What does this research mean for the field?

A continuous-time Markov chain constructed from a static, Boltzmann-weighted ensemble can accurately infer molecular kinetics and reproduce MD-derived rate matrices and time scales with only a short MD calibration step. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To construct a framework that infers physically realistic kinetic trajectories and rate matrices directly from static, Boltzmann-weighted molecular ensembles without extensive molecular dynamics simulations.

September 8, 2026Journal of Chemical Information and Modeling

DIME: Dynamics Inferred from Monte Carlo Ensembles via Continuous-Time Markov Chains

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Authors

KMKrishna Praneet Mulukutla MKMarimuthu Krishnan

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Overview

Computational study demonstrates kinetic trajectory inference from static Monte Carlo ensembles across six biomolecular systems, indicating accurate rate estimation without long simulations.

Key Points

To construct a framework that infers physically realistic kinetic trajectories and rate matrices directly from static, Boltzmann-weighted molecular ensembles without extensive molecular dynamics simulations.
Clustered static Monte Carlo ensembles into metastable states to define stationary distributions and built a rate matrix enforcing detailed balance with barrier heights extracted from the free energy surface.
Calibrated the absolute physical timescale by matching the slowest relaxation mode against a short molecular dynamics reference trajectory.
Validated the framework across six systems ranging from simple peptides (n-pentane, alanine dipeptide, WLALL, AIB9) to wild-type chignolin and bovine pancreatic trypsin inhibitor, including metadynamics simulations.
Inferred continuous-time Markov models reproduced molecular dynamics rate matrices, mean first-passage times, and implied timescales across all benchmark systems after short timescale calibration.
Verified kinetic symmetry in the achiral peptide AIB9 without reference data and captured millisecond native-state dynamics in bovine pancreatic trypsin inhibitor.
Demonstrated that the approach works effectively with free energy surfaces derived from enhanced sampling techniques such as well-tempered metadynamics.

Cite This Study

Mulukutla et al. (2026) studied this question.

synapsesocial.com/papers/6aa0091858e84d0ff5b47b25 https://doi.org/10.1021/acs.jcim.6c01396

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September 8, 2026Journal of Chemical Information and Modeling

DIME: Dynamics Inferred from Monte Carlo Ensembles via Continuous-Time Markov Chains

View Full Paper

Ask AI

Bookmark

Authors

KMKrishna Praneet Mulukutla MKMarimuthu Krishnan

Discussion

Member takes

Overview

Computational study demonstrates kinetic trajectory inference from static Monte Carlo ensembles across six biomolecular systems, indicating accurate rate estimation without long simulations.

Key Points

To construct a framework that infers physically realistic kinetic trajectories and rate matrices directly from static, Boltzmann-weighted molecular ensembles without extensive molecular dynamics simulations.
Clustered static Monte Carlo ensembles into metastable states to define stationary distributions and built a rate matrix enforcing detailed balance with barrier heights extracted from the free energy surface.
Calibrated the absolute physical timescale by matching the slowest relaxation mode against a short molecular dynamics reference trajectory.
Validated the framework across six systems ranging from simple peptides (n-pentane, alanine dipeptide, WLALL, AIB9) to wild-type chignolin and bovine pancreatic trypsin inhibitor, including metadynamics simulations.
Inferred continuous-time Markov models reproduced molecular dynamics rate matrices, mean first-passage times, and implied timescales across all benchmark systems after short timescale calibration.
Verified kinetic symmetry in the achiral peptide AIB9 without reference data and captured millisecond native-state dynamics in bovine pancreatic trypsin inhibitor.
Demonstrated that the approach works effectively with free energy surfaces derived from enhanced sampling techniques such as well-tempered metadynamics.

Cite This Study

Mulukutla et al. (2026) studied this question.

synapsesocial.com/papers/6aa0091858e84d0ff5b47b25 https://doi.org/10.1021/acs.jcim.6c01396

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