Since it launched in 2018, Facebook's machine learning framework PyTorch has been put to good use, with applications ranging from powering Elon Musk's autonomous cars to driving robot farming projects. Now pharmaceutical firm AstraZeneca has revealed how its in-house team of engineers are tapping PyTorch too, and for equally as important endeavors: to simplify and speed up drug discovery. Combining PyTorch with Microsoft Azure Machine Learning, AstraZeneca's technology can comb through massive amounts of data to gain new insights about the complex links between drugs, diseases, genes, proteins or molecules. Those insights are used to feed an algorithm that can, in turn, recommend a number of drug targets for a given disease for scientists to test in the lab. The method could allow for huge strides in a sector like drug discovery, which so far has been based on costly and time-consuming trial-and-error methods.
Created by Microsoft in partnership with the Inria laboratory, the AI Factory is a co-innovation laboratory that links startups, the research world and the various players in the sector. Through these synergies, the AI Factory for Health projects will serve the patients and the medical profession to offer a renewed and individualized medicine. Startups selected to join the AI Factory for Health will benefit from AstraZeneca's know-how and expertise in pathologies, knowledge of patient needs and healthcare professionals, as well as legal expertise, access to institutions, and health data for the different European countries in which AstraZeneca operates.
AI has applications in many areas of research, including genomics. Slavé Petrovski of AstraZeneca reveals how AI is used in the study of the human genome and how it may evolve in the future. The field of genomics generates large datasets that are utilised in the discovery and development of potential new therapeutics. Artificial intelligence (AI) is highly valuable in this area of study as it accelerates the time it takes to get from information to insight. Drug Target Review's Victoria Rees spoke with Slavé Petrovski, Head of Genome Analytics and Informatics at AstraZeneca's Centre for Genomics Research (CGR) to discover how AI is used in this field.
Today, together with researchers at Kings College London, the Universities of Northumbria and Suffolk, and the Francis Crick Institute, AstraZeneca published a paper in Environmental Science and Technology calling for the wider application of machine learning in environmental toxicology research, to reduce the burden on animal testing and better meet the future challenges of scientific discovery. Environmental Protection, together with Access to Healthcare and Ethics and Transparency, is a key priority of the approach to sustainability at AstraZeneca. Our scientific approach to environmental sustainability reduces our environmental impact by protecting our air, land and water, reducing our dependence on natural resources and ensuring the environmental safety of our products. This publication is the result of an ongoing collaboration between AstraZeneca and academic partners, who have been working together to see how machine learning can help us to better understand the impact of chemicals on the environment. Pollution from contaminants continues to be a cause for concern, not only on the environment but also for public health.
AstraZeneca is linking up with DeepMatter, a big data firm focused on achieving reproducibility in chemistry, to help improve the productivity of its automated compound synthesis operations. DeepMatter's artificial intelligence-powered DigitalGlassware platform captures real-time data from a multisensor probe placed within the reaction vessel--factors such as temperature, pressure, ultraviolet light levels and more, as well as taking measurements from the ambient environment. Combined with data on solvents, catalysts and reagents, the system monitors, records and analyzes the individual steps necessary to synthesize pharmaceutical compounds. "We've been impressed with the automated chemistry platforms developed at AstraZeneca sites for autonomous delivery of new lead series," DeepMatter CEO Mark Warne said in a statement. "We see an opportunity to draw together knowledge from the DigitalGlassware platform to enable machine learning and AI technologies to increase the certainty of producing a high quality and choice of candidate drug molecules," Warne said.