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We propose a generative model-based framework for learning collective variables (CVs) that faithfully capture the individual metastable states of the fulldimensional molecular dynamics (MD) systems. Unlike most existing approaches based on various feature extraction strategies, the new framework transfers the exhausting efforts of feature selection into a generative task of reconstructing the full-dimensional probability density function (PDF) from a set of CVs under a prior distribution with pre-assigned local maxima. By pairing the CVs with a set of auxiliary Gaussian random variables, we seek an invertible mapping that recovers the full-dimensional PDF and meanwhile, preserves the correspondence between the metastable states of the MD space and individual local maxima of the prior distribution. Through identifying the metastable states within MD space that are generally unknown and imposing the correspondence between the two spaces, the constructed CVs retain clear physical interpretations and provide kinetic insight for the molecular systems on the collective scale. We demonstrate the effectiveness of the proposed method with the alanine dipeptide in the aqueous environment. The constructed CVs faithfully capture the essential metastable states of the full MD systems, which show good agreement with kinetic properties such as the transition from the ballistic to the plateau regime for the mean square displacement.
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An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions
Determining collective variables (CVs) for conformational transitions is crucial to understanding their dynamics and targeting them in enhanced sampling simulations. Often, CVs are proposed based on intuition or prior knowledge of a system. However, the problem of systematically determining a proper reaction coordinate (RC) for a specific process in terms of a set of putative CVs can be achieved using committor analysis (CA). Identifying essential degrees of freedom that govern such transitions using CA remains elusive because of the high dimensionality of the conformational space. Various schemes exist to leverage the power of machine learning (ML) to extract an RC from CA. Here, we extend these studies and compare the ability of 17 different ML schemes to identify accurate RCs associated with conformational transitions. We tested these methods on an alanine dipeptide in vacuum and on a sarcosine dipeptoid in an implicit solvent. Our comparison revealed that the light gradient boosting machine method outperforms other methods. In order to extract key features from the models, we employed Shapley Additive exPlanations analysis and compared its interpretation with the “feature importance” approach. For the alanine dipeptide, our methodology identifies ϕ and θ dihedrals as essential degrees of freedom in the C7ax to C7eq transition. For the sarcosine dipeptoid system, the dihedrals ψ and ω are the most important for the cisαD to transαD transition. We further argue that analysis of the full dynamical pathway, and not just endpoint states, is essential for identifying key degrees of freedom governing transitions.
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- Award ID(s):
- 1955381
- PAR ID:
- 10528889
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- The Journal of Chemical Physics
- Volume:
- 159
- Issue:
- 3
- ISSN:
- 0021-9606
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1063/5.0147597
