In the vital search for active compounds, there is great value in any approach that can focus experimental programs on the likeliest success routes, or that can deliver breakthrough insights. Machine learning is one such technology, with the potential to extract more value from existing data in order to guide drug discovery programs. But this data is often sparse and noisy – posing challenges to conventional machine learning and data analysis approaches. The Alchemite™ technology can master such data and is helping to reduce costs and optimise the drug discovery process.
Finding a novel antimalarial compound
In work now published in the Journal of Medicinal Chemistry, Intellegens participated in a global challenge organised by the Open Source Malaria consortium to design new antimalarial compounds with a novel mechanism of action, despite the sparsity of available experimental bioactivity data. The active compound identified by Alchemite™ deep learning show to have good potency when synthesised and tested. Predictions from Alchemite™ outperformed alternative machine learning approaches assessed by the consortium.
Predicting pharmacokinetics parameters and curves
In work now published in Molecular Pharmaceutics, the Alchemite™ method was applied to the prediction of PK parameters, based on compound structure and sparse in vitro data from AstraZeneca. This work can reduce time, cost, and number of animal studies in late-stage drug discovery.
Optimising kinase profiling programmes
A project at Genentech applied the Alchemite™ method to optimise kinase profiling programmes. The data set studied consisted of approximately 650 kinases and 10,000 compounds, significantly outperforming state-of-the-art quantitative structure-activity relationship (QSAR) approaches, including multi-target deep learning.
Alchemite™ succeeds on global pharma data
A peer-reviewed study has been published in Applied AI Letters. Working with Takeda’s proprietary global dataset, the project team demonstrated the ability of machine learning imputation to reduce cost and improve success rates of drug discovery.
Drug discovery partnership with Optibrium
Intellegens partners with Optibrium to embed the Alchemite™ deep learning algorithms within Cerella™ – a software platform for active learning in drug discovery. Cerella™ users can gain more value from compound data, capturing complex relationships between structures, activities and other properties on a scale that cannot be achieved using conventional tools and supporting chemical space exploration beyond conventional QSAR models. Cerella™, powered by Alchemite™, increases confidence in decision making and lead optimisation and enables you to reduce costs and accelerate discovery cycles by targeting experimental resources more effectively.
Intellegens publications on drug discovery
Find out more about the science behind the Alchemite™ methodology for drug discovery:
Alchemite™ for small molecule drug discovery
Alchemite™ technology enables you to:
- Fill gaps in your compound data, avoiding missed opportunities
- Capture complex relationships between structures, activities, and other properties
- Use accurate uncertainty estimates to prioritise compounds most likely to succeed
- Focus your experimental resources on the most valuable measurements
- Explore chemical space beyond conventional QSAR models
- Standardise and share data and models while ensuring rigorous security for your IP