Next generation atomistic modelling for medicinal chemistry and biology

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Nobel Laureate Richard Feynman in his Lectures on Physics famously remarked that "...everything that living things do can be understood in terms of the jigglings and wigglings of atoms". This deceptively simple statement highlights the difficulty that structural biologists, medicinal chemists and computational scientists are faced with when attempting to understand human health and disease. We are used to thinking about a static, isolated picture of objects at the atomic scale, but often it is the dynamics (the "jigglings and wigglings") of the system and its environmental interactions that determine the underlying science, such as the role of intrinsically disordered proteins in neurodegenerative diseases or the possible link between quantum entanglement and molecular vibrations in biological photosynthesis.Twentieth century science not only set the challenge of studying life at the level of the structure and dynamics of atoms, but also provided (in theory) the solution, through the laws of quantum mechanics and the famous Schroedinger equation. Quantum mechanics explains the fundamental behaviour of matter at the atomic scale, and smaller. It enables scientists to make predictions about materials that are inaccessible to experiment, such as the structure of solid hydrogen in a star's core. At a more everyday level, quantum mechanics is routinely used by researchers in the microelectronics and renewable energy industries to rapidly scan multitudes of hypothetical materials compositions. In this way, the costly manufacturing process of the new materials need only begin once the desired properties have been predicted.However, quantum mechanics does not directly enable scientists to understand the biomolecular origins of disease, or to design new medicines to combat it. The reason for this comes down to Feynman's statement. It is infeasible to solve (even approximate) equations of quantum mechanics for the length and time scales sufficient to model all of the atomistic movements that need to take place, for example, for a drug molecule to find its target. Instead, computational chemists use a much simplified computational model, known as a force field, to estimate the dynamics of atoms. The force field models the atoms as bonded together in a molecule by springs, and interacting with other atoms through electrostatic and van der Waals forces, which are much stronger than gravity at the atomic scale. The strengths of these interactions are modelled by thousands of adjustable parameters, which have been manually tuned to reproduce experimental data over a period of many decades. We are reaching a stagnation point where accuracy is urgently needed for computer-aided design of new medicines, but parameter tuning delivers only small improvements.My vision for this UKRI Future Leaders Fellowship is to build a multi-disciplinary team that will work together to close the accuracy gap between quantum mechanics, and the approximate force fields used in biology and medicine. By working with international coding efforts, I will build the theory and software infrastructure required to dispense with these adjustable force field parameters, and instead derive them directly for the system under study, such as a protein implicated in disease. This will enable me to build more accurate computational models of the electrostatic and van der Waals interactions that determine the strength of binding of potential drugs to their targets. By crossing disciplinary boundaries to train in data science and machine learning, I will deploy the expertise that has been made famous for its applications in face and speech recognition, to create a spectrum of tools for speeding up the assignment of parameters and improving the accuracy of force field design. Finally, by undertaking secondments in the pharmaceutical industry, I will ensure that the developed methods will be used for the cost efficient design of the next generation of medicines.