Browsing by Author "Arrasate, Sonia"
Now showing items 1-6 of 6
-
Data analysis in chemistry and bio-medical sciences
Todeschini, Roberto; Pazos, A.; Arrasate, Sonia; González-Díaz, Humberto (MPDI, 2016-12-14) -
MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products
Carracedo-Reboredo, Paula; Aranzamendi, Eider; He, Shan; Arrasate, Sonia; Munteanu, Cristian-Robert; Fernández-Lozano, Carlos; Sotomayor, Nuria; Lete, Esther; González-Díaz, Humberto (BMC, 2024-01-23)[Absctract]: The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts ... -
MIANN models in medicinal, physical and organic chemistry
González-Díaz, Humberto; Arrasate, Sonia; Sotomayor, Nuria; Lete, Esther; Munteanu, Cristian-Robert; Pazos, A.; Besada-Porto, Lina; Ruso, Juan M. (Bentham, 2013)[Abstract] Reducing costs in terms of time, animal sacrifice, and material resources with computational methods has become a promising goal in Medicinal, Biological, Physical and Organic Chemistry. There are many computational ... -
Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds
Cabrera-Andrade, Alejandro; López-Cortés, Andrés; Munteanu, Cristian-Robert; Pazos, A.; Pérez-Castillo, Yunierkis; Tejera, Eduardo; Arrasate, Sonia; González-Díaz, Humberto (American Chemical Society, 2020-10-14)[Abstract] Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple ... -
Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning
Munteanu, Cristian-Robert; Gutiérrez-Asorey, Pablo; Blanes-Rodríguez, Manuel; Hidalgo-Delgado, Ismael; Blanco Liverio, María de Jesús; Galdo, Brais; Porto-Pazos, Ana B.; Gestal, M.; Arrasate, Sonia; González-Díaz, Humberto (MDPI, 2021)[Abstract] The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built ... -
Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models
Urista, Diana V.; Carrué, Diego B.; Otero, Iago; Arrasate, Sonia; Quevedo‐Tumailli, Viviana F.; Gestal, M.; González-Díaz, Humberto; Munteanu, Cristian-Robert (MDPI, 2020-07)[Abstract]: Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed ...
