Integrative computational strategy for anticancer drug discovery: QSAR-ANN modeling, molecular docking, ADMET prediction, molecular dynamics and MM-PBSA simulations, and retrosynthetic analysis
A comprehensive in silico strategy integrating 3D-QSAR, ANN, docking, ADMET, MD simulations and retrosynthesis led to the identification of a new drug candidate (L5) as a promising aromatase inhibitor for breast cancer.
Authors
- Fouad Khalil (ORCID: https://orcid.org/0000-0002-9815-2744)
- Mohammed Bouachrıne (ORCID: https://orcid.org/0000-0002-8901-047X)
- Marwa Alaqarbeh (ORCID: https://orcid.org/0000-0002-0879-5077)
- Samir Chtita (ORCID: https://orcid.org/0000-0003-2344-5101)
- Abdellah El Aissouq (ORCID: https://orcid.org/0000-0001-6909-9828)
- Lhoucine Naanaai (ORCID: https://orcid.org/0009-0004-2781-2970)
- Said El Rhabori (ORCID: https://orcid.org/0000-0003-1468-950X)
- Hicham Zaitan (ORCID: https://orcid.org/0000-0002-2542-3240)
- Yassine Elallouche
Institutions
- Applied Science Private University (JO)
- Université Moulay Ismail de Meknes (MA)
- Sidi Mohamed Ben Abdellah University (MA)
- University of Hassan II Casablanca (MA)
Publication Details
- Journal
- New Journal of Chemistry
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5nj02417j
- Citations
- 2
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 3.01