Solving Molecular Conformation by Machine Learning and NMR

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Understanding the conformation of a drug molecule in solution is important to optimise the mechanism of a drug binding to its protein target. This means that computational molecular modelling is a crucial step in drug design. The best way to assign the conformation of a molecule is using NMR (by interpreting coupling constants and nuclear Overhauser effects, or NOEs). NMR requires sufficient, pure compound to analyse and even then, yields information of that molecule conformationally averaged. DFT is a very useful way to calculate NMR parameters for molecule or conformer of interest; however, DFT takes a long time and requires significant computational power which is expensive and highly energy consuming (thus negatively impacting the environment). Machine learning is a less energy-costly alternative. Machine learning is incredibly fast compared to DFT. Once trained, it can make predictions that might take days using DFT, in a matter of seconds. The quality of these predictions depends entirely on the information that the machine is trained on and in this case, the machine is trained on DFT-calculated NMR parameters, resulting in a model that can predict NMR parameters to near DFT-level accuracy. The Butts group at Bristol have published a model capable of doing just this. Currently, these models are able to predict molecular constitution and connectivity to a very high level. However, the objective of this PhD is to extend this into predicting conformational information. Just one molecule can have hundreds of different potential conformers which means that running DFT calculations on every conformer for a series of drug candidates uses a vast amount of computational time. This project falls within the EPSRC artificial intelligence and robotics research area since it involves the application of machine learning to chemistry with the potential to significant impact. Furthermore, it also falls within the EPSRC physical sciences field of research since it furthers the understanding of how molecules behave conformationally. The applications of the research are potentially very important since it could contribute a faster and cheaper way to sample compounds that could be developed into drugs, enhancing drug discovery and development.