yvquanli/GLAM
Code for "An adaptive graph learning method for automated molecular interactions and properties predictions".
This tool helps drug discovery researchers accelerate finding promising drug candidates. It takes your molecular structure data and relevant biological datasets as input, then automatically generates a prediction model for molecular interactions and properties. The output is a highly accurate and adaptable predictor, aiding medicinal chemists and pharmacologists in designing better drugs more efficiently.
No commits in the last 6 months.
Use this if you need to quickly and reliably predict how new drug candidates will interact with biological targets or exhibit specific properties, without manual fine-tuning of your prediction models.
Not ideal if you are looking for a general-purpose machine learning library rather than a specialized tool for drug discovery predictions.
Stars
40
Forks
11
Language
Python
License
MIT
Category
Last pushed
Jan 14, 2023
Commits (30d)
0
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