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PEAS: An Application for Autonomous Precision Conformation Sampling.

Mithony Keng1, Kenneth M Merz1,2

  • 1Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States.

Journal of Chemical Information and Modeling
|November 5, 2025
PubMed
Summary
This summary is machine-generated.

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PEAS is a new Python application that simplifies molecular modeling workflows. It automates charge state prediction and conformation sampling for ion mobility mass spectrometry, improving efficiency and accuracy.

Area of Science:

  • Computational chemistry
  • Computational biology
  • Biophysics

Background:

  • Molecular modeling is crucial in computational chemistry and biology.
  • Advancements in hardware and software improve accuracy and affordability.
  • Selecting optimal modeling workflows can be challenging due to numerous available tools.

Purpose of the Study:

  • To develop a user-friendly application for streamlining molecular modeling workflows.
  • To automate the process of assigning chemical structures to experimental ion mobility mass spectrometry collisional cross-section (CCS) values.
  • To simplify complex multi-step modeling processes for researchers.

Main Methods:

  • Developed PEAS (precise ensemble autonomous sampling), an open-source Python application.

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  • Integrated existing validated engines: SEER for charge state prediction, Confab for conformation generation, and CCS Focusing for conformer filtering.
  • Streamlined the workflow through vertical modeling engine integration to minimize user intervention.

    • Main Results:

    • PEAS effectively automates key steps in molecular modeling, including charge state determination and conformation sampling.
    • The application integrates multiple validated engines, ensuring efficiency and accuracy.
    • Unified performance of integrated engines delivers outcomes comparable to individual tool performance.

    Conclusions:

    • PEAS offers a user-friendly solution for complex molecular modeling tasks.
    • The application streamlines the assignment of chemical structures to experimental CCS values.
    • PEAS enhances the accessibility and efficiency of computational chemistry and biology research.