Predicted Binders for Various Protein Targets from Machine Learning Applied to Affinity Screening Data

Inventor(s)

Abstract

We used datasets from affinity screening to train machine learning models. The models were then used to rank compounds from commercially available compound collections to generate the following lists of predicted binders. The following lists of compounds were predicted by the ML model to be binders for the protein targets: ATG16L1, Covid 3CLPro, Covid Ace2, Covid Nu- cleocapsid, Covid PLPro, Covid Spike, DDB1, LC3B, MID2, RACK1, AAMP, CDC20, DCAF1, DNMT3A, KLHL3, PRPF4, RBBP4, WDR12, WDR5, setDB1, SMU1, TRIM2, TRIM9

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

"Predicted Binders for Various Protein Targets from Machine Learning Applied to Affinity Screening Data", Technical Disclosure Commons, (September 13, 2021)
https://www.tdcommons.org/dpubs_series/4590