Výsledky vyhľadávania - "Multi-target models"

PTML Model for Selection of Nanoparticles, Anticancer Drugs, and Vitamins in the Design of Drug–Vitamin Nanoparticle Release Systems for Cancer Cotherapy

Autori: Robin Zuluaga Piedad Gañán Ricardo Santana a ďalší

Zdroj: Addi. Archivo Digital para la Docencia y la Investigación
Universidad del País Vasco

Predmety: Big Data, 0301 basic medicine, Databases, Factual, nanoparticle, ChEMBL, Antineoplastic Agents, Vitamins, 02 engineering and technology, anticancer compounds, 3. Good health, Machine Learning, Drug Liberation, 03 medical and health sciences, machine learning, Drug Delivery Systems, big data, Neoplasms, Antineoplastic Combined Chemotherapy Protocols, Linear Models, Nanoparticles, Computer Simulation, Perturbation Theory Machine Learning, PTML, 0210 nano-technology, multi-target models

Popis súboru: application/pdf

Prístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/32459098
http://hdl.handle.net/10810/72299
https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.0c00308
https://www.ncbi.nlm.nih.gov/pubmed/32459098
https://pubmed.ncbi.nlm.nih.gov/32459098/

PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives

Autori: Ricardo Santana Piedad Gañán Robin Zuluaga a ďalší

Zdroj: Addi. Archivo Digital para la Docencia y la Investigación
Universidad del País Vasco

Predmety: 0301 basic medicine, Models, Statistical, Databases, Factual, Molecular Structure, ChEMBL, Bayes Theorem, Vitamins, vitamins, 01 natural sciences, 0104 chemical sciences, machinelearning, Machine Learning, 03 medical and health sciences, big data, Combinatorial Chemistry Techniques, multi-target models, perturbation theory

Popis súboru: application/pdf

Prístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/32011854
http://hdl.handle.net/10810/72302
https://pubs.acs.org/doi/10.1021/acscombsci.9b00166
http://pubs.acs.org/doi/10.1021/acscombsci.9b00166
https://www.ncbi.nlm.nih.gov/pubmed/32011854

PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer

Autori: Humbert González-Díaz Sonia Arrasate Harbil Bediaga

Zdroj: Addi. Archivo Digital para la Docencia y la Investigación
Universidad del País Vasco

Predmety: 0301 basic medicine, ChEMBL, Quantitative Structure-Activity Relationship, Antineoplastic Agents, Models, Theoretical, 3. Good health, Machine Learning, 03 medical and health sciences, machine learning, big data, Neoplasms, Drug Discovery, anti-cancer compounds, Humans, Biological Assay, Neural Networks, Computer, artificial neural networks, multi-target models, Databases, Chemical, perturbation theory

Popis súboru: application/pdf

Prístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/30240186
http://hdl.handle.net/10810/72593
https://pubsdc3.acs.org/doi/10.1021/acscombsci.8b00090
https://pubs.acs.org/doi/10.1021/acscombsci.8b00090
http://europepmc.org/abstract/MED/30240186
https://jglobal.jst.go.jp/detail?JGLOBAL_ID=201902231006007972
https://www.ncbi.nlm.nih.gov/pubmed/30240186

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

Autori: Amit Kumar Halder M. Natália Dias Soeiro Cordeiro

Zdroj: International Journal of Molecular Sciences, Vol 20, Iss 17, p 4191 (2019)

Predmety: PI3K inhibitors, cancer, QSAR, multi-target models, linear discriminant analysis, random forest, Biology (General), QH301-705.5, Chemistry, QD1-999

Relation: https://www.mdpi.com/1422-0067/20/17/4191; https://doaj.org/toc/1422-0067; https://doaj.org/article/63de9ee086a84ee887aa9821b4515582

Dostupnosť: https://doi.org/10.3390/ijms20174191
https://doaj.org/article/63de9ee086a84ee887aa9821b4515582

Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents

Autori: Amit Kumar Halder Amal Kanta Giri Maria Natália Dias Soeiro Cordeiro

Zdroj: Molecules, Vol 24, Iss 21, p 3909 (2019)

Predmety: erk inhibitors, qsar, multi-target models, fragment analysis, virtual screening, molecular docking, molecular dynamics, binding free energy, Organic chemistry, QD241-441

Popis súboru: electronic resource

Relation: https://www.mdpi.com/1420-3049/24/21/3909; https://doaj.org/toc/1420-3049

Prístupová URL adresa: https://doaj.org/article/857286722c5141b1a9c188cee98e0fa5

PTML Multi-Label Algorithms: Models, Software, and Applications

Autori: Ortega Tenezaca, Bernabé Quevedo-Tumailli, Viviana Bediaga, Harbil a ďalší

Prispievatelia: Ortega Tenezaca, Bernabé Quevedo-Tumailli, Viviana Bediaga, Harbil a ďalší

Predmety: Cheminformatics, Drug Discovery, Large data sets, Machinelearning, Multi-target models, PTML, Perturbation theory

