  Selection of appropriate collective variables for enhancing sampling of
molecular simulations remains an unsolved problem in computational biophysics.
In particular, picking initial collective variables (CVs) is particularly
challenging in higher dimensions. Which atomic coordinates or transforms there
of from a list of thousands should one pick for enhanced sampling runs? How
does a modeler even begin to pick starting coordinates for investigation? This
remains true even in the case of simple two state systems and only increases in
difficulty for multi-state systems. In this work, we solve the initial CV
problem using a data-driven approach inspired by the filed of supervised
machine learning. In particular, we show how the decision functions in
supervised machine learning (SML) algorithms can be used as initial CVs
(SML_cv) for accelerated sampling. Using solvated alanine dipeptide and
Chignolin mini-protein as our test cases, we illustrate how the distance to the
Support Vector Machines' decision hyperplane, the output probability estimates
from Logistic Regression, the outputs from deep neural network classifiers, and
other classifiers may be used to reversibly sample slow structural transitions.
We discuss the utility of other SML algorithms that might be useful for
identifying CVs for accelerating molecular simulations.
