289 related articles for article (PubMed ID: 34884870)
1. IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds.
Quevedo-Tumailli V; Ortega-Tenezaca B; González-Díaz H
Int J Mol Sci; 2021 Dec; 22(23):. PubMed ID: 34884870
[TBL] [Abstract][Full Text] [Related]
2. CAPi: Computational Model for Apicoplast Inhibitors Prediction Against Plasmodium Parasite.
Dixit S; Singla D
Curr Comput Aided Drug Des; 2017 Nov; 13(4):303-310. PubMed ID: 28260517
[TBL] [Abstract][Full Text] [Related]
3. PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer.
Bediaga H; Arrasate S; González-Díaz H
ACS Comb Sci; 2018 Nov; 20(11):621-632. PubMed ID: 30240186
[TBL] [Abstract][Full Text] [Related]
4. Chromosome Gene Orientation Inversion Networks (GOINs) of Plasmodium Proteome.
Quevedo-Tumailli VF; Ortega-Tenezaca B; González-Díaz H
J Proteome Res; 2018 Mar; 17(3):1258-1268. PubMed ID: 29336158
[TBL] [Abstract][Full Text] [Related]
5. PTML Model for Selection of Nanoparticles, Anticancer Drugs, and Vitamins in the Design of Drug-Vitamin Nanoparticle Release Systems for Cancer Cotherapy.
Santana R; Zuluaga R; Gañán P; Arrasate S; Onieva E; Montemore MM; González-Díaz H
Mol Pharm; 2020 Jul; 17(7):2612-2627. PubMed ID: 32459098
[TBL] [Abstract][Full Text] [Related]
6. Machine Learning Study of Metabolic Networks
Diéguez-Santana K; Casañola-Martin GM; Torres R; Rasulev B; Green JR; González-Díaz H
Mol Pharm; 2022 Jul; 19(7):2151-2163. PubMed ID: 35671399
[TBL] [Abstract][Full Text] [Related]
7. Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps.
Marrero-Ponce Y; Iyarreta-Veitía M; Montero-Torres A; Romero-Zaldivar C; Brandt CA; Avila PE; Kirchgatter K; Machado Y
J Chem Inf Model; 2005; 45(4):1082-100. PubMed ID: 16045304
[TBL] [Abstract][Full Text] [Related]
8. Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds.
Vásquez-Domínguez E; Armijos-Jaramillo VD; Tejera E; González-Díaz H
Mol Pharm; 2019 Oct; 16(10):4200-4212. PubMed ID: 31426639
[TBL] [Abstract][Full Text] [Related]
9. Designing nanoparticle release systems for drug-vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models.
Santana R; Zuluaga R; Gañán P; Arrasate S; Onieva E; González-Díaz H
Nanoscale; 2019 Nov; 11(45):21811-21823. PubMed ID: 31691701
[TBL] [Abstract][Full Text] [Related]
10. NL MIND-BEST: a web server for ligands and proteins discovery--theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum.
González-Díaz H; Prado-Prado F; Sobarzo-Sánchez E; Haddad M; Maurel Chevalley S; Valentin A; Quetin-Leclercq J; Dea-Ayuela MA; Teresa Gomez-Muños M; Munteanu CR; José Torres-Labandeira J; García-Mera X; Tapia RA; Ubeira FM
J Theor Biol; 2011 May; 276(1):229-49. PubMed ID: 21277861
[TBL] [Abstract][Full Text] [Related]
11. Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum.
Mswahili ME; Martin GL; Woo J; Choi GJ; Jeong YS
Biomolecules; 2021 Nov; 11(12):. PubMed ID: 34944394
[TBL] [Abstract][Full Text] [Related]
12. Bayesian models trained with HTS data for predicting β-haematin inhibition and in vitro antimalarial activity.
Wicht KJ; Combrinck JM; Smith PJ; Egan TJ
Bioorg Med Chem; 2015 Aug; 23(16):5210-7. PubMed ID: 25573118
[TBL] [Abstract][Full Text] [Related]
13. Identification via a Parallel Hit Progression Strategy of Improved Small Molecule Inhibitors of the Malaria Purine Uptake Transporter that Inhibit
Sosa Y; Deniskin R; Frame IJ; Steiginga MS; Bandyopadhyay D; Graybill TL; Kallal LA; Ouellette MT; Pope AJ; Widdowson KL; Young RJ; Akabas MH
ACS Infect Dis; 2019 Oct; 5(10):1738-1753. PubMed ID: 31373203
[TBL] [Abstract][Full Text] [Related]
14. On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems.
He S; Segura Abarrategi J; Bediaga H; Arrasate S; González-Díaz H
Beilstein J Nanotechnol; 2024; 15():535-555. PubMed ID: 38774585
[TBL] [Abstract][Full Text] [Related]
15. Mapping the genome of Plasmodium falciparum on the drug-like chemical space reveals novel anti-malarial targets and potential drug leads.
Jensen K; Plichta D; Panagiotou G; Kouskoumvekaki I
Mol Biosyst; 2012 Jun; 8(6):1678-85. PubMed ID: 22446744
[TBL] [Abstract][Full Text] [Related]
16. IFPTML mapping of nanoparticle antibacterial activity
Ortega-Tenezaca B; González-Díaz H
Nanoscale; 2021 Jan; 13(2):1318-1330. PubMed ID: 33410431
[TBL] [Abstract][Full Text] [Related]
17. From crystal to compound: structure-based antimalarial drug discovery.
Drinkwater N; McGowan S
Biochem J; 2014 Aug; 461(3):349-69. PubMed ID: 25008945
[TBL] [Abstract][Full Text] [Related]
18. Towards machine learning discovery of dual antibacterial drug-nanoparticle systems.
Diéguez-Santana K; González-Díaz H
Nanoscale; 2021 Nov; 13(42):17854-17870. PubMed ID: 34671801
[TBL] [Abstract][Full Text] [Related]
19. Computational identification of Plasmodium falciparum RNA pseudouridylate synthase as a viable drug target, its physicochemical properties, 3D structure prediction and prediction of potential inhibitors.
Afolabi R; Chinedu S; Ajamma Y; Adam Y; Koenig R; Adebiyi E
Infect Genet Evol; 2022 Jan; 97():105194. PubMed ID: 34968763
[TBL] [Abstract][Full Text] [Related]
20. Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [
Diez-Alarcia R; Yáñez-Pérez V; Muneta-Arrate I; Arrasate S; Lete E; Meana JJ; González-Díaz H
ACS Chem Neurosci; 2019 Nov; 10(11):4476-4491. PubMed ID: 31618004
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]