Yesylevskyy Semen
Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Prague, Czech Republic.
Receptor.AI Inc., London, UK.
J Comput Chem. 2025 Jan 5;46(1):e27536. doi: 10.1002/jcc.27536.
Transition to the memory safe natively compiled programming languages is a major software development trend in recent years, which eliminates memory-related security exploits, enables a fearless concurrency and parallelization, and drastically improves ergonomics and speed of software development. Modern memory-safe programing languages, such as Rust, are currently not used for developing molecular modeling and simulation software despite such obvious benefits as faster development cycle, better performance and smaller amount of bugs. This work introduces MolAR-the first memory-safe library for analysis of MD simulations written in Rust. MolAR is intended to explore the advantages and challenges of implementing molecular analysis software in the memory-safe natively compiled language and to develop specific memory-safe abstractions for this kind of software. MolAR demonstrates an excellent performance in benchmarks outperforming popular molecular analysis libraries and tools, which makes it attractive for implementing computationally intensive analysis tasks. MolAR is freely available under Artistic License 2.0 at https://github.com/yesint/molar.
向内存安全的原生编译编程语言过渡是近年来软件开发的一个主要趋势，它消除了与内存相关的安全漏洞利用，实现了无畏的并发和并行化，并极大地改善了软件开发的人机工程学和速度。尽管现代内存安全编程语言（如Rust）具有开发周期更快、性能更好和错误数量更少等明显优势，但目前尚未用于开发分子建模和模拟软件。这项工作引入了MolAR——第一个用Rust编写的用于分析分子动力学（MD）模拟的内存安全库。MolAR旨在探索在内存安全的原生编译语言中实现分子分析软件的优势和挑战，并为这类软件开发特定的内存安全抽象。MolAR在基准测试中表现出色，优于流行的分子分析库和工具，这使其在实现计算密集型分析任务方面具有吸引力。MolAR根据艺术许可2.0在https://github.com/yesint/molar上免费提供。
