Recent advances in the potential energy landscapes approach are highlighted, including
both theoretical and computational contributions. Treating the high dimensionality of …

Kinetic transition networks can now be calculated for small proteins using geometry
optimisation to characterise minima, transition states and pathways, and unimolecular rate …

By using principal component analysis (PCA) to examine the molecular dynamics (MD) of
protein folding trajectories, generated with the coarse-grained UNRES force field, for the B …

Machine learning techniques are being increasingly used as flexible non-linear fitting and
prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as …

Abstract Analysis of an intrinsically disordered protein (IDP) reveals an underlying
multifunnel structure for the energy landscape. We suggest that such 'intrinsically …

The potential energy landscape perspective provides both a conceptual and a
computational framework for predicting, understanding and designing molecular properties …

This report advances the hypothesis that multifunctional systems may be associated with
multifunnel potential and free energy landscapes, with particular focus on biomolecules. It …

This perspective focuses on conceptual and computational aspects of the potential energy
landscape framework. It has two objectives: first to summarise some key developments of …

Computing accurate reaction rates is a central challenge in computational chemistry and
biology because of the high cost of free energy estimation with unbiased molecular …

Markov state models have become popular in the computational biochemistry and
biophysics communities as a technique for identifying stationary and kinetic information of …