BGC Detection and Classification Using Deep Learning
(Meta-)genome screening for functional and natural product gene sequences
GEne Cluster prediction with COnditional random fields.
COCONUT (COlleCtion of Open Natural prodUcTs): A comprehensive platform facilitating natural product research by providing data, tools, and services for deposition, curation, and reuse.
Natural Product Discovery tools -- a toolkit containing various pipelines for in silico analysis of natural product mass spectrometry data
a *biosynformatic* fingerprint to explore natural product distance and diversity
Data-driven elucidation of flavor chemistry
Official implementation of "FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching" (ICLR 2026)
Machine learning meets natural products
NatUKE: Natural Product Knowledge Extraction Benchmark
This repository gathers scripts used for the paper "Open and re-usable annotated mass spectrometry dataset of a chemodiverse collection of 1,600 plant extracts."
Repository for data, code & figures related to the publication: "Natural products from reconstructed bacterial genomes of the Middle and Upper Paleolithic"
This repository is associated with the manuscript: "IMPPAT 2.0: An Enhanced and Expanded Phytochemical Atlas of Indian Medicinal Plants"
The NPLinker webapp for visualizing NPLinker predictions. Online demo 👇
Repository for files related to DEREP-NP dereplication database
76,907 phytochemical records enriched with PubMed, ClinicalTrials.gov, ChEMBL bioactivity & USPTO patents. Production-ready JSON + Parquet. Free 400-row sample. Full dataset: ethno-api.com
Reproducible code and data repository for the fungal ICS BGC prediction publication. GitHub repository is managed by Grant Nickles. For any data related questions email gnickles@wisc.edu or gnick317@gmail.com. For publication questions reach out to the corresponding authors Dr. Drott at Milton.Drott@usda.gov or Dr. Keller at npkeller@wisc.edu.
Source code and data for "Natural products have increased rates of clinical trial success throughout the drug development process"
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