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Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches
| dc.contributor.author | Martínez-López, Yoan | |
| dc.contributor.author | Castillo-Garit, J A | |
| dc.contributor.author | Casanola-Martin, Gerardo M. | |
| dc.contributor.author | Rasulev, Bakhtiyor | |
| dc.contributor.author | Rodríguez-Gonzalez, Ansel Y. | |
| dc.contributor.author | Martínez-Santiago, Oscar | |
| dc.contributor.author | Barigye, Stephen J. | |
| dc.date.accessioned | 2024-12-27T13:58:15Z | |
| dc.date.available | 2024-12-27T13:58:15Z | |
| dc.date.issued | 2024 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/5812 | |
| dc.description.abstract | Ubiquitin–proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. The UPS is involved in different biological activities, such as the regulation of gene transcription and cell cycle. Several researchers have applied cheminformatics and artificial intelligence methods to study the inhibition of proteasomes, including the prediction of UPP inhibitors. Following this idea, we applied a new tool for obtaining molecular descriptors (MDs) for modeling proteasome Inhibition in terms of EC50 (µmol/L), in which a set of new MDs called atomic weighted vectors (AWV) and several prediction algorithms were used in cheminformatics studies. In the manuscript, a set of descriptors based on AWV are presented as datasets for training different machine learning techniques, such as linear regression, multiple linear regression (MLR), random forest (RF), K-nearest neighbors (IBK), multi-layer perceptron, best-first search, and genetic algorithm. The results suggest that these atomic descriptors allow adequate modeling of proteasome inhibitors despite artificial intelligence techniques, as a variant to build efficient models for the prediction of inhibitory activity. | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | Molecular Diversity, 28(4), 1983-1994 | es_CL |
| dc.subject | AWV | es_CL |
| dc.subject | GA | es_CL |
| dc.subject | Descriptor | es_CL |
| dc.subject | ML | es_CL |
| dc.subject | Deep learning | es_CL |
| dc.subject | UPS | es_CL |
| dc.title | Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.indexacion | Isi | es_CL |
| dc.ucm.uri | springerlink.ucm.elogim.com/article/10.1007/s11030-023-10638-2 | es_CL |
| dc.ucm.doi | doi.org/10.1007/s11030-023-10638-2 | es_CL |
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