Overcoming PAINS in AI Drug Discovery
Model Medicines GALILEO™ Platform’s AI Graph Mining Approach Classifies Pan Assay INterference compoundS (PAINS) with Best-in-Class Model Performance Metrics
In our latest research, Model Medicines' Graph-Mining approach outperformed other PAINS classification models for AI drug discovery.
PAINS stands for Pan Assay INterference compoundS in AI drug discovery. It refers to compounds that have a common substructural motif that encodes for an increased chance of any member registering as a hit, leading to false positive and false negative "discoveries" that waste time and resources.
Our best-in-class approach significantly outperformed traditional models like Structure Filter, ML-RF, and GNN, as well as literature reported SVM, ML-RF DNN, and Graph Mining models.
Conclusion
This paper demonstrates how we're optimizing drug discovery and reducing the number of PAINS compounds in both training and inference data sets. Our proprietary AI Graph-Mining PAINS classification engine improves the efficacy, efficiency, and ROI of our GALILEO™ AI Drug Discovery platform.
Details
Date
Mar 20, 2023
Category
Paper
Reading
2 Mins
Author
Navya Ramesh, M.S.
Machine Learning Engineer
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