Transcriptomics-to-Activity Transformer is a deep learning model to predict compound bioactivity in a dose-response assay using compound-induced transcriptomic profiles over concentration.
tat: Python source code for TAT.
We have tested TAT on machines with the following GPUs:
- NVIDIA Tesla V100
- NVIDIA Ampere A100
We have tested TAT on machines with the following systems:
- Red Hat Enterprise Linux 8
- CentOS Linux 7
- python 3.8.15
- pandas 1.5.2
- numpy 1.23.5
- pytorch 1.12.1.post200
- rdkit 2022.09.3
- scikit-learn 1.2.0
- matplotlib 3.6.2
- seaborn 0.12.2
- skorch 0.9.0
- Install the python libraries mentioned in Software dependencies above into your python environment.
An example dataset with transcriptional signatures over concentration
can be downloaded from https://broad.io/rosetta/. The example dataset
is LINCS-Pilot1.
With the example LINCS dataset, we show how to build a TAT model that takes as input the transcriptional signatures over concentration of compounds to predict a compound-induced morphological feature in a Cell Painting assay.
Make sure to modify the data directory path in preprocess.py to
ensure that the code finds the LINCS data.
cd ./tat
python preprocess.py
python model_build.py
Copyright 2024 Novartis Institutes for BioMedical Research Inc.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
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See LICENSE.txt