ersilia-os/zaira-chem
Automated QSAR based on multiple small molecule descriptors
This tool helps researchers in drug discovery quickly build predictive models for how chemical structures will behave. You provide a list of chemical structures (in SMILES format) and their measured properties (like bioactivity), and it automatically trains a model. The output is a predictive model that can then estimate the properties of new, untested chemical structures. This is ideal for computational chemists, pharmacologists, and drug discovery scientists.
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Use this if you need to rapidly develop a Quantitative Structure-Activity Relationship (QSAR) model to predict pharmacological properties of small molecules without deep machine learning expertise.
Not ideal if you require highly customized machine learning model architectures or in-depth control over every parameter of the model training process.
Stars
44
Forks
16
Language
Python
License
GPL-3.0
Category
Last pushed
Nov 18, 2024
Commits (30d)
0
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