ToolBoxSF: Robustly interrogating machine learning-based scoring functions: what are they learning?

Abstract

<p>This Zenodo repository provides comprehensive resources for the pre-print research paper titled "Robustly interrogating machine learning-based scoring functions: what are they learning?" Our collection includes Singularity containers containing pre-trained models, benchmark datasets, and training/test CSV files, offering valuable insights into the inner workings of machine learning-based scoring functions.</p><p>Key Components:</p><p>Singularity Containers:</p><ul><li>Machine Learning Models: Explore state-of-the-art scoring models used in the study, enabling reproducibility and in-depth analysis.</li><li>Environment Setup: Simplify model deployment and experimentation by utilizing our pre-configured environments.</li></ul><p>Benchmark Datasets:</p><ul><li>Curated benchmark datasets used in the pre-print, facilitating validation and evaluation of scoring functions.</li></ul><p>Training and Test CSV Files:</p><ul><li>Training and test data in CSV format, along with associated metadata.</li><li>Facilitate model testing and comparison using the provided data.</li></ul><p>This Zenodo collection is a valuable resource for researchers, data scientists, and machine learning enthusiasts seeking to replicate the study's findings, explore model behaviors, and conduct further investigations into machine learning-based scoring functions. Detailed documentation and usage instructions are included to support your research efforts at <a href="https://github.com/guydurant/toolboxsf">https://github.com/guydurant/toolboxs</a>f.</p><p>Citation Information: Please cite this Zenodo repository when using our resources in your work, and consider acknowledging the original pre-print when publishing research based on these materials.</p&gt