Applications of Deep Learning for Binding Affinity Prediction

点击翻译按钮获取中文摘要

Background:By applying computational methods to biochemistry and developing a deeper understanding of biological systems, the efficiency of the drug discovery process, and consequently patient outcomes, could be improved. A core issue in computational chemical biology is predicting whether small molecules will bind to a protein target, reproducing the accuracy observed in experimental binding assays. In recent years, machine learning has provided substantial improvements in protein-ligand binding prediction; however, current predictors still leave much to be desired. Novel artificial intelligence methods, such as deep learning, have the potential to transform healthcare by solving issues like binding affinity prediction.Aims and objectives:To improve the generalisability of machine learning-based computational chemistry tools for predicting protein-ligand binding affinity.To explore various molecular representations with the aim of understanding how best to interpret biological systems computationally, in particular protein-ligand binding.To investigate the applicability of novel AI methods to drug discovery on protein targets, such as hERG.Novelty of the research methods:We propose investigating novel applications of deep learning, including Graph Neural Networks, Atom Centred Symmetry Functions, and Large Language Models to the fundamental problem of protein-ligand binding affinity prediction. We will assess the ability of models to learn the underlying physics, including investigating improved benchmarks, of binding interactions and explore key aspects of generalisability and non-additivity. Model applications will include predicting off-target binding to proteins involved in the absorption, distribution, metabolism, and excretion of small molecule drugs, such as hERG and cytochrome P450s, as well as other targets of interest to the partner(s). Various molecular representations and approaches will be investigated, including proteo-chemometric 1D (SMILES for small molecules; amino acid sequences for proteins), 2D (graphs for ligands, proteins, and their interactions), as well as 3D (equivariant neural networks).Alignment to EPSRC's strategies and research areas:This multidisciplinary project aligns with EPSRC's strategic delivery plan by nurturing industry and university partnerships for effective research within the UK; pursuing discovery research within the Physical and Mathematical Sciences; and targeting mission-inspired research to transform Health and Healthcare with AI. This interdisciplinary bioinformatics project spans multiple research areas within the EPSRC remit. This project falls within the EPSRC "Artificial intelligence technologies", "Biological informatics", "Chemical biology and biological chemistry", "Computational and Theoretical Chemistry" research areas.