Admetica
Admetica is a command-line tool for making ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) predictions using pre-trained models. This tool is part of the Admetica project, which aims to improve ADMET prediction tools through a global, open-source collaboration.
Table of Contents
Installation
Admetica is a powerful tool for making ADMET predictions and can be easily installed on any operating system. You can install it using pip, and optionally, you can set up a conda environment for better package management.
Creating a Conda Environment (Optional)
To create a new conda environment and install Admetica, use the following commands:
conda create --name admetica-env python=3.11
conda activate admetica-env
Installing Admetica
To install Admetica, run:
pip install admetica==1.4.0
By default, the pip installation will include all necessary dependencies for making ADMET predictions.
Usage
Predicting
Admetica provides a command-line interface to make predictions. To use it, run:
admetica_predict \
--dataset-path data.csv \
--smiles-column smiles \
--properties Caco2,PPBR \
--save-path predictions.csv
This command assumes the presence of a file named data.csv with SMILES strings in the column smiles. In addition, you should specify the properties to be calculated (e.g. Caco2). The predictions will be saved to predictions.csv.
Supported models are:
- Lipophilicity
- Solubility
- Caco2
- PPBR
- VDss
- CL-Micro
- CL-Hepa
- Half-Life
- hERG
- LD50
- CYP1A2-Inhibitor
- CYP1A2-Substrate
- CYP2C9-Inhibitor
- CYP2C9-Substrate
- CYP2C19-Inhibitor
- CYP2C19-Substrate
- CYP2D6-Inhibitor
- CYP2D6-Substrate
Release files for admetica 1.4.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| admetica-1.4.1.tar.gz | 74.1 MB | Details |
Release files / admetica-1.4.1.tar.gz
| Download URL | admetica-1.4.1.tar.gz |
|---|---|
| Size | 74.1 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
e78ca5ff0be4d0716e4d93e53dd3663e92bc980f3c51f1f513fadf06397654d6
|
|
BLAKE2b-256 checksum How to use checksums |
0fb16ea5c3ccfbf2a2178e865aeb30649be800d411063ff97b62c63a978f3619
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.12.4
|