Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics-Reference-Cited by-同舟云学术

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics

Published:2018-05-23 Issue:11 Volume:9 Page:2572-2587
ISSN:1948-7193
Container-title:ACS Chemical Neuroscience
language:en
Short-container-title:ACS Chem. Neurosci.

Author:

Ferreira da Costa Joana¹,Silva David¹,Caamaño Olga¹,Brea José M.²³,Loza Maria Isabel²³,Munteanu Cristian R.⁴^ORCID,Pazos Alejandro⁴⁵,García-Mera Xerardo¹,González-Díaz Humbert⁶⁷^ORCID

Affiliation:

1. Department of Organic Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain

2. CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain

3. Department of Pharmacology, Pharmacy and Pharmaceutical Technology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain

4. Instituto de Investigacion Biomedica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña, 15006, Spain

5. Computer Science Department, Faculty of Computer Science, University of A Coruna, 15071 A Coruña, Spain

6. Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940 Leioa, Spain

7. IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain

Funder

European Regional Development Fund

Ikerbasque, Basque Foundation for Science

Eusko Jaurlaritza

Secretar?a de Estado de Investigaci?n, Desarrollo e Innovaci?n

Xunta de Galicia

Publisher

American Chemical Society (ACS)

Subject

Cell Biology,Cognitive Neuroscience,Physiology,Biochemistry,General Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acschemneuro.8b00083

Reference64 articles.

1. Probabilistic Neural Network Model for the In Silico Evaluation of Anti-HIV Activity and Mechanism of Action

2. A QSAR Model for in Silico Screening of MAO-A Inhibitors. Prediction, Synthesis, and Biological Assay of Novel Coumarins

3. Quantitative Structure−Activity Relationship and Complex Network Approach to Monoamine Oxidase A and B Inhibitors

4. Virtual Screening of Human 5-Aminoimidazole-4-carboxamide Ribonucleotide Transformylase against the NCI Diversity Set by Use of AutoDock to Identify Novel Nonfolate Inhibitors

5. Phthalocyanine–Peptide Conjugates for Epidermal Growth Factor Receptor Targeting

Cited by 39 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Stapling Amantadine to Melanostatin Neuropeptide: Discovery of Potent Positive Allosteric Modulators of the D₂ Receptors;ACS Medicinal Chemistry Letters;2023-11-14

2. Optimizing drug discovery using multitasking models for quantitative structure–biological effect relationships: an update of the literature;Expert Opinion on Drug Discovery;2023-08-28

3. Target identification for potential drug discovery;Computational Approaches in Drug Discovery, Development and Systems Pharmacology;2023

4. Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds;Molecular Pharmaceutics;2022-06-07

5. PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors;Biomedicines;2022-02-18