Time for a Step Change in Force Field Design

The literature shows that there is a serious problem at the heart of biomolecular simulation: it cannot reliably predict the structure and dynamics of proteins in aqueous solution. Thus, molecular dynamics simulation cannot usefully complement experiment while studying the onset of Alzheimer's disease. Popular force fields do not reliably reproduce the misfolding and aggregation of the intrinsically disordered amyloid beta peptide. Remedies usually add or take away ill-defined energy terms or repeatedly re-parameterise. Because this strategy has not solved the problem, force field architecture needs to be
overhauled. We propose FFLUX, a truly novel force field, that is much closer to the underlying quantum reality and one that "sees the electrons". FFLUX exploits a parameter-free definition of an atom inside a system. Using machine learning FFLUX learns how atomic energies, charges and multipole moments vary with the surrounding atoms' geometry. As such it captures all polarisation and many-body effects, as well as charge transfer, in one streamlined scheme. The approach avoids perturbation theory and thus benefits from a clear treatment of short-range interactions. Moreover, FFLUX breaks free from the rigid-body constraints of advanced polarisable force fields. The well-defined atom at the heart of FFLUX enables physics-based machine learning. It uses kriging instead of neural nets, thereby reducing the training data size. Our careful work plan is rooted in amino acids and water clusters, and scaled up to the solvated amyloid beta peptide via a sequence of increasingly relevant systems, both in gas-phase and in water. We will
introduce more sophisticated machine learning and implement state-of-art parallellisation on CPUs, GPUs and FPGAs, thereby offering a new user community the program DL_FFLUX. Bringing about a step change in biomolecular simulation is a huge task, even for this type of grant, but feasible as evidenced by proofs-of-concept from our group.