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Understanding kinetics and thermodynamics profile of biomolecules isnecessary to understand their functional roles which has a major impact inmechanism driven drug discovery. Molecular dynamics simulation has beenroutinely used to understand conformational dynamics and molecular recognitionin biomolecules. Statistical analysis of high-dimensional spatiotemporal datagenerated from molecular dynamics simulation requires identification of fewlow-dimensional variables which can describe essential dynamics of a systemwithout significant loss of informations. In physical chemistry, theselow-dimensional variables often called collective variables. Collectivevariables are used to generated reduced representation of free energy surfaceand calculate transition probabilities between different metastable basins.However the choice of collective variables is not trivial for complex systems.Collective variables ranges from geometric criteria's such as distances,dihedral angles to abstract ones such as weighted linear combinations ofmultiple geometric variables. Advent of machine learning algorithms led toincreasing use of abstract collective variables to represent biomoleculardynamics. In this review, I will highlight several nuances of commonly usedcollective variables ranging from geometric to abstract ones. Further, I willput forward some cases where machine learning based collective variables wereused to describe simple systems which in principle could have been described bygeometric ones. Finally, I will put forward my thoughts on artificial generalintelligence and how it can be used to discover and predict collectivevariables from spatiotemporal data generated by molecular dynamics simulations.

Collective variable discovery in the age of machine learning: reality, hype and everything in between