Computational methods for structural biology often begin with rapid prototyping in Python. Performance demands, however, frequently lead to implementations in C or C++, resulting in software packages with C++ cores and Python user interfaces. The Biochemical Algorithms Library (BALL) is a representative example of this approach: a well-established C++ framework initiated in 1996, with Python bindings added in 2010. BALL was once the largest open-source library of its kind, offering broad functionality for molecular structure analysis.
This motivated the redesign of the library and led to our Julia-centric framework, BiochemicalAlgorithms.jl. It provides core data structures, file I/O, preprocessing, molecular mechanics, optimization algorithms, and Julia ecosystem integration across the structural bioinformatics pipeline. Moving development to Julia has simplified the implementation of key design goals, particularly rapid application development.
Building on this recently published framework, we will demonstrate its practical value through a stochastic gradient descent-based protein energy minimization scheme. While classical energy minimization is often performed with deterministic methods such as conjugate gradient, stochastic optimization, as used in deep learning, introduces controlled randomness that can support broader exploration of the protein energy landscape and help avoid premature trapping in local minima. In this sense, the example serves both as an optimization strategy and as a demonstration of how quickly new algorithmic ideas can be implemented within BiochemicalAlgorithms.jl. More broadly, this work highlights how the framework supports efficient method development and provides a practical environment for building computational tools in structural bioinformatics.

Co-authors: Thomas Kemmer, Andreas Hildebrandt