Predicting Monoamine Oxidase Inhibitory Activity Through Ligand-Based Models

Vilar, Santiago; Ferino, Giulio; Quezada, Elías; Santana, Lourdes; Friedman, Carol

doi:10.2174/1568026611212200010

Cited by 4 publications

(6 citation statements)

References 111 publications

(149 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SpMax3_Bh(m) has been used in predicting depuration rate constants for environmental pollutants of the polychlorinated biphenyls group (87), and the less relevant (in our case) SpMax6_Bh(m) has been used to predict chronic toxicity of substances to Pseudokirchneriella subcapitata (88). The second most important descriptor for our data set was DECC (eccentric topologic index) has been previously reported to be important in the prediction of MAO-A activity (89,90) , placental barrier permeability (91), and gas chromatographic retention times (92). F06 [C-N] was used in a model to describe the anti-proliferative effect of phenyl 4-(2-oxoimidazolidin-1yl)-benzenesulfonates (local QSAR model) (93), anti-malaric effect (94), or skin permeability of substances (95).…”

Section: Discussionmentioning

confidence: 98%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

Preprint

View full text Add to dashboard Cite

Prototype of a family of at least nine members, c-src tyrosine kinase is a therapeutically interesting target, because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We have explored the use of global QSAR models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a data set of 1038 compounds from ChEMBL and 19 blocks of molecular descriptors, we have developed over 200 QSAR classification models, based on six machine learning algorithms and 17 feature selection methods. We have selected 49 with reasonably good performance (positive predictive value and balanced accuracy higher than 70% in nested cross validation) and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over the “named” ZINC data set (over 100,000 compounds). 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using Vina and Ledock, and a number of 90 were predicted to be active based on the binding energy. External data from CHEMBL and PUBCHEM confirmed that at least 7.83% (in the case of QSAR) or 6.67% (in the case of integrated QSAR and molecular docking) of the compounds are active on the c-src target.

show abstract

Section: Discussionmentioning

confidence: 98%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The interested readers are directed toward two recent reviews on the topic. 42,84 Ligand-Based Models for MAO Substrate/ Inhibitor Prediction Vilar et al 84 have reviewed the details of the ligandbased (Q)SAR models for the prediction of inhibitors of MAO isoforms with an aim to get insights into their SAR and mechanism. A short summary of these studies is given in Table 3.…”

Section: Monoamine Oxidases (Mao-a and Mao-b)mentioning

confidence: 99%

“…Nevertheless, a brief summary of the currently available models for substrate/inhibitors is given below. The interested readers are directed toward two recent reviews on the topic …”

Section: Prediction Of Phase I (Non‐cyp450) Drug Metabolismmentioning

confidence: 99%

See 1 more Smart Citation

Recent advances in the prediction of non‐CYP450‐mediated drug metabolism

Dixit

Lal

Agrawal

2017

WIREs Comput Mol Sci

View full text Add to dashboard Cite

Computational models of drug metabolism prediction have focused mainly on cytochrome P450 enzymes, because drug–drug interactions, reactive metabolite formation, hepatotoxicity, idiosyncratic adverse drug interactions, and/or loss of efficacy of many drugs were the results of interactions with CYP450s. Metabolic regioselectivity and isoform specificity prediction models for CYP450‐catalyzed reactions have reached approximately 95% accuracy. Thus, a new drug candidate is less likely to show unexpected metabolic profile due to metabolism via CYP450 pathways. For such candidates, secondary metabolic Phase I and II enzymes are likely to play an expected (or unexpected) role in drug metabolism. The importance of flavin monooxygenases (FMOs), aldehyde and alcohol dehydrogenase, monoamine oxidase from the Phase I and UDP‐glucuronosyltransferase (UGT), sulfotransferase, glutathione S‐transferase, and methyltransferase from Phase II has increased and United States Food and Drug Administration guidelines on NDA have specific recommendations for in vitro and in vivo testing against these enzymes. Thus, there is an urgent requirement of reliable predictive models for drug metabolism catalyzed by these enzymes. In this review, we have classified drug metabolism prediction models (site of metabolism, isoform specificity, and kinetic parameter) for these enzymes into Phase I and II. When such models are unavailable, we discuss the Quantitative Structure Activity Relationship (QSAR), pharmacophore, docking, dynamics, and reactivity studies performed for the prediction of substrates and inhibitors. Recently published models for FMO and UGT are discussed. The need for comprehensive, widely applicable, sequential primary and secondary metabolite prediction is highlighted. Potential difficulties and future prospectives in the development of such models are discussed. WIREs Comput Mol Sci 2017, 7:e1323. doi: 10.1002/wcms.1323 This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Computer and Information Science > Chemoinformatics Software > Molecular Modeling

show abstract

“…This approach has facilitated the addition of activities to the rasagiline scaffold and a new combined cholinesterase and monoamine oxidase inhibitor . Using structure‐based techniques, complete theoretic searches for new lead compounds are also possible .…”

mentioning

confidence: 99%