ROSHAMBO2: Accelerating Molecular Alignment for Large Chemical Libraries with GPU Optimization and Algorithmic Advances.

Abstract

Molecular alignment and 3D similarity are crucial tasks in computational drug discovery, enabling applications such as virtual screening and pharmacophore modeling. ROSHAMBO, an open-source package for optimizing molecular alignment using Gaussian volume overlaps, demonstrated near-state-of-the-art performance and accuracy across multiple target classes. However, its computational efficiency has been a limiting factor in the virtual screening of ultralarge chemical libraries. To address this limitation, we introduce ROSHMABO2, an optimized version that achieves a greater than 200-fold improvement in performance over the original ROSHAMBO implementation through algorithmic innovations, GPU acceleration, and optimized memory handling. This performance establishes ROSHMABO2 as an ideal tool for high-throughput applications, such as virtual screening and chemical library design, enabling efficient exploration of large chemical spaces. In addition to its computational enhancements, the new version retains its modularity, accessibility, and compatibility with diverse workflows. These improvements position ROSHAMBO2 as a transformative tool for modern cheminformatics, addressing the growing demands for scalable molecular modeling. ROSHAMBO2 is accessible at https://github.com/molecularinformatics/roshambo2 and is available for use under the MIT license.