Machine Learning-Based QSAR Models for Discovery of Inhibitors Targeting Leishmania infantum Amastigotes
: Artificial intelligence-based QSAR models proved to be useful tools for prioritizing antileishmanial candidates. The integration of molecular descriptors, machine learning, and virtual screening offers an efficient strategy for drug discovery.
Authors
- Francisco Torrens (ORCID: https://orcid.org/0000-0002-8534-208X)
- Julio A. Rojas-Vargas (ORCID: https://orcid.org/0000-0002-8877-1592)
- Naiví Flores-Balmaseda
- Facundo Pérez-Giménez
- Juan A. Castillo-Garit
- Susana Rojas-Socarrás
Institutions
- Universitat de València (ES)
- Parc Científic de la Universitat de València (ES)
- Universidad Central (BO)
- Universidad Central (CR)
- Dirección de Investigación y Desarrollo (CL)
- Metropolitan University of Technology (CL)
Publication Details
- Journal
- Pharmaceuticals
- Published
- 2026-04-07
- DOI
- https://doi.org/10.3390/ph19040588
- Primary Topic
- Research on Leishmaniasis Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Universidad Tecnológica Metropolitana