Oak Brook, IL – Volume 34 of SLAS Discovery includes two original research articles, one short communication and one entry in the upcoming Special Issue on EUOS/SLAS Joint Challenge: Compound Solubility .
The upcoming special issue, EUOS/SLAS Joint Challenge: Compound Solubility highlights a scientific challenge in which computational experts developed machine learning models to predict solubility for ~100,000 compounds from the EU-OPENSCREEN collection. The challenge prioritized interpretable AI workflows, combining robust data curation with meaningful chemical descriptors to create a benchmark for future solubility prediction in drug discovery.
Access to this issue of SLAS Discovery is available at https://www.slas-discovery.org/issue/S2472-5552(25)X0005-8
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SLAS Discovery reports how scientists develop and use novel technologies and/or approaches to provide and characterize computational, chemical and biological tools to understand and treat human disease. The journal focuses on drug discovery sciences with a strong record of scientific rigor and impact, reporting on research that:
SLAS (Society for Laboratory Automation and Screening) is an international professional society of academic, industry and government life sciences researchers and the developers and providers of laboratory automation technology. The SLAS mission is to bring together researchers in academia, industry and government to advance life sciences discovery and technology via education, knowledge exchange and global community building.
SLAS Discovery: Advancing the Science of Drug Discovery, 2024 Impact Factor 2.7. Editor-in-Chief Robert M. Campbell, Ph.D., Grove Biopharma, Inc., Chicago, IL (USA)
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SLAS DISCOVERY
28-Jul-2025