Author, as appears in the article.: Ojeda-Montes, Maria J; Casanova-Marti, Angela; Gimeno, Aleix; Tomas-Hernandez, Sarah; Cereto-Massague, Adria; Wolber, Gerhard; Beltran-Debon, Raul; Valls, Cristina; Mulero, Miquel; Pinent, Montserrat; Pujadas, Gerard; Garcia-Vallve, Santiago
Department: Bioquímica i Biotecnologia
URV's Author/s: Beltrán Debón, Raúl Alejandro / CASANOVA MARTÍ, ANGELA / Cereto Massagué, Adrián José / Garcia Vallve, Santiago / Mulero Abellán, Miguel / OJEDA MONTES, Mª JOSÉ / Pinent Armengol, Montserrat / Pujadas Anguiano, Gerard / TOMAS HERNÁNDEZ, SARA / Valls Bautista, Cristina
Keywords: cd26; dipeptidyl peptidase 4; diversifying molecular scaffolds; expanding chemical space; molecular fingerprints; virtual molecular libraries; Animals; Binding sites; Cd26; Computational chemistry; Databases, chemical; Dipeptidyl peptidase 4; Dipeptidyl-peptidase iv inhibitors; Diversifying molecular scaffolds; Drug design; Expanding chemical space; Humans; Kidney; Ligands; Molecular docking simulation; Molecular fingerprints; Structure-activity relationship; Swine; Virtual molecular libraries; Virtual screening
Abstract: Aim: Fragment-based drug design or bioisosteric replacement is used to find new actives with low (or no) similarity to existing ones but requires the synthesis of nonexisting compounds to prove their predicted bioactivity. Protein-ligand docking or pharmacophore screening are alternatives but they can become computationally expensive when applied to very large databases such as ZINC. Therefore, fast strategies are necessary to find new leads in such databases. Materials & methods: We designed a computational strategy to find lead molecules with very low (or no) similarity to existing actives and applied it to DPP-IV. Results: The bioactivity assays confirm that this strategy finds new leads for DPP-IV inhibitors. Conclusion: This computational strategy reduces the time of finding new lead molecules.
Thematic Areas: Astronomia / física; Biotecnología; Chemistry, medicinal; Ciência da computação; Ciências ambientais; Ciências biológicas i; Ciências biológicas ii; Ciências biológicas iii; Drug discovery; Enfermagem; Engenharias iv; Farmacia; General medicine; Medicina ii; Molecular medicine; Odontología; Pharmacology; Química; Saúde coletiva
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: raul.beltran@urv.cat; gerard.pujadas@urv.cat; santi.garcia-vallve@urv.cat; montserrat.pinent@urv.cat; miquel.mulero@urv.cat; cristina.valls@urv.cat; adrianjose.cereto@urv.cat
ISSN: 17568919
Record's date: 2024-11-23
Journal volume: 11
Paper version: info:eu-repo/semantics/acceptedVersion
Link to the original source: https://www.future-science.com/doi/10.4155/fmc-2018-0597
Paper original source: Future Medicinal Chemistry. 11 (12): 1387-1401
APA: Ojeda-Montes, Maria J; Casanova-Marti, Angela; Gimeno, Aleix; Tomas-Hernandez, Sarah; Cereto-Massague, Adria; Wolber, Gerhard; Beltran-Debon, Raul; Va (2019). Mining large databases to find new leads with low similarity to known actives: application to find new DPP-IV inhibitors. Future Medicinal Chemistry, 11(12), 1387-1401. DOI: 10.4155/fmc-2018-0597
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Article's DOI: 10.4155/fmc-2018-0597
Entity: Universitat Rovira i Virgili
Journal publication year: 2019
Publication Type: Journal Publications
Repositori URV