Development of machine-learning methods for the optimization of binding selectivity for early stage drug discovery

This project focuses on the development of novel computational methods to support the drug development process in order to reduce the large amount of compounds needed to optimize a pre-clinical drug candidate. Specifically, the focus is on developing machine learning models to guide medicinal chemists in order to choose compounds more efficiently when optimizing drugs for binding selectivity or promiscuity. In recent years, the "one drug one target" model has been challenged, resulting in a shift in emphasis of optimizing drugs towards desired polypharmacology patterns, which requires the drug compound to bind to multiple different protein targets. One example where binding promiscuity of a drug is highly desired are bacterial metallo-beta-lactamase (MBL) inhibitors. This class of compounds is designed to tackle antibiotic resistances. A specific selectivity pattern is required where drug candidates should be active against as many different bacterial MBLs as possible while not affecting human MBLs. Currently, the development of multi-target MBL inhibitors is ongoing and no single inhibitor that hits the four most important bacterial MBL targets[1]: VIM-1, VIM-2, IMP-1 and NDM-1 is known. The first goal of this project is the development of computational methods to design new compounds with the desired selectivity pattern against the MBL protein family as well as the direct validation of those models in the lab. Ultimately, the goal is to create a methodology which can be used to predict and optimize binding selectivity of drug compounds for different protein families and can act as a general guideline for chemists to create selective or promiscuous compounds. As such, the project aims to introduce two major novelties: first, the machine learning method itself, which will be used to suggest new compounds to make in the lab; and second, the synthesis of a new MBL inhibitor that is effective against all desired bacterial MBL targets. The development of new antibiotics or antibiotic resistance inhibitors is desperately needed in a world where antibacterial resistance is increasing steadily and the development of treatments uneconomical for the pharmaceutical industry. Furthermore, the focus on polypharmacology and the increasing cost of drug discovery in the pharmaceutical industry calls for the development of new methods for the design of selective compounds in order to make the process cheaper and faster.

This project therefore falls within the EPSRC "Artificial Intelligence Technologies", "Chemical Biology and Biological Chemistry" and "Computational & Theoretical Chemistry" research areas and is done in collaboration with Prof. Schofield at the Department of Organic Chemistry at the University of Oxford as well as in collaboration with the pharmaceutical company Glaxo-Smith-Kline.