EPSRC logo

Details of Grant 

EPSRC Reference: EP/S014128/1
Title: A Robot Chemist
Principal Investigator: King, Professor R
Other Investigators:
Procter, Professor DJ
Researcher Co-Investigators:
Project Partners:
Department: Computer Science
Organisation: University of Manchester, The
Scheme: Standard Research - NR1
Starts: 01 October 2018 Ends: 30 November 2019 Value (£): 243,280
EPSRC Research Topic Classifications:
Artificial Intelligence Chemical Synthetic Methodology
EPSRC Industrial Sector Classifications:
Information Technologies Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
05 Jun 2018 ASD - FS Interview Panel Announced
Summary on Grant Application Form
Eve is an artificially-intelligent 'Robot Scientist' designed to make drug discovery faster and much cheaper. She has already discovered that a compound used in soap and toothpaste might also be used in the fight against drug-resistant malaria, demonstrating its success. The proposal is to now give Eve the ability to do chemical reactions and to synthesise new compounds.

Eve inhabits an enclosure 2.5 meters in length, 2 metres wide, and 93 meters high. It consists of two robot arms, surrounded by equipment regularly found in laboratories for dispensing liquids into a large number of wells lined up on plastic plates, then incubating and testing them. But by integrating together instruments usually separated into different departments, Eve can do tests and interpret the results, and go on and use that knowledge in further tests faster. We will now give Eve the power to design and make her own, new compounds before testing their potential for drug discovery.

For the majority of medicines available today, scientists view drug molecules as nanometre-scale keys that slot into similarly sized protein or enzyme locks in cells in our bodies. Drug screening tests put these locks using biological systems that trigger a signal, such as a fluorescent flash, when a molecule fits into it like a key.

While pharmaceutical industry screening can identify positive signals known as hits, Eve is also independently able to follow up and check if the hits were true prospects, known as leads. But simply screening and following up hits is not where Eve's greatest promise for drug discovery lies. Instead, by learning from the results from those tests, Eve is able to do what it currently takes teams of chemists and biologists many months to hammer out. Drug researchers currently already use software that employs 'machine learning' to take screening results and create a 'quantitative structure-activity relationship'. This is a mathematical function that relates the composition, shape and properties like fattiness and electrical change of the molecules, to how good drugs they are likely to be. Using such models scientists choose which molecule to make and test next.

Currently, Eve can only learn to predict which out of a large set of ~15,000 compounds would be hits. The proposal is to add to Eve the ability to also synthesise novel compounds. In particular, we will program Eve to be able to carry out a chemical process known as 'late stage functionalization'. This is the introduction of a medicinally-relevant chemical group to existing drug-like molecules in Eve's library. This will enable Eve to make new chemical entities and to form an extended collection of drug-like molecules. We will program Eve to use machine learning to (1) Learn how to best to design drugs using late stage functionalization, and (2) learn which molecules are most likely to undergo successful late stage functionalization.

An important goal of our project, therefore, is for Eve to develop, optimize and 'road test' a new and important chemical process that will be of great use to molecule-makers around the world. However, the main project goal is to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs, and so potentially improve the lives of millions of people worldwide.

Key Findings
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Potential use in non-academic contexts
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Impacts
Description This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Summary
Date Materialised
Sectors submitted by the Researcher
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Project URL:  
Further Information:  
Organisation Website: http://www.man.ac.uk