{"repo":"smortezah/napr","free":true,"listed":false,"github":"https://github.com/smortezah/napr","clone":"git clone https://github.com/smortezah/napr.git","description":"Machine learning meets natural products","language":"Jupyter Notebook","stars":11,"topics":["python","machine-learning","data-science","natural-products","tensorflow","scikit-learn","pandas","classification","clustering","exploratory-data-analysis"],"license":"MIT","category":"machine-learning","readme_excerpt":"Napr Navigate the natural products chemical space with Machine Learning. Get started Tutorials Notebook What it covers -------------------------------------------------------------------------------------------------------------------- -------------------------------------------------------------------- Terpene-explore Exploratory analysis of the COCONUT terpene dataset Terpene-classification Classification of terpenes with kNN, random forest & XGBoost methods Contributing Run the tests: PRs welcome at all experience levels. Cite Hosseini, M.; Pereira, D.M. The Chemical Space of Terpenes: Insights from Data Science and AI. Pharmaceuticals 2023 , 16, 202. https://doi.org/10.3390/ph16020202","default_branch":null,"files":null,"tree":[],"storefront":"/r/smortezah","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/smortezah/napr/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}