Deep imputation on large-scale drug discovery data
OA paper outlining the practical applications of deep imputation on large-scale drug discovery data. It compares deep learning to traditional QSAR methods.
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.
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.
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.
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…
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…
Practical applications of deep learning to impute heterogeneous drug discovery data
This article outlines practical applications of deep learning on drug discovery data. It introduces some of the research behind our…
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.
Imputation of assay bioactivity data using deep learning
This article describes a novel deep learning neural network method and its application for the imputation of bioactivity data, such…
