FLF Next generation atomistic modelling for medicinal chemistry and biology
Lead Research Organisation:
Newcastle University
Department Name: Sch of Natural & Environmental Sciences
Abstract
The efficiency of drug discovery has been falling for decades such that, for each new molecule that reaches the consumer, estimated research and development costs are in excess of $2 billion. The drug discovery process involves the design of, usually, a small organic molecule that is capable of binding to its target in vivo with therapeutic benefit. Part of the problem is that during this pipeline too many molecules are synthesised in the lab, at great expense, that turn out not to have the required binding affinity. A computational model that is capable of reliably predicting binding from structure is needed.
Allied with structural biology methods, such as x-ray crystallography and cryo-electron microscopy, computational structure-based biomolecular simulation is an important part of the solution. At the simplest level, biomolecular simulation can be used to 'animate' the static pictures solved by structural biology, by finding the forces on the atoms, and hence solving for their dynamics. But we can also move beyond this, and use rigorous thermodynamics to predict the effects that changes in structures of potential drug molecules will have on binding to their target. Such approaches were employed by computational researchers all over the world during the COVID-19 pandemic to urgently provide new understanding of the SARS-CoV-2 virus and to design inhibitors of its function.
Although we know that the forces on the atoms should be calculated using the equations of quantum mechanics, these are much too computationally costly to solve routinely for biological problems. Instead the dynamics and interactions of biological molecules are typically computed using a simplified computational model, known as a force field. The force field models the atoms as bonded by springs, and interacting with each other through electrostatic and van der Waals forces. The strengths of these interactions are modelled by thousands of adjustable parameters, which have been tuned to reproduce experimental data. These force fields are an important enabling technology for biomolecular simulation scientists, and the accuracy of their predictions depends on the realism of the force field model and its parameters.
Traditionally, force field models evolved over periods of many decades. Design decisions taken early in the process became 'baked in', since re-training the model with new design rules was infeasible. The Open Force Field Initiative is an academic-industrial partnership aiming to advance the science and software infrastructure required to build the next generation of molecular mechanics force fields. In one example of our work from the first period of the Fellowship, we have co-developed a flexible framework to extend the Open Force Field software stack with custom force field models. In a proof-of-principle, we were able to train and test a new generalised force field model in a matter of weeks, rather than years, with improvements in accuracy over traditional force fields.
My vision for the renewal period of the Future Leaders Fellowship is to deploy this software infrastructure to rapidly move from new hypotheses to trained force field models, with unambiguous determination of the effects of design decisions on model accuracy. For example, I will test whether machine learning models trained on high-level quantum mechanical datasets yield accurate force field atomic charges, and whether accurate protein force field models can be built using the new force field models described above. Force field models that show requisite accuracy will be deployed in molecular design workflows. Through working with the project partners in the pharmaceutical industry and at an open science antiviral discovery initiative, I will showcase the accuracy improvements in structure-based biomolecular simulations that will translate to improved efficiency of the drug discovery pipeline.
Allied with structural biology methods, such as x-ray crystallography and cryo-electron microscopy, computational structure-based biomolecular simulation is an important part of the solution. At the simplest level, biomolecular simulation can be used to 'animate' the static pictures solved by structural biology, by finding the forces on the atoms, and hence solving for their dynamics. But we can also move beyond this, and use rigorous thermodynamics to predict the effects that changes in structures of potential drug molecules will have on binding to their target. Such approaches were employed by computational researchers all over the world during the COVID-19 pandemic to urgently provide new understanding of the SARS-CoV-2 virus and to design inhibitors of its function.
Although we know that the forces on the atoms should be calculated using the equations of quantum mechanics, these are much too computationally costly to solve routinely for biological problems. Instead the dynamics and interactions of biological molecules are typically computed using a simplified computational model, known as a force field. The force field models the atoms as bonded by springs, and interacting with each other through electrostatic and van der Waals forces. The strengths of these interactions are modelled by thousands of adjustable parameters, which have been tuned to reproduce experimental data. These force fields are an important enabling technology for biomolecular simulation scientists, and the accuracy of their predictions depends on the realism of the force field model and its parameters.
Traditionally, force field models evolved over periods of many decades. Design decisions taken early in the process became 'baked in', since re-training the model with new design rules was infeasible. The Open Force Field Initiative is an academic-industrial partnership aiming to advance the science and software infrastructure required to build the next generation of molecular mechanics force fields. In one example of our work from the first period of the Fellowship, we have co-developed a flexible framework to extend the Open Force Field software stack with custom force field models. In a proof-of-principle, we were able to train and test a new generalised force field model in a matter of weeks, rather than years, with improvements in accuracy over traditional force fields.
My vision for the renewal period of the Future Leaders Fellowship is to deploy this software infrastructure to rapidly move from new hypotheses to trained force field models, with unambiguous determination of the effects of design decisions on model accuracy. For example, I will test whether machine learning models trained on high-level quantum mechanical datasets yield accurate force field atomic charges, and whether accurate protein force field models can be built using the new force field models described above. Force field models that show requisite accuracy will be deployed in molecular design workflows. Through working with the project partners in the pharmaceutical industry and at an open science antiviral discovery initiative, I will showcase the accuracy improvements in structure-based biomolecular simulations that will translate to improved efficiency of the drug discovery pipeline.
