Topologically and Geometrically Inspired Machine Learning for Drug Design

Drug design is immensely time-consuming and expensive. The largest pharmaceutical companies annually spend billions on the research and development of new drugs. A key area of such research is the investigation of biologically active small molecules. Various computer-aided drug design methods have emerged to improve the efficiency of the search for these active small molecules. In particular, shape-based ligand methods, which use the three-dimensional shape of the ligands themselves for predicting activity, are an attractive approach to the task because the shape of a ligand is important for ligand-protein binding.

The recent field of topological data analysis (TDA) uses tools from algebraic topology to analyse high-dimensional datasets with many promising recent results. Techniques from TDA are well suited to analysing the shape of point clouds which makes it a natural candidate for shape-based drug discovery.

This project aims to take inspiration from computational topology and geometry to create novel shape-based methods for computer-aided drug design. This project will generate simplicial complexes from the point clouds of molecules which will be used to make topological and geometric features. These features will then be fed as inputs to a machine learning pipeline which will be designed to classify active and inactive molecules. Any machine learning pipeline that classifies molecular activity should be invariant under the three-dimensional special Euclidean group, SE(3), since molecular activity is unaffected by the translations and rotations of a molecule. Therefore, this project will take advantage of techniques from the new field of geometric deep learning to create an invariant or equivariant classification pipeline. Data fusion, combining geometric features with chemical descriptors, will also be investigated as a potential way to improve the predictive power of the classification method. The popular DUD-E and the more recent LIT-PCBA virtual screening benchmarking data sets will be used to validate the created methods.
This research is aligned with the following EPSRC research areas: artificial intelligence technologies, geometry and topology, statistics and applied probability.
This work will be done in collaboration with Oxford Drug Design.

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.