Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses.

ABSTRACT: The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug design. To contribute to this field, we developed an accurate predictive classifier for druggable cancer-driving proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. The optimal classifier was achieved with the support vector machine method, utilizing 200 tri-amino acid composition descriptors. The high performance of the model is evident from an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and an accuracy of 0.929 ± 0.006 (threefold cross-vali

SUBMITTER: Lopez-Cortes A 

PROVIDER: S-EPMC11339426 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature