An Exploration of the Antiproliferative Potential of Chalcones and Dihydropyrazole Derivatives in Prostate Cancer via Androgen Receptor: Combined QSAR, Machine Learning, and Molecular Docking Techniques Article (Web of Science)

cited authors

  • Oyeneyin, O. E.; Obadawo, B. S.; Metibemu, D. S.; Owolabi, T. O.; Olanrewaju, A. A.; Orimoloye, S. M.; Ipinloju, N.; Olubosede, O.

publication date

  • January 1, 2022

webpage

published in

category

keywords

  • Anticancer properties
  • Computer-aided drug design
  • Data science
  • Extreme learning machine

start page

  • 211

end page

  • 223

volume

  • 10

issue

  • 2