MT-NIR: Multi-task Photochemical Property Prediction Model (uvvis-nir range)

This repository contains code for predicting near-infrared (NIR) and UV-Vis properties of photoswitches using multitask learning with Chemprop.

Features

Current prediction capabilties of photochemical properties - Maximum absorption wavelength (nm) - Extinction coefficient (log(M^-1 cm^-1)) - Photoisomerization quantum yield

Citation

If you use this software in your research, please cite it using the following:

bibtex @software{mt-nir, author = {Jesus Diaz Sanchez}, title = {MT-NIR: Multi-task Photochemical Property Prediction Model}, year = {2024}, url = {https://github.com/jdsanc/mt-nir}, version = {1.0.0} }

You can also use the CITATION.cff file in the repository for citation information.

Installation

1. Install Miniconda3

First, install Miniconda3 if you haven't already:

macOS:

You can install Miniconda using Homebrew (recommended) or download directly:

Option 1: Using Homebrew

```bash

Install Homebrew if you don't have it

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Install Miniconda

brew install --cask miniconda ```

Option 2: Direct Download

```bash

Download Miniconda

curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh

Install Miniconda

bash Miniconda3-latest-MacOSX-x86_64.sh ```

Linux:

```bash

Download Miniconda

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

Install Miniconda

bash Miniconda3-latest-Linux-x86_64.sh ```

For Linux users, you might need to install wget first: ```bash

Ubuntu/Debian

sudo apt-get install wget

CentOS/RHEL

sudo yum install wget

Fedora

sudo dnf install wget ```

Follow the prompts during installation. After installation, restart your terminal or run: bash source ~/.bashrc # Linux source ~/.zshrc # macOS

For more detailed Linux commands and troubleshooting, refer to the Linux Command Line Guide.

2. Clone the Repository

bash git clone https://github.com/jdsanc/mt-nir.git cd mt-nir

3. Create Conda Environment

You can create the environment in two ways:

Option 1: Using environment.yml (Recommended)

bash conda env create -f environment.yml

Option 2: Manual Installation

bash conda create -n chemprop_v2 python=3.11 conda activate chemprop_v2 pip install chemprop pandas numpy rdkit

4. Activate the Environment

bash conda activate chemprop_v2

Usage

Model is already trained all you need to do load the model into your terminal. Using script described in Predictions.

Prediction

You can handle both single SMILES strings and CSV files for bulk prediction:

For a single SMILES: bash python predict.py --smiles "C1=CC=C(C=C1)N=NC2=CC=CC=C2" Ensure you surround your input smiles by quotes ""

For a CSV file: bash python predict.py --csv your_input_file.csv Ensure if using your own .csv file to have the header written as "smiles" verbatum.

The script will output predictions in terminal for single prediction or in output csv called 'yourinputfile_predict.csv' with the following properties:

• maxabswavelength (nm)
• extinct_coeff (log(M^-1 cm^-1))
• photoisomerization_QY