Relation: #PLACEHOLDER_PARENT_METADATA_VALUE#; info:eu-repo/grantAgreement/MINECO//CTQ2013-41229-P/ES/CATALISIS ASIMETRICA EN SINTESIS. NUEVOS LIGANDOS QUIRALES PARA CATALIZADORES BASADOS EN METALES DE TRANSICION, APLICACIONES SINTETICAS Y MODELOS COMPUTACIONALES/; info:eu-repo/grantAgreement/MINECO//CTQ2016-74881-P; https://doi.org/10.2174/1568026620666200916122616; Sí; https://hdl.handle.net/10261/339506; http://dx.doi.org/10.13039/501100003329; http://dx.doi.org/10.13039/501100010801; http://dx.doi.org/10.13039/501100001871; http://dx.doi.org/10.13039/501100003086; https://api.elsevier.com/content/abstract/scopus_id/85092238305

Dostupnosť: https://hdl.handle.net/10261/339506
https://doi.org/10.2174/1568026620666200916122616
https://doi.org/10.13039/501100003329
https://doi.org/10.13039/501100010801
https://doi.org/10.13039/501100001871
https://doi.org/10.13039/501100003086
https://api.elsevier.com/content/abstract/scopus_id/85092238305

Synthesis, Pharmacological and Biological Evaluation of 2- Furoyl-based MIF-1 Peptidomimetics, and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML)

Autori: Dias, Ivo Rodríguez Borges, José E. Yáñez, Víctor a ďalší

Prispievatelia: Dias, Ivo Rodríguez Borges, José E. Yáñez, Víctor a ďalší

Predmety: Allosteric modulators, Artificial Neural Networks, Big data, ChEMBL, Machine Learning, Melanostatin, Multi-target models, Perturbation Theory

Time: 3209

Relation: https://doi.org/10.1021/acschemneuro.0c00687; FCT -- UIDB/50006/2020; FEDER -- CTQ2016-74881-P; http://hdl.handle.net/10347/32607

Dostupnosť: http://hdl.handle.net/10347/32607
https://doi.org/10.1021/ACSCHEMNEURO.0C00687

PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives

Autori: Química Orgánica e Inorgánica Kimika Organikoa eta Ez-Organikoa Santana, Ricardo a ďalší

Témy: ChEMBL, vitamins, perturbation theory, machinelearning, big data, multi-target models, info:eu-repo/semantics/article

URL: http://hdl.handle.net/10810/72302
https://doi.org/10.1021/acscombsci.9b00166
2156-8944
https://doi.org/10.1021/acscombsci.9b00166

PTML Model for Selection of Nanoparticle, Anticancer Drug, and Vitamin in the Design of Drug-Vitamin Nanoparticle Release Systems for Cancer Co-Therapy

Autori: Química Orgánica e Inorgánica Kimika Organikoa eta Ez-Organikoa Santana, Ricardo a ďalší

Témy: ChEMBL, nanoparticle, anticancer compounds, Perturbation Theory Machine Learning, PTML, machine learning, big data, multi-target models, info:eu-repo/semantics/article

URL: http://hdl.handle.net/10810/72299
https://doi.org/10.1021/acs.molpharmaceut.0c00308
1543-8384
1543-8392
https://doi.org/10.1021/acs.molpharmaceut.0c00308
info:eu-repo/grantAgreement/MINECO/CTQ2016-74881-P

PTML Multi-Label Algorithms: Models, Software, and Applications

Autori: Ministerio de Economía y Competitividad (España) Agencia Vasca de Desarrollo Empresarial Eusko Jaurlaritza a ďalší

Témy: Cheminformatics, Drug Discovery, Large data sets, Machinelearning, Multi-target models, PTML, Perturbation theory, artículo de revisión

URL: http://hdl.handle.net/10261/339506
https://api.elsevier.com/content/abstract/scopus_id/85092238305
https://doi.org/10.2174/1568026620666200916122616
https://doi.org/10.2174/1568026620666200916122616
Sí
info:eu-repo/grantAgreement/MINECO//CTQ2013-41229-P/ES/CATALISIS ASIMETRICA EN SINTESIS. NUEVOS LIGANDOS QUIRALES PARA CATALIZADORES BASADOS EN METALES DE TRANSICION, APLICACIONES SINTETICAS Y MODELOS COMPUTACIONALES
info:eu-repo/grantAgreement/MINECO//CTQ2016-74881-P

PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer

Autori: Química orgánica II Kimika organikoa II Bediaga Bañeres, Harbil a ďalší

Témy: ChEMBL, anti-cancer compounds, perturbation theory, machine learning, artificial neural networks, big data, multi-target models, info:eu-repo/semantics/article

URL: http://hdl.handle.net/10810/72593
https://doi.org/10.1021/acscombsci.8b00090
2156-8952
https://doi.org/10.1021/acscombsci.8b00090
info:eu-repo/grantAgreement/MINECO/CTQ2016-74881-P

A unified in silico model based on perturbation theory for assessing the genotoxicity of metal oxide nanoparticles.

Autori: Halder AK Melo A Cordeiro MNDS

Zdroj: Chemosphere [Chemosphere] 2020 Apr; Vol. 244, pp. 125489. Date of Electronic Publication: 2019 Nov 27.

Spôsob vydávania: Journal Article

Informácie o časopise: Publisher: Elsevier Science Ltd Country of Publication: England NLM ID: 0320657 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1298 (Electronic) Linking ISSN: 00456535 NLM ISO Abbreviation: Chemosphere Subsets: MEDLINE

Výrazy zo slovníka MeSH: Metal Nanoparticles/*toxicity , Toxicity Tests/*methods, Computer Simulation ; DNA Damage ; Ecosystem ; Humans ; Machine Learning ; Metals ; Nanostructures ; Oxides