ArticleJournal of chemical theory and computation2025
TinkerModeller: An Efficient Tool for Building Biological Systems in Tinker Simulations.
Article in Journal of chemical theory and computation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- MAPLE: a machine-learning force-field-native platform for automated reaction modeling and enzyme design.Chemical science · 2026Article
- pyEF: A Python Framework for QM and QM/MM Atom-Wise Electric Field Analysis.Journal of chemical theory and computation · 2026Article
- Multiscale Simulation Guided Electric Field-Enhanced Ammonia Catalytic Cracking.ACS catalysis · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polarizable force fields advance our understanding of electrostatic interactions in molecular systems; however, their widespread application is limited by the complexity of required molecular modeling. We here present TinkerModeller (TKM), a versatile software package designed to streamline the construction of biological systems in the Tinker molecular simulation software. The core functionality of TKM lies in its capacity to generate input files for complex molecular systems and facilitate the conversion from classical to polarizable force fields. With a user-friendly, standalone script, TKM provides an intuitive interface that supports users from molecular modeling through to postanalysis, creating a comprehensive platform for molecular dynamics simulations within Tinker. Furthermore, TKM includes an electric field (EF) postanalysis module, introducing a novel approach that employs charge methods and point charge approximations for efficient internal EF estimation. This module offers a computationally low-demand solution for high-throughput EF estimation. Our work paves the way for broader, more accessible use of polarizable force fields within Tinker and introduces a new method for EF estimation, advancing our capacity to explore electrostatic effects in biological and materials science applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.