2. Machine Learning Guidelines
In this section, we provide a general set of recommendations through our curated guidelines that might be useful for regimenting routine tasks for performing machine learning (ML) research projects in computer and molecular sciences (CMSs). These guidelines cover pertinent topics in ML in general, from data engineering such as file formatting, naming conventions to sharing models in open-access environments to necessary steps to publish datasets, models and workflows to improve appropriate starategies for data management and reproducibility of the scientific results.