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1.
Molecules ; 26(6)2021 Mar 11.
Artículo en Inglés | MEDLINE | ID: mdl-33799871

RESUMEN

Considering the urgency of the COVID-19 pandemic, we developed a receptor-based pharmacophore model for identifying FDA-approved drugs and hits from natural products. The COVID-19 main protease (Mpro) was selected for the development of the pharmacophore model. The model consisted of a hydrogen bond acceptor, donor, and hydrophobic features. These features demonstrated good corroboration with a previously reported model that was used to validate the present model, showing an RMSD value of 0.32. The virtual screening was carried out using the ZINC database. A set of 208,000 hits was extracted and filtered using the ligand pharmacophore mapping, applying the lead-like properties. Lipinski's filter and the fit value filter were used to minimize hits to the top 2000. Simultaneous docking was carried out for 200 hits for natural drugs belonging to the FDA-approved drug database. The top 28 hits from these experiments, with promising predicted pharmacodynamic and pharmacokinetic properties, are reported here. To optimize these hits as Mpro inhibitors and potential treatment options for COVID-19, bench work investigations are needed.


Asunto(s)
Antivirales/química , Antivirales/farmacología , Productos Biológicos/química , Productos Biológicos/farmacología , Receptores de Droga/metabolismo , Sitios de Unión , /química , Bases de Datos Farmacéuticas , Descubrimiento de Drogas , Humanos , Enlace de Hidrógeno , Interacciones Hidrofóbicas e Hidrofílicas , Ligandos , Simulación del Acoplamiento Molecular , Simulación de Dinámica Molecular , Unión Proteica , Relación Estructura-Actividad Cuantitativa
2.
Int J Mol Sci ; 22(6)2021 Mar 11.
Artículo en Inglés | MEDLINE | ID: mdl-33799613

RESUMEN

A novel framework for inverse quantitative structure-activity relationships (inverse QSAR) has recently been proposed and developed using both artificial neural networks and mixed integer linear programming. However, classes of chemical graphs treated by the framework are limited. In order to deal with an arbitrary graph in the framework, we introduce a new model, called a two-layered model, and develop a corresponding method. In this model, each chemical graph is regarded as two parts: the exterior and the interior. The exterior consists of maximal acyclic induced subgraphs with bounded height, the interior is the connected subgraph obtained by ignoring the exterior, and the feature vector consists of the frequency of adjacent atom pairs in the interior and the frequency of chemical acyclic graphs in the exterior. Our method is more flexible than the existing method in the sense that any type of graphs can be inferred. We compared the proposed method with an existing method using several data sets obtained from PubChem database. The new method could infer more general chemical graphs with up to 50 non-hydrogen atoms. The proposed inverse QSAR method can be applied to the inference of more general chemical graphs than before.


Asunto(s)
Algoritmos , Modelos Químicos , Compuestos Orgánicos/química , Relación Estructura-Actividad Cuantitativa , Bases de Datos de Compuestos Químicos , Modelos Moleculares , Estructura Molecular
3.
BMC Bioinformatics ; 22(1): 151, 2021 Mar 24.
Artículo en Inglés | MEDLINE | ID: mdl-33761866

RESUMEN

BACKGROUND: A number of predictive models for aquatic toxicity are available, however, the accuracy and extent of easy to use of these in silico tools in risk assessment still need further studied. This study evaluated the performance of seven in silico tools to daphnia and fish: ECOSAR, T.E.S.T., Danish QSAR Database, VEGA, KATE, Read Across and Trent Analysis. 37 Priority Controlled Chemicals in China (PCCs) and 92 New Chemicals (NCs) were used as validation dataset. RESULTS: In the quantitative evaluation to PCCs with the criteria of 10-fold difference between experimental value and estimated value, the accuracies of VEGA is the highest among all of the models, both in prediction of daphnia and fish acute toxicity, with accuracies of 100% and 90% after considering AD, respectively. The performance of KATE, ECOSAR and T.E.S.T. is similar, with accuracies are slightly lower than VEGA. The accuracy of Danish Q.D. is the lowest among the above tools with which QSAR is the main mechanism. The performance of Read Across and Trent Analysis is lowest among all of the tested in silico tools. The predictive ability of models to NCs was lower than that of PCCs possibly because never appeared in training set of the models, and ECOSAR perform best than other in silico tools. CONCLUSION: QSAR based in silico tools had the greater prediction accuracy than category approach (Read Across and Trent Analysis) in predicting the acute toxicity of daphnia and fish. Category approach (Read Across and Trent Analysis) requires expert knowledge to be utilized effectively. ECOSAR performs well in both PCCs and NCs, and the application shoud be promoted in both risk assessment and priority activities. We suggest that distribution of multiple data and water solubility should be considered when developing in silico models. Both more intelligent in silico tools and testing are necessary to identify hazards of Chemicals.


