Garcia-Sosa, A. T.; Maran, U. Combined Naïve Bayesian, chemical fingerprints, and molecular docking classifiers to model and predict androgen receptor binding activity data for environmentally- and health-sensitive substances. Int. J. Mol. Sci. 2021, 22, 6695.

Many chemicals that enter the environment, food chain, and the human body can disrupt androgen-dependent pathways and mimic hormones and therefore, may be responsible for multiple diseases from reproductive to tumor. Thus, modeling and predicting androgen binding activity is an important area of research. The aim of the current study was to find a method or ...