TY - JOUR U1 - Wissenschaftlicher Artikel A1 - Hagg, Alexander A1 - Kirschner, Karl N. T1 - Open-Source Machine Learning in Computational Chemistry JF - Journal of Chemical Information and Modeling N2 - The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community. KW - Computational chemistry KW - Computational modeling KW - Machine learning KW - Software KW - Optimization Y1 - 2023 UN - https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-74406 SN - 1549-9596 SS - 1549-9596 U6 - https://doi.org/10.1021/acs.jcim.3c00643 DO - https://doi.org/10.1021/acs.jcim.3c00643 PM - 37466636 VL - 63 IS - 15 SP - 4505 EP - 4532 PB - American Chemical Society (ACS) ER -