Asunto(s)
Daphnia , Relación Estructura-Actividad Cuantitativa , Contaminantes Químicos del Agua , Animales , China , Simulación por Computador , Contaminantes Químicos del Agua/toxicidad
4.
Sci Total Environ ; 770: 144561, 2021 May 20.
Artículo en Inglés | MEDLINE | ID: mdl-33736422

RESUMEN

The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.


Asunto(s)
Contaminantes Ambientales , Biodegradación Ambiental , Simulación por Computador , Simulación del Acoplamiento Molecular , Relación Estructura-Actividad Cuantitativa
5.
Molecules ; 26(4)2021 Feb 19.
Artículo en Inglés | MEDLINE | ID: mdl-33669720

RESUMEN

Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R2 = 0.897, Q2LOO = 0.854, and Q2ext = 0.876 and complying with all the parameters established in the validation Tropsha's test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2: main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC50 values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes.


Asunto(s)
Antivirales/química , Inhibidores de Cisteína Proteinasa/química , Bases de Datos de Compuestos Químicos , Simulación del Acoplamiento Molecular , Simulación de Dinámica Molecular , /enzimología , Antivirales/uso terapéutico , /antagonistas & inhibidores , Inhibidores de Cisteína Proteinasa/uso terapéutico , Evaluación Preclínica de Medicamentos , Relación Estructura-Actividad Cuantitativa , /química
6.
Methods Mol Biol ; 2266: 171-186, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33759127

RESUMEN

Comparative Binding Energy (COMBINE) analysis is an approach for deriving a target-specific scoring function to compute binding free energy, drug-binding kinetics, or a related property by exploiting the information contained in the three-dimensional structures of receptor-ligand complexes. Here, we describe the process of setting up and running COMBINE analysis to derive a Quantitative Structure-Kinetics Relationship (QSKR) for the dissociation rate constants (koff) of inhibitors of a drug target. The derived QSKR model can be used to estimate residence times (τ, τ=1/koff) for similar inhibitors binding to the same target, and it can also help to identify key receptor-ligand interactions that distinguish inhibitors with short and long residence times. Herein, we demonstrate the protocol for the application of COMBINE analysis on a dataset of 70 inhibitors of heat shock protein 90 (HSP90) belonging to 11 different chemical classes. The procedure is generally applicable to any drug target with known structural information on its complexes with inhibitors.


Asunto(s)
Descubrimiento de Drogas/métodos , Proteínas HSP90 de Choque Térmico/química , Preparaciones Farmacéuticas/química , Programas Informáticos , Proteínas HSP90 de Choque Térmico/antagonistas & inhibidores , Cinética , Ligandos , Unión Proteica , Proteínas/antagonistas & inhibidores , Proteínas/química , Relación Estructura-Actividad Cuantitativa , Termodinámica
7.
Methods Mol Biol ; 2266: 155-170, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33759126

RESUMEN

Medicinal chemistry society has enough arguments to justify the usage of fragment-based drug design (FBDD) methodologies for the identification of lead compounds. Since the FDA approval of three kinase inhibitors - vemurafenib, venetoclax, and erdafitinib, FBDD has become a challenging alternative to high-throughput screening methods in drug discovery. The following protocol presents in silico drug design of selective histone deacetylase 6 (HDAC6) inhibitors through a fragment-based approach. To date, structural motifs that are important for HDAC inhibitory activity and selectivity are described as: surface recognition group (CAP group), aliphatic or aromatic linker, and zinc-binding group (ZBG). The main idea of this FBDD method is to identify novel and target-selective CAP groups by virtual scanning of publicly available fragment databases. Template structure used to search for novel heterocyclic and carbocyclic fragments is 1,8-naphthalimide (CAP group of scriptaid, a potent HDAC inhibitor). Herein, the design of HDAC6 inhibitors is based on linking the identified fragments with the aliphatic or aromatic linker and hydroxamic acid (ZBG) moiety. Final selection of potential selective HDAC6 inhibitors is based on combined structure-based (molecular docking) and ligand-based (three-dimensional quantitative structure-activity relationships, 3D-QSAR) techniques. Designed compounds are docked in the active site pockets of human HDAC1 and HDAC6 isoforms, and their docking conformations used to predict their HDAC inhibitory and selectivity profiles through two developed 3D-QSAR models (describing HDAC1 and HDAC6 inhibitory activities).


