FTIR-Deep Learning - FTIR Deep Learning With Python

Overview

CANDIY-spectrum

Human analyis of chemical spectra such as Mass Spectra (MS), Infra-Red Specta (FTIR), and Nuclear Magnetic Resonance is both time consuming and potentially inaccurate. This project aims to develop a set of methodologies incorporating these spectra for the prediction of chemical functional groups and structures.

Scraping

Manual Scraping

IR and MS spectra were downloaded from NIST website. https://webbook.nist.gov/chemistry/. Scraping can be done through replacing the correct CAS number in the placeholder. https://webbook.nist.gov/cgi/cbook.cgi?ID="insert_cas"&Units=SI and downloading the required spectra.

(Or)

Automatic Scraping

Download all the species name available in NIST from this link https://webbook.nist.gov/chemistry/download/. Change path of cas_list to where the species name file is stored.

python scrap.py --save_dir='./data/' --cas_list='species.txt' --scrap_IR=true --scrap_MS=true --scrap_InChi=true

Prepare dataset

Parse all jdx files of IR and Mass spectra to standardize and store in a csv format. Also, parse inchi.txt to create target csv indicating presence of functional groups

python prepare_load_dataset.py --data_dir='./data/' --cas_list='species.txt'

Train the model

Run Spectra_Train.ipynb to train the model.

Owner
Wei Mei
Wei Mei
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