FP-MAP: an extensive library of fingerprint-based molecular activity prediction tools

نویسندگان

چکیده

Discovering new drugs for disease treatment is challenging, requiring a multidisciplinary effort as well time, and resources. With view to improving hit discovery lead compound identification, machine learning (ML) approaches are being increasingly used in the decision-making process. Although number of ML-based studies have been published, most only report fragments wider range bioactivities wherein each model typically focuses on particular disease. This study introduces FP-MAP, an extensive atlas fingerprint-based prediction models that covers diverse activities including neglected tropical diseases (caused by viral, bacterial parasitic pathogens) other targets implicated such Alzheimer’s. To arrive at best predictive models, performance ≈4,000 classification/regression were evaluated different bioactivity data sets using 12 molecular fingerprints. The performing achieved test set AUC values 0.62–0.99 integrated into easy-to-use graphical user interface can be downloaded from https://gitlab.com/vishsoft/fpmap .

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ژورنال

عنوان ژورنال: Frontiers in Chemistry

سال: 2023

ISSN: ['2296-2646']

DOI: https://doi.org/10.3389/fchem.2023.1239467