Asunto(s)
Descubrimiento de Drogas/métodos , Histona Desacetilasa 6/química , Inhibidores de Histona Desacetilasas/química , Simulación del Acoplamiento Molecular/métodos , Naftalimidas/química , Secuencias de Aminoácidos , Dominio Catalítico , Bases de Datos de Compuestos Químicos , Diseño de Fármacos , Histona Desacetilasa 1/antagonistas & inhibidores , Histona Desacetilasa 1/química , Histona Desacetilasa 6/antagonistas & inhibidores , Técnicas In Vitro , Ligandos , Conformación Molecular , Simulación de Dinámica Molecular , Relación Estructura-Actividad Cuantitativa , Bibliotecas de Moléculas Pequeñas , Relación Estructura-Actividad
8.
Methods Mol Biol ; 2266: 203-226, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33759129

RESUMEN

Computational prediction of protein-ligand binding involves initial determination of the binding mode and subsequent evaluation of the strength of the protein-ligand interactions, which directly correlates with ligand binding affinities. As a consequence of increasing computer power, rigorous approaches to calculate protein-ligand binding affinities, such as free energy perturbation (FEP) methods, are becoming an essential part of the toolbox of computer-aided drug design. In this chapter, we provide a general overview of these methods and introduce the QFEP modules, which are open-source API workflows based on our molecular dynamics (MD) package Q. The module QligFEP allows estimation of relative binding affinities along ligand series, while QresFEP is a module to estimate binding affinity shifts caused by single-point mutations of the protein. We herein provide guidelines for the use of each of these modules based on data extracted from ligand-design projects. While these modules are stand-alone, the combined use of the two workflows in a drug-design project yields complementary perspectives of the ligand binding problem, providing two sides of the same coin. The selected case studies illustrate how to use QFEP to approach the two key questions associated with ligand binding prediction: identifying the most favorable binding mode from different alternatives and establishing structure-affinity relationships that allow the rational optimization of hit compounds.


Asunto(s)
Descubrimiento de Drogas/métodos , Simulación de Dinámica Molecular , Proteínas/química , Algoritmos , Diseño Asistido por Computadora , Diseño de Fármacos , Técnicas In Vitro , Ligandos , Mutagénesis Sitio-Dirigida , Mutación , Unión Proteica , Relación Estructura-Actividad Cuantitativa , Termodinámica , Flujo de Trabajo
9.
J Chromatogr A ; 1641: 461994, 2021 Mar 29.
Artículo en Inglés | MEDLINE | ID: mdl-33676112

RESUMEN

A new approach of characterizing the specific properties of 4-(trans-4'-n-alkylcyclohexyl) benzoates, based on the interaction of the acidic and basic testing substances, for their form Crystalline, Smectic B and Nematic have been proposed. The testing substances selected for our study have been considered in the light of the results obtained in the previous research and the data available in the literature for other liquid crystals with different structures. The DN values denoting the electron donor number in the Gutmann scale and the AN* values indicating the acceptor number in the Riddle-Fowkes scale have been chosen in the estimation of the electron acceptor parameter KA and electron donor parameter KD values. The temperature-dependent quotients of KA to KD are employed for the assessment of the electron donor-acceptor properties of 4-(trans-4'-n-alkylcyclohexyl) benzoates. The 4-(trans-4'-n-alkyl cyclohexyl) benzoates tested have the affinity to act as a donor of electrons concerning electron acceptor of the testing substance. The sizes and shapes effects of the testing substances (or penetrants) and the LCs tested (or solvents while in mesophases) are taken into account. The inverse gas chromatography tests were supported by the Quantitative Structure-Activity Relationship modelling technique to determine which part (or group) of liquid crystals tested was dominant in the interaction with the testing substances.


Asunto(s)
Benzoatos/química , Cromatografía de Gases/métodos , Ciclohexanos/química , Electrones , Cristales Líquidos/química , Cloruro de Metileno/química , Relación Estructura-Actividad Cuantitativa , Solventes/química , Temperatura
10.
Molecules ; 26(4)2021 Feb 19.
Artículo en Inglés | MEDLINE | ID: mdl-33669834

RESUMEN

Applied datasets can vary from a few hundred to thousands of samples in typical quantitative structure-activity/property (QSAR/QSPR) relationships and classification. However, the size of the datasets and the train/test split ratios can greatly affect the outcome of the models, and thus the classification performance itself. We compared several combinations of dataset sizes and split ratios with five different machine learning algorithms to find the differences or similarities and to select the best parameter settings in nonbinary (multiclass) classification. It is also known that the models are ranked differently according to the performance merit(s) used. Here, 25 performance parameters were calculated for each model, then factorial ANOVA was applied to compare the results. The results clearly show the differences not just between the applied machine learning algorithms but also between the dataset sizes and to a lesser extent the train/test split ratios. The XGBoost algorithm could outperform the others, even in multiclass modeling. The performance parameters reacted differently to the change of the sample set size; some of them were much more sensitive to this factor than the others. Moreover, significant differences could be detected between train/test split ratios as well, exerting a great effect on the test validation of our models.


Asunto(s)
Algoritmos , Bases de Datos como Asunto , Relación Estructura-Actividad Cuantitativa , Intervalos de Confianza , Aprendizaje Automático
11.
Molecules ; 26(4)2021 Feb 20.
Artículo en Inglés | MEDLINE | ID: mdl-33672700

RESUMEN

Plants synthesize a large number of natural products, many of which are bioactive and have practical values as well as commercial potential. To explore this vast structural diversity, we present PSC-db