AI in Drug Discovery

Journal of Cheminformatics is calling for submissions to our Collection on AI in Drug Discovery. 

The dramatic increase in the use of Artificial Intelligence (AI) and traditional machine learning methods in different scientific fields has become an essential asset in the future development of the chemical industry, including the pharmaceutical, agro biotech, and other chemical sectors. 

This Special Issue invites cutting-edge contributions in the rapidly evolving field of AI-driven drug discovery. We are seeking submissions encompassing various facets of the field, such as generative models, explainable AI, model distillation, uncertainty quantification, reaction informatics and synthetic route prediction, quantum machine learning for reactivity, methodologies for mining very large compound datasets, federated learning, analysis of HTS data and identification of frequent hitters, as well as other topics related to the use of machine learning (ML) in chemistry.  We look forward to receiving contributions from all researchers active in the field, whether they are developing novel methodologies or expanding the scope of established methodologies. A non-exhaustive list of topics includes:

●    Big Data and Advanced Machine Learning in Chemistry
●    Use of Deep Learning to Predict Molecular Properties
●    Modeling and Prediction of Chemical Reaction Data
●    eXplainable AI (XAI) in Chemistry 
●    Cheminformatics
●    Generative Models

The core collection will mainly consist of a selection of articles to be presented during the AIDD workshop “AI in Drug Discovery”  of the 33^rd International Conference on Artificial Neural Networks (ICANN2024), which is co-organized by the European Neural Network Society and the Horizon2020 Marie Skłodowska-Curie Innovative Training Networks European Industrial Advanced machine learning for Innovative Drug Discovery (AIDD) as well as Horizon Europe Marie Skłodowska-Curie Doctoral Network Explainable AI for Molecules - AiChemist. Any authors participating in the ICANN2024 conference will receive a 20% discount on the APC fee. Authors that are not planning to participate in ICANN2024 are also welcome to submit to this Special Issue.