Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins.

ABSTRACT: Recently, numerous models have been developed to predict drug interactions with molecules. However, integrating diverse data sources and improving the accuracy of biological activity predictions remains a challenge. This work proposes a novel solution that addresses these limitations. Here, we have developed a machine learning model to predict the efficacy of different assays and drugs for diseases related to calmodulin. To achieve this, we have compiled a comprehensive data set including commercialized drugs and experimental compounds targeting CaM complexes. The IFPTML-XGB model achieved high predictive performance, with a test accuracy of 89.1% and a sensitivity of 89.0%, demonstrating its robustness for assay efficacy prediction. We have used the IFPTML modeling technique to identify k

SUBMITTER: Baltasar-Marchueta M 

PROVIDER: S-EPMC12606648 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature