MLCV: Bridging Machine-Learning-Based Dimensionality Reduction and Free-Energy Calculation

Importance-sampling
algorithms leaning on the definition of a model
reaction coordinate (RC) are widely employed to probe processes relevant
to chemistry and biology alike, spanning time scales not amenable
to common, brute-force molecular dynamics (MD) simulations. In practice,
the model RC often consists of a handful of collective variables (CVs)
chosen on the basis of chemical intuition. However, constructing manually
a low-dimensional RC model to describe an intricate geometrical transformation
for the purpose of free-energy calculations and analyses remains a
daunting challenge due to the inherent complexity of the conformational
transitions at play. To solve this issue, remarkable progress has
been made in employing machine-learning techniques, such as autoencoders,
to extract the low-dimensional RC model from a large set of CVs. Implementation
of the differentiable, nonlinear machine-learned CVs in common MD
engines to perform free-energy calculations is, however, particularly
cumbersome. To address this issue, we present here a user-friendly
tool (called MLCV) that facilitates the use of machine-learned CVs
in importance-sampling simulations through the popular Colvars module.
Our approach is critically probed with three case examples consisting
of small peptides, showcasing that through hard-coded neural network
in Colvars, deep-learning and enhanced-sampling can be effectively
bridged with MD simulations. The MLCV code is versatile, applicable
to all the CVs available in Colvars, and can be connected to any kind
of dense neural networks. We believe that MLCV provides an effective,
powerful, and user-friendly platform accessible to experts and nonexperts
alike for machine-learning (ML)-guided CV discovery and enhanced-sampling
simulations to unveil the molecular mechanisms underlying complex
biochemical processes.