Rapid AI generation of optimised compound designs, guided by user interaction
Pairing AI with human expertise We present a novel AI compound optimisation system, designed to include human oversight as a…
Combining deep learning with multi-parameter optimisation to predict compounds with selective activity against a broadleaf weed species
Introduction The emergence of resistance and increased stringency of regulatory requirements have created a need for new agrochemicals. The long…
Explaining the ‘black box’: deep learning in drug discovery
Recent years have seen a remarkable rise in the number and scope of artificial intelligence and machine learning (especially deep…
Avoid the hype – how to successfully implement AI in drug discovery
AI has the potential to transform discovery. However, to ensure real impact, there are several practicalities that organisations must consider…
An augmented approach to generative chemistry
Generative molecular design provides new exciting avenues of chemical space exploration. But how can we use these methods effectively to assess many optimisation strategies and find the compounds destined for success in our projects?
Join Dr Matt Segall and Dr Michael Parker as they explore state-of-the-art generative chemistry, and discuss the importance of an augmented intelligence approach for successful discovery.
Perspectives on generative chemistry – potential and reality
Have advances in AI and deep learning reached a threshold whereby generative chemistry methods are redefining drug design? This webinar…
AI in early drug discovery: from promise to practice
In this webinar, Jeff Blaney (Senior Director of Discovery Chemistry, Genentech), Darren Green (Head of Cheminformatics & Data Science, GlaxoSmithKline), Julian Levell (Head of Discovery, New Equilibrium Biosciences), Matthew Segall (CEO, Optibrium) discuss the state of AI in early drug discovery from hit to preclinical candidate and share their experiences with and expectations of AI, including predictive modelling, synthesis prediction, and generative chemistry. Hear about the successes of AI drug discovery and an outlook on what AI needs to achieve to really transform the industry.
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
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.
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…
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.
Applying med chem transformations and multi-parameter optimisation
Summary This article on applying med chem transformations and multi-parameter optimisation describes the concepts and algorithms underlying StarDrop’s Nova module. We’ve developed…
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…
Practical applications of matched series analysis
This paper, co-authored with our colleagues at NextMove Software, explores applications of Matched Series Analysis within StarDrop’s Nova module to…
