Open-source annotation and target prediction tool that explores some of the largest chemical and biological databases, mining these for identification of common name, synonyms, and structurally similar molecules.

Getting Started

Scripts can be executed using Linux-based command line. Please note that reference folders must be present for CACTI correct execution.

Prerequisites

• Python 3.0
• xlrd 1.2
• matplotlib 2.0
• scipy 1.4
• Rdkit 2018.03.4
• networkx 2.3
• numpy 1.15
• pandas 0.23
• requests 2.12
• scikit-learn 0.20

Conda environment

This project can be executed with preinstalled packages, or using a conda environment. To install the Anaconda environment, please type

conda env create --file metadata/CACTI-env.txt --name CACTI python=3

Activate the directory by typing

conda activate CACTI

Execute

1. Change directory into that containing the base script (CACTI.py). Please note that if using anaconda environment, it must be activated first
2. Execute command line script

Full analysis

To execute full analysis (synonyms, literature, similar analogs and clustering analysis), please type

python CACTI.py -in filename.txt

To save the output to a particular directory, one can type the path using:

python CACTI.py -in filename.txt -out /outputdir

To add an optional output name:

python CACTI.py -in filename.txt -out /outputdir -p outname

Description of available options:

python CACTI.py -h

Partial analysis

Module 1: Synonyms and literature

python CACTI.py -in filename.txt -lit

To obtain results faster and if available, one could use their own NBCI API code when executing the literature module

python CACTI.py -in filename.txt -lit -apincbi apiValue

If desired to find literature evidence with a keyword, please type the following

python CACTI.py -in filename.txt -lit -k keyword1,keyword2

Module 2: Close analogs

To find close analogs and perform similarity analysis between them, with 80% Tanimoto similarity

python CACTI.py -in filename.txt -simnet -sdb

To change the similarity threshold, add the corresponding flag, where X is a numerical value between 0-1

python CACTI.py -in filename.txt -simnet -sdb -similthreshold X

Module 3: Clustering analysis

The clustering analysis is performed using the drug-target pair collection, provided in the metadata folder.

python CACTI.py -in filename.txt -pred -similthreshold X

If there is an particular clustering reference file to use instead, one can provide the path by typing

python CACTI.py -in filename.txt -pred -predin alternative_textfile.txt -similthreshold X