Calderon Guzman, D.; Osnaya Brizuela, N.; Valenzuela Peraza, A.; Ortiz Herrera, M.; Ortega Garcia, D.; Juarez Olguin, H.; Barragan Mejia, G. Cancer Drugs and Target Prediction in Human. Minireview. Preprints2024, 2024020853. https://doi.org/10.20944/preprints202402.0853.v1
APA Style
Calderon Guzman, D., Osnaya Brizuela, N., Valenzuela Peraza, A., Ortiz Herrera, M., Ortega Garcia, D., Juarez Olguin, H., & Barragan Mejia, G. (2024). Cancer Drugs and Target Prediction in Human. Minireview. Preprints. https://doi.org/10.20944/preprints202402.0853.v1
Chicago/Turabian Style
Calderon Guzman, D., Hugo Juarez Olguin and Gerardo Barragan Mejia. 2024 "Cancer Drugs and Target Prediction in Human. Minireview" Preprints. https://doi.org/10.20944/preprints202402.0853.v1
Abstract
The present minireview assessed the benefits of using artificial intelligence applications in healthcare at predicting the targets of bioactive small molecules in human. Method. The tool is a tuned algorithm, with novel data in web interface. Results. We used tables to order the information of Swiss Target Prediction that allow predictions on combination of chemotherapies and probable side effects. This tool is useful to understand the molecular mechanisms underlying a given phenotype or bioactivity, to rationalize possible favorable or unfavorable side effects and to predict off-targets of known molecules as well as to clear the way for drug repurposing. Predictions are done using a ligand-based approach to compare the similarity between a query molecule and the known ligands of a large collection of protein targets. Conclusion. This study allowed us to formulate recommendations for the use of some of the chemotherapeutic agent groups in cancer patients at a low cost. It is very important and beneficial for patients that take medicine for a longer period and on whose lives depend on such treatment.
Copyright:
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