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

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. Key Components: Singularity Containers: Machine Learning Models: Explore state-of-the-art scoring models used in the study, enabling reproducibility and in-depth analysis. Environment Setup: Simplify model deployment and experimentation by utilizing our pre-configured environments. Benchmark Datasets: Curated benchmark datasets used in the pre-print, facilitating validation and evaluation of scoring functions. Training and Test CSV Files: Training and test data in CSV format, along with associated metadata. Facilitate model testing and comparison using the provided data. 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 https://github.com/guydurant/toolboxsf. 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.