Deepmol: an automated machine and deep learning framework for computational chemistry

The domain of computational chemistry has experienced a significant evolution due to the introduction of Machine Learning (ML) technologies. Despite its potential to revolutionize the field, researchers are often encumbered by obstacles, such as the complexity of selecting optimal algorithms, the automation of data pre-processing steps, the necessity for adaptive feature engineering, and the assurance of model performance consistency across different datasets. Addressing these issues head-on, DeepMol stands out as an Automated ML (AutoML) tool by automating critical steps of the ML pipeline. DeepMol rapidly and automatically identifies the most effective data representation, pre-processing methods and model configurations for a specific molecular property/activity prediction problem. On 22 benchmark datasets, DeepMol obtained competitive pipelines compared with those requiring time-consuming feature engineering, model design and selection processes. As one of the first AutoML tools specifically developed for the computational chemistry domain, DeepMol stands out with its open-source code, in-depth tutorials, detailed documentation, and examples of real-world applications, all available at https://github.com/BioSystemsUM/DeepMol and https://deepmol.readthedocs.io/en/latest/ . By introducing AutoML as a groundbreaking feature in computational chemistry, DeepMol establishes itself as the pioneering state-of-the-art tool in the field. Scientific contribution DeepMol aims to provide an integrated framework of AutoML for computational chemistry. DeepMol provides a more robust alternative to other tools with its integrated pipeline serialization, enabling seamless deployment using the fit, transform, and predict paradigms. It uniquely supports both conventional and deep learning models for regression, classification and multi-task, offering unmatched flexibility compared to other AutoML tools. DeepMol's predefined configurations and customizable objective functions make it accessible to users at all skill levels while enabling efficient and reproducible workflows. Benchmarking on diverse datasets demonstrated its ability to deliver optimized pipelines and superior performance across various molecular machine-learning tasks.