Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers reduced false positive rates in BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing machine-learning tools. We supplemented this with random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable putative BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a major addition to in-silico BGC identification.

AI and Big Data Analytics MSD Czech Republic s r o Prague Czech Republic

Big Data Solutions MSD Czech Republic s r o Prague Czech Republic

Bioinformatics and Cheminformatics Solutions MSD Czech Republic s r o Prague Czech Republic

Data Science MSD Czech Republic s r o Prague Czech Republic

Department of Informatics and Chemistry Faculty of Chemical Technology University of Chemistry and Technology Prague Czech Republic

Exploratory Science Center Merck and Co Inc Cambridge Massachusetts USA

Genetics and Pharmacogenomics Merck and Co Inc Boston MA USA

Infectious Diseases and Vaccine Research MRL Merck and Co Inc West Point PA USA

NLP MSD Czech Republic s r o Prague Czech Republic

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Deciphering the biosynthetic landscape of biofilms in glacier-fed streams

MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration