34 research outputs found
Blind protein structure prediction using accelerated free-energy simulations.
We report a key proof of principle of a new acceleration method [Modeling Employing Limited Data (MELD)] for predicting protein structures by molecular dynamics simulation. It shows that such Boltzmann-satisfying techniques are now sufficiently fast and accurate to predict native protein structures in a limited test within the Critical Assessment of Structure Prediction (CASP) community-wide blind competition
Nuclear quantum effects in water
In this work, a path integral Car-Parrinello molecular dynamics simulation of
liquid water is performed. It is found that the inclusion of nuclear quantum
effects systematically improves the agreement of first principles simulations
of liquid water with experiment. In addition, the proton momentum distribution
is computed utilizing a recently developed open path integral molecular
dynamics methodology. It is shown that these results are in good agreement with
neutron Compton scattering data for liquid water and ice.Comment: 4 page
Efficient multiple time scale molecular dynamics: using colored noise thermostats to stabilize resonances
Multiple time scale molecular dynamics enhances computational efficiency by
updating slow motions less frequently than fast motions. However, in practice
the largest outer time step possible is limited not by the physical forces but
by resonances between the fast and slow modes. In this paper we show that this
problem can be alleviated by using a simple colored noise thermostatting scheme
which selectively targets the high frequency modes in the system. For two
sample problems, flexible water and solvated alanine dipeptide, we demonstrate
that this allows the use of large outer time steps while still obtaining
accurate sampling and minimizing the perturbation of the dynamics. Furthermore,
this approach is shown to be comparable to constraining fast motions, thus
providing an alternative to molecular dynamics with constraints.Comment: accepted for publication by the Journal of Chemical Physic
Roughness of molecular property landscapes and its impact on modellability
In molecular discovery and drug design, structure-property relationships and
activity landscapes are often qualitatively or quantitatively analyzed to guide
the navigation of chemical space. The roughness (or smoothness) of these
molecular property landscapes is one of their most studied geometric
attributes, as it can characterize the presence of activity cliffs, with
rougher landscapes generally expected to pose tougher optimization challenges.
Here, we introduce a general, quantitative measure for describing the roughness
of molecular property landscapes. The proposed roughness index (ROGI) is
loosely inspired by the concept of fractal dimension and strongly correlates
with the out-of-sample error achieved by machine learning models on numerous
regression tasks.Comment: 17 pages, 6 figures, 2 tables (SI with 17 pages, 16 figures