Innovative and Efficient Hit Identification Platform

The identification of new small molecule (hits or chemical probes) able to modulate difficult-to-target therapeutic proteins is still one of the major challenges for the biomedical community. These small molecules not only provide new understanding of biological targets and pathways related to a given disease (target identification/validation), but can also form the first step towards the development of a new medicine (hit identification). Conventionally, bioactive small molecules have been discovered by the screening of massive compound collections (commercial or academic), but, despite their enormous size, the probability of finding tractable hits against novel biological targets is reduced due to these collections only occupy restricted areas within the bioactive chemical space. Consequently, despite the significant economic resources and efforts devoted, hits resulting from these campaigns have limited chances of becoming clinical candidates. This project aims to address this problem by providing an innovative and efficient approach that combines the screening of a small diverse fragment library (generated on the basis of Diversity-Oriented Synthesis) through High-Throughput Crystallography (XChem facility at Diamond) against challenging proteins with the state-of-the-art techniques in computational chemistry (including artificial intelligence and deep learning technology) to identify and profile better-quality hits. This is a highly collaborative project that complies with the BBSRC remit; the success of this project will have a huge impact across bioscience research since this innovative approach can be applied to a wide range of biological targets.