Chillin in Flatland

Innovation in the field of small molecule therapeutics is becoming increasingly difficult. The 'low hanging fruit' of well characterised, easy to drug targets have almost all been picked, yet the need to quickly develop potent new medicines is still a pressing issue. The recent Coronavirus pandemic, and the developing anti-microbial resistance crisis are two obvious examples of this need. Computational methods are emerging as impactful tools in medicinal chemistry and drug discovery, with the ability to screen and analyse vast libraries of compounds far faster than the laboratory scientist. This project aims to develop an open-source, computational tool that allows the investigation of heterocyclic isosteres to discover new cores and scaffolds for compound optimisation and the generation of novel chemical species. It has been proposed that 3D molecules stand a better chance of becoming successful drugs, however over half of the carbons in these '3D' molecules are sp2 hybridised, and therefore flat. Furthermore the synthetic chemistry required to selectively introduce each sp3 carbon is slow, and limited in its scope. Heteroaromatic systems are widely used, and their synthesis well understood in lead optimisation and drug discovery. Their physicochemical properties are more easily tuned, and they are often similar to cellular metabolites and signalling molecules. In 2009 Pitt et al. published VEHICLe, a database of 25 000 potentially accessible small heterocycles of which only 1100 have been reportedly synthesised. We propose to develop a computational tool (the HeteroCycle Isostere Explorer) to discover new heterocyclic cores for compound optimisation by comparing the shape and electrostatic potentials of input molecules to those of each member of the VEHICLe database, and returning the best bioisosteric replacements. This will allow access to new drug-like molecules quickly, but can also be used to determine underexplored areas of sp2 space, which could yield novel physicochemical properties in therapeutic lead compounds. This project would fall within the EPSRC chemical biology research area, and the software development within the computational chemistry area. The aim is to use the tool to generate molecules which would then be synthesised and assayed to test and refine its predictions, and this would then fall also within the synthetic organic chemistry and synthetic biology research areas. When sufficient experimental results have been accumulated, the goal is to develop and incorporate into the tool machine learning models, which can use experimental evidence to guide the predictions. This would then also fall within the AI for Science research area and theme.

The UK's world-leading position in biomedical research is critically dependent upon training scientists with the cutting-edge research skills and technological know-how needed to drive future scientific advances. Since 2009, the EPSRC and MRC CDT in Systems Approaches to Biomedical Science (SABS) has been working with its consortium of 22 industrial and institutional partners to meet this training need.

Over this period, our partners have identified a growing training need caused by the increasing reliance on computational approaches and research software. The new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 will address this need. By embedding a sustainable approach to software and computational model development into all aspects of the existing SABS training programme, we aim to foster a culture change in how the computational tools and research software that now underpin much of biomedical research are developed, and hence how quantitative and predictive translational biomedical research is undertaken.

As with all CDT Programmes, the future impact of SABS:R^3 will be through its alumni, and by the culture change that its training engenders. By these measures, our existing SABS CDT is already proving remarkably successful. Our alumni have gone on to a wide range of successful careers, 21 in academic research, 19 in industry (including 5 in SABS partner companies) and the other 10 working in organisations from the Office of National Statistics to the EPSRC. SABS' unique Open Innovation framework has facilitated new company connections and a high level of operational freedom, facilitating 14 multi-company, pre-competitive, collaborative doctoral research projects between 11 companies, each focused on a SABS student.

The impact of sustainable and open computational approaches on biomedical research is clear from existing SABS' student projects. Examples include SAbDab which resulted from the first-ever co-sponsored doctorate in SABS, by UCB and Roche. It was released as open source software, is embedded in the pipelines of several pharmaceutical companies (including UCB, Medimmune, GSK, and Lonza) and has resulted in 13 papers. The SABS student who developed SAbDab was initially seconded to MedImmune, sponsored by EPSRC IAA funding; he went on to work at Roche, and is now at BenevolentAI. Similarly, PanDDA, multi-dataset X-ray crystallographic software to detect ligand-bound states in protein complexes is in CCP4 and is an integral part of Diamond Light Source's XChem Pipeline. The SABS student who developed PanDDA was awarded an EMBO Fellowship.

Future SABS:R^3 students will undertake research supported by both our industrial partners and academic supervisors. These supervisors have a strong track record of high impact research through the release of open source software, computational tools, and databases, and through commercialisation and licensing of their research. All of this research has been undertaken in collaboration with industrial partners, with many examples of these tools now in routine use within partner companies.

The newly focused SABS:R^3 will permit new industrial collaborations. Six new partners have joined the consortium to support this new bid, ranging from major multinationals (e.g. Unilever) to SMEs (e.g. Lhasa). SABS:R^3 will continue to make all of its research and teaching resources publicly available and will continue to help to create other centres with similar aims. To promote a wider cultural change, the SABS:R^3 will also engage with the academic publishing industry (Elsevier, OUP, and Taylor & Francis). We will explore novel ways of disseminating the outputs of computational biomedical research, to engender trust in the released tools and software, facilitate more uptake and re-use.