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

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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.