (Optibrium) demonstrate how the deep learning imputation algorithm underlying our Cerella platform, overcomes these challenges.
pKa prediction using quantum mechanics and machine learning
The dissociation of a proton from a heteroatom has a significant influence on the charge distribution and interactions of a…
Chemical space navigation
In this webinar, presented by our guest, Dr. Franca Klingler from BioSolveIT, we learn how novel search algorithms have been…
Avoiding missed opportunities by analysing the sensitivity of our decisions
In this webinar we discussed how you can ensure that you don’t miss valuable opportunities due to the criteria used…
Private: Integrated AI synthesis prediction in drug discovery
Learn more about how AI, machine learning and other computational tools can support the discovery process, bringing you feasible synthetic routes to your target compounds.
