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Abstract: Having an accurate reaction coordinate (RC) is essential for reliable kinetic characterization of molecular processes, but there are few quantitative metrics to evaluate RC quality. In this study, we consider the dimensionless γ metric from the Exponential Average Time-dependent Rate (EATR) method, which represents the fraction of a biasing potential along the RC that contributes to increasing the rate constant. We demonstrate that γ can be used to test whether the utility of a RC for predicting kinetics with a Metadynamics bias improves as the coordinate is iteratively updated to include new data. We evaluate RCs approximated via the iterative State Predictive Information Bottleneck (SPIB) approach, which was previously shown to be accurate across six protein-ligand dissociation systems. For these same systems, we compute γ values and mean accelerated times τ̅_accel. After systematically scanning over fitting parameters, the results show that γ increases closer to 1, while τ̅_accel decreases, revealing a consistent inverse correlation. These results demonstrate that γ serves as a practical criterion for RC evaluation and offers guidance for selecting SPIB-derived coordinates yielding quantitative kinetic predictions.

Abstract: Molecular dynamics simulations hold great promise for providing insight into the microscopic behavior of complex molecular systems. However, their effectiveness is often constrained by long timescales associated with rare events. Enhanced sampling methods have been developed to address these challenges, and recent years have seen a growing integration with machine learning techniques. This Review provides a comprehensive overview of how they are reshaping the field, with a particular focus on the data-driven construction of collective variables. Furthermore, these techniques have also improved biasing schemes and unlocked novel strategies via reinforcement learning and generative approaches. In addition to methodological advances, we highlight applications spanning different areas, such as biomolecular processes, ligand binding, catalytic reactions, and phase transitions. We conclude by outlining future directions aimed at enabling more automated strategies for rare-event sampling.

Abstract: Selecting appropriate collective variables (CVs) is a crucial bottleneck in enhanced sampling molecular dynamics simulations. Although progress has been made with data-driven and intuition-based approaches, optimal CVs remain system-specific. Meanwhile, simple geometric descriptors are still widely used due to their transferability. A promising, yet under-explored, candidate for a more efficient CV is solvation. Indeed, despite its central role in ligand binding and folding, the complexity of solvent behavior has hindered its widespread use. Here, we introduce a data-driven and automatic strategy to construct robust solvation-based CVs. Our method identifies critical hydration sites by analyzing the radial distribution function of water around a ligand. Remarkably, using only these hydration CVs within on-the-fly probability enhanced sampling simulations, we successfully converge the binding free energy landscapes for a series of host–guest systems. These landscapes show excellent agreement with those from more computationally expensive benchmark methods. We further demonstrate that the choice of where to bias water is key to efficient convergence, providing clear guidelines for implementation. This work not only underscores the central role of water in molecular recognition but also offers a powerful and generalizable framework for enhancing the sampling of complex biomolecular events.

TL;DR: Researchers develop a data-driven method to construct solvation-based collective variables, enabling efficient convergence of ligand binding free energy landscapes using on-the-fly probability enhanced sampling simulations, highlighting water's central role in molecular recognition.