SCAM Detective is a machine learning application and web portal (https://scamdetective.mml.unc.edu/) to identify putative Small Colloidally Aggregating Molecules (SCAMs) in chemical libraries used in high-throughput screening (HTS). SCAM Detective models were developed to predict, with high accuracy, the detergent-sensitive aggregation of compounds in AmpC β-lactamase and cruzain inhibition assays, the preferred counter-screens used widely to identify false positives in the HTS campaigns.
The SCAM Detective provides an alternative method for assessing the potential of chemicals to be putative aggregators and cause false-positive readouts in bioassays. SCAM Detective makes predictions based on Quantitative Structure-Interference Relationship (QSIR) models built on two independent datasets generated from High Throughput Screening campaigns against AmpC β-lactamase (PubChem AID 485341/485294 and AID 585/584) and the cysteine protease cruzain (PubChem AID 1476/1478). The models were developed using open-source chemical descriptors based on ECFP6-like Morgan fingerprints with 2048 bits and an atom radius of 3 calculated in RDKit, along with the random forest (RF) algorithm, using Python 3.6. The models were generated applying the best practices for model development and validation widely accepted by the community (see the figure below).
A web application version of this tool is available at: https://scamdetective.mml.unc.edu/. The toolkit in this GitHub allows batch predictions.
- To predict, with high accuracy, the detergent-sensitive aggregation of compounds in AmpC β-lactamase and cruzain inhibition assays, the preferred counter-screens used widely to identify false positives in the HTS campaigns.
- Maps of the predicted fragment contribution are generated allowing interpretation of the predictions.
- SDF
- SMILES in .csv or .txt.
- Download QSIR models from here.
- Python 3.6+ (Anaconda distribution is recommended)
- RDKit
- scikit-learn
For more information, please refer to our paper:
Alves, V. M.; Capuzzi, S. J.; Braga, R. C.; Korn, D.; Hochuli, J. E.; Bowler, K. H.; Yasgar, A.; Rai, G.; Simeonov, A.; Muratov, E. N.; Zakharov, A. V.; Tropsha, A. SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules. J. Chem. Inf. Model. 2020, 60 (8), 4056–4063. https://doi.org/10.1021/acs.jcim.0c00415. Our paper was the Editor's choice for the August issue of the journal:
SCAM Detective is sponsored by the UNC Eshelman School of Pharmacy of the University of North Carolina at Chapel Hill.
