Improving QSAR Models for the Prediction of the Activities and Toxicity of Small Molecule Candidates

The project aims to significantly improve Quantitative Structure-Activity Relationship (QSAR) model prediction for small molecule drug activities and toxicity. The context of this research lies in the critical need to improve the reliability of QSAR models in drug development, as accurate predictions can expedite the identification of potential drug candidates, thus reducing costs and time associated with traditional experimental methods. The project's primary objectives involve the implementation of innovative methodologies such as curating noisy datasets using techniques such as Self-Training, Label Smoothing, and Class Prototyping. This process aims to enhance the quality of training data, thereby enhancing the robustness of QSAR models. Furthermore, the project leverages recent advancements in deep learning research to explore diverse molecular representations as inputs for QSAR models.

There have been many recent advancements in graph neural networks, both 2 and 3-dimensional, which have yet to be applied to graphical representations of molecules. The exploration of different molecular representations has the potential to uncover more intricate relationships between molecular structures and activities, leading to more accurate predictions. Additionally, the research incorporates activity cliffs (AC) prediction into QSAR modelling, in which only limited research has been done. Activity cliffs refer to pairs of structurally similar molecules that exhibit significant differences in binding affinity to a given target. By integrating AC prediction into QSAR models, the project aims to capture these subtle distinctions, further improving the predictive capabilities of the models.

In alignment with EPSRC's strategies, this project falls within the EPSRC Computational and Theoretical Chemistry research area. The endeavour resonates with the EPSRC's goal of promoting cutting-edge research in computational chemistry to drive advancements in drug discovery and other scientific domains. Notably, this research is conducted in collaboration with Lhasa Limited, which utilises a federated learning data set. Federated learning allows multiple organisations to collaborate without sharing sensitive data, enabling the training of machine learning algorithms across distributed, private datasets. This collaboration broadens the project's scope and impact, as well as providing potential paths for further investigation into both local model prediction and the use of local models within a broader federated model to generate consolidated predicted activity labels, without exposing any sensitive data.

In conclusion, the research project aims to enhance QSAR modelling through multiple innovative avenues, including data curation, diverse molecular representations, activity cliffs prediction, and federated learning. By aligning with EPSRC's research area and partnering with industry leader Lhasa Limited, this project has the potential to revolutionise the field of drug discovery and computational chemistry.

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.