Efficient virtual screening techniques are critical in drug discovery for identifying potential drug candidates. We present an open-source package for molecular alignment and 3D similarity calculations optimized for large-scale virtual screening of small molecules. This work parallels widely used proprietary tools and offers an approach complementary to structure-based virtual screening. Our package employs the PAPER software for optimizing molecular alignments based on Gaussian volume overlaps. GPU acceleration is utilized to significantly reduce computational time and resource requirements. After obtaining the optimal alignments between the target and the query molecules, both shape and color (based on pharmacophore features) scores are computed to assess molecular similarity, with aligned molecules optionally being output in sdf format. The package was benchmarked using the DUDE-Z public data sets. Results demonstrated the package’s near-state-of-the-art performance and robustness across multiple target classes, with speed that enables many routine ligand-based drug discovery workflows. As an open-source and freely available resource (github.com/molecularinformatics/roshambo) with both a convenient Python API and command line interface, our package also addresses the need for accessible and efficient virtual screening tools in drug discovery.

中文翻译：

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ROSHAMBO：开源分子比对和 3D 相似性评分


高效的虚拟筛选技术在药物发现中对于识别潜在的候选药物至关重要。我们提出了一个开源软件包，用于分子比对和 3D 相似性计算，针对小分子的大规模虚拟筛选进行了优化。这项工作与广泛使用的专有工具类似，并提供了一种与基于结构的虚拟筛选相辅相成的方法。我们的软件包采用 PAPER 软件来优化基于高斯体积重叠的分子比对。GPU 加速用于显著减少计算时间和资源需求。在获得目标分子和查询分子之间的最佳比对后，计算形状和颜色（基于药效团特征）分数以评估分子相似性，对齐的分子可选择以 sdf 格式输出。该软件包使用 DUDE-Z 公共数据集进行了基准测试。结果表明，该套装在多个靶标类别中具有近乎最先进的性能和稳定性，其速度可实现许多基于配体的常规药物发现工作流程。作为具有便捷 Python API 和命令行界面的开源免费资源 （github.com/molecularinformatics/roshambo），我们的软件包还满足了药物发现中对可访问且高效的虚拟筛选工具的需求。