Model for High-Throughput Screening of Multitarget Drugs in Chemical Neurosciences: Synthesis, Assay, and Theoretic Study of Rasagiline Carbamates

Alonso, Nerea; Caamaño, Olga; Romero-Duran, Francisco; Luan, Feng; Cordeiro, M. Natália D. S.; Yáñez, Matilde; González-Dı́az, Humberto; Garcı́a-Mera, Xerardo

doi:10.1021/cn400111n

Cited by 51 publications

(35 citation statements)

References 67 publications

(102 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This dataset includes Multi-output assay endpoints of 2217 compounds for at least one out of 338 assays, with 148 molecular or cellular targets, and 35 types of activity measures in 11 model organisms (including human). In a third work [ 52 ], we introduced another ALMA model for neurotoxicity/neuroprotective effects of drugs based on the method MI. First, we used MI to calculate molecular descriptors of the type of stochastic spectral moments of all compounds.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Multi-Target Networks of Neuroprotective Compounds with Entropy Indices and Synthesis, Assay, and Theoretical Study of New Asymmetric 1,2-Rasagiline Carbamates

Durán

Alonso

Caamaño

et al. 2014

IJMS

Self Cite

View full text Add to dashboard Cite

In a multi-target complex network, the links (Lij) represent the interactions between the drug (di) and the target (tj), characterized by different experimental measures (Ki, Km, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (cj). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%–90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of Multi-Target Networks of Neuroprotective Compounds with Entropy Indices and Synthesis, Assay, and Theoretical Study of New Asymmetric 1,2-Rasagiline Carbamates

Durán

Alonso

Caamaño

et al. 2014

IJMS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many different computational chemistry, cheminformatics, bioinformatics and systems biology methods were applied to predict drug activities. Luan 36 , Alonso 37 , Prado-Prado 38,39 and coauthors developed multitarget/multiplexing quantitative structure-property relationships models for multiplexing assays of neurotoxicity/ neuroprotective effects of drugs. These models are based on based on two different well-known software MARCH-INSIDE and DRAGON and show accuracy, specificity as well as sensitivity above 80-90%.…”

Section: Discussionmentioning

confidence: 99%

Pred-binding: large-scale protein–ligand binding affinity prediction

Shar

Tao

Gao

et al. 2016

Journal of Enzyme Inhibition and Medicinal Chemistry

View full text Add to dashboard Cite

Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently, experimental determination of compound-protein interactions remains challenging because of funding investment and difficulties of purifying proteins. In this study, we proposed two in silico models based on support vector machine (SVM) and random forest (RF), using 1589 molecular descriptors and 1080 protein descriptors in 9948 ligand-protein pairs to predict DTIs that were quantified by K i values. The cross-validation coefficient of determination of 0.6079 for SVM and 0.6267 for RF were obtained, respectively. In addition, the two-dimensional (2D) autocorrelation, topological charge indices and three-dimensional (3D)-MoRSE descriptors of compounds, the autocorrelation descriptors and the amphiphilic pseudo-amino acid composition of protein are found most important for K i predictions. These models provide a new opportunity for the prediction of ligand-receptor interactions that will facilitate the target discovery and toxicity evaluation in drug development.

show abstract

“…The above anticancer compounds are synthesized using traditional computational techniques, such as molecular docking, and in recent years, new approaches like the prediction of drug and drug targets candidates with mathematical and computational techniques was introduced. [149][150][151][152] Advanced chemoinformatic tools were known as multitasking models for quantitative structure biological effect relationships. 153 Such models are able to integrate multiple kinds of chemical and biological data, [154][155][156][157] where many different kinds of pharmacological activities, pharmacokinetic parameters, and toxicity proles are simultaneously predicted.…”

Section: Computer-aided Synthesis Of Compoundsmentioning

confidence: 99%