Next generation atomistic modelling for medicinal chemistry and biology

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.

This Fellowship will close the accuracy gap between quantum mechanical modelling and the approximate classical force fields used at the forefront of biomolecular modelling. It provides a key underpinning technology for understanding the roles of biological molecules in human health and disease, and for the rational structure-based design of new medicines. As such, it is important to me that my Fellowship takes an open science, open data approach to knowledge generation. By effectively helping to democratise force field parameterisation, design and dissemination, I will improve the culture of inclusivity in the field of computational modelling.

1) Economic Impact. Analysis of research and development costs in the pharmaceutical industry places the price of each new drug that reaches the consumer at $1.8bn, of which $0.4bn is spent on improving binding affinity between the molecule and its therapeutic target whilst minimising off-target effects (hit-to-lead optimisation). This Fellowship will have the following commercial benefits in the pharmaceutical industry:

- Within 4 years, the technology developed in this Fellowship will allow medicinal chemistry researchers to accurately screen hundreds of potential drug candidates on the computer and only synthesise in the lab those predicted to strongly bind to their target, thus increasing the efficiency of hit-to-lead optimisation and reducing experimental workload. This will drastically reduce costs in the pharmaceutical industry.

- I will further develop computational methods to begin to address therapeutic targets that are traditionally considered "undruggable". Within 4 years, I will develop a general approach incorporating atomistic modelling with deep learning for knowledge-based design of peptide libraries against protein targets, which may have hidden allosteric binding pockets. My project partners have many such targets in their discovery pipeline in oncology and neurodegenerative disease areas.

- Many next-generation medicinal technologies are not currently amenable to computational study. For example, the design of drugs against proteins with metals in their binding sites, and molecules for light-induced drug delivery, photodynamic therapy and targeted nanomedicines. To address this issue, within 7 years, I will develop automated and accurate computational methods to model metals in biology and molecules in electronically excited states in complex environments, thus providing underpinning technologies for new industrial sectors.

2) Skills Generation

- This Fellowship will provide a source of post-doctoral researchers and research software engineers with combined expertise in data science, medicinal chemistry and scientific computing. These skills will be highly sought after as the UK pharmaceutical industry enhances the roles of molecular modelling and AI in drug discovery.

3) Wider Society

- In the longer term (7-10 years), new medicines will be designed with substantial computational input, ultimately resulting in the improved health and wellbeing of UK citizens. Wider society will benefit from an accelerated and larger pipeline of effective drugs. This will be particularly important as focus shifts to personalised medicines that will require accurate predictive molecular simulation to feed into multi-scale models that are able to determine links between single amino acid substitutions and disease.

- This Fellowship will inspire the next-generation of researchers. School students in the North East will benefit from hands-on virtual reality demonstrations of the drug discovery process, and will gain an appreciation of local success stories through examples of medicines developed in Newcastle.

- Young people who may already be considering a career in research will benefit through our collaborations with the Journal of Sketching Science (with readership numbers of 100K+), which will produce visually appealing articles describing our research.