DeepMol: An Automated Machine and Deep Learning Framework for Computational Chemistry

Abstract

AbstractThe 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,DeepMolstands out as an Automated ML (AutoML) tool by automating critical steps of the ML pipeline.DeepMolrapidly 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,DeepMolobtained 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,DeepMolstands out with its open-source code, in-depth tutorials, detailed documentation, and examples of real-world applications, all available athttps://github.com/BioSystemsUM/DeepMolandhttps://deepmol.readthedocs.io/en/latest/. By introducing AutoML as a ground-breaking feature in computational chemistry, DeepMol establishes itself as the pioneering state-of-the-art tool in the field.