DataMool is an open-source toolkit designed to simplify molecular processing and featurization workflows for machine learning scientists in drug discovery. Built on top of RDKit, it offers a Pythonic API that streamlines molecular data handling, enabling efficient and intuitive operations. Key Features and Functionality: - Intuitive API: Provides a user-friendly interface with sensible defaults, allowing users to perform common tasks such as molecule conversion, fingerprint generation, and standardization with minimal code. - Powerful Integration: Seamlessly integrates with RDKit, supporting various molecular operations, including conformer generation and molecular I/O across multiple formats like SDF, XLSX, and CSV. - Parallel Processing: Incorporates built-in parallelization to accelerate computational workflows, enhancing efficiency in large-scale molecular data processing. - Modern I/O Support: Facilitates reading and writing of multiple file formats, including SDF, XLSX, and CSV, with out-of-the-box support for cloud storage solutions. Primary Value and Problem Solved: DataMool addresses the complexity and inefficiency often encountered in molecular data processing within drug discovery. By providing a cohesive and efficient toolkit, it enables scientists to focus on model development and analysis rather than data wrangling, thereby accelerating the drug discovery pipeline.