The Open Molecular Software Foundation (OMSF) and the Growing Role of Open Source Software in Molecular Modeling

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Authors: Karmen Čondić-Jurkić, Irfan Alibay, Woody Sherman, Mallory R. Tollefson, W. Patrick Walters, Zachary Baker, Lillian T. Chong, Jennifer N. Wei, Jeffrey J. Gray, Brian D. Weitzner, Daniel G. A. Smith, Julia Koehler Leman
+2 more Chris Bahl, David L. Mobley
Cited by: 3
References: 113

Abstract

The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine-learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high costs, and opaque systems that limit reproducibility, independent scrutiny, and scientific independence. Additionally, it results in taxpayer-funded research being left siloed in commercial tools where it cannot have as much impact as if it were returned to the general public. This Perspective advocates for permissively licensed open source software as a scientific and economic multiplier by reducing the duplication of effort and enabling scientific validation of modeling tools and frictionless experimentation with new ideas. Coordinated multiproject consortia, such as Open Force Field, Open Free Energy, OpenFold, and OpenADMET, have formed to collaboratively build shared computational infrastructure and release all methods under permissive licenses. The success of these large-scale efforts requires organizational structures that extend beyond code. The Open Molecular Software Foundation (OMSF), a U.S. nonprofit, serves as a domain-specific institutional home and fiscal sponsor. By providing governance, administrative infrastructure, and dedicated research software engineers, OMSF aligns incentives across academic and industrial stakeholders. This framework enables a synergistic ecosystem where projects interoperate to accelerate innovation, eliminate duplication, and ensure long-term software sustainability, thereby creating durable foundations that elevate the entire molecular modeling community.

Fields of Study & Subjects

Scientific Computing and Data Management Machine Learning in Materials Science Computational Drug Discovery Methods

Publication details

Journal / source
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Volume
66
Issue
6
Pages
2967–2984
Publication date
10 March 2026
Language
EN