Combination Automation and Predictive Machine Learning Makes Drug Discovery Go FAST

A team from the University of Cambridge has unveiled a pioneering platform that combines automated experimentation with Machine Learning (ML) to predict chemical reactions, potentially accelerating the development of new drugs.

The need for accurate prediction of molecular interactions is crucial in the discovery and manufacture of new drugs. Historically, this process has been largely reliant on trial and error, often leading to unsuccessful reactions. The traditional approach, involving the simulation of electrons and atoms in simplified models, is not only computationally intensive but also prone to inaccuracies.

In response to these challenges, the team at Cambridge has pioneered a data-driven method that draws inspiration from genomics. This approach, which combines high-speed automated experiments with advanced machine learning techniques, offers a quicker and more efficient way to understand chemical reactivity. The method, known as the ‘chemical reactome’, has undergone rigorous validation with a dataset of over 39,000 reactions relevant to pharmaceuticals.

The research, which is a collaborative effort with Pfizer and reported in Nature Chemistry, represents a significant shift in the approach to organic chemistry. According to the researchers, the reactome could greatly expedite the production of pharmaceuticals and other valuable products by providing a deeper understanding of chemical interactions.

The reactome methodology is adept at identifying correlations between various reaction elements and outcomes, as well as pinpointing data gaps. It leverages high-throughput automated experiments to generate its data, aiming to provide a more comprehensive understanding of chemical reactions beyond what is observed in initial experiments.

In a parallel study published in Nature Communications, the Cambridge team has also developed a machine learning model that facilitates precise molecular transformations. This model is particularly useful in late-stage functionalisation reactions, which are complex and often unpredictable. By accurately predicting the reaction sites under varying conditions, this model allows chemists to make precise modifications to complex molecules without having to start from the beginning.

The researchers believe that their approach, which overcomes the challenges posed by the limited data available in chemical research, could lead to significant advancements beyond the realm of late-stage functionalisation. Their work, supported in part by Pfizer and the Royal Society, is a testament to the potential of integrating machine learning into chemistry, opening new avenues in the field of drug development.

Staff Writer

Our in-house science writing team has prepared this content specifically for Lab Horizons

Leave a Reply

Your email address will not be published. Required fields are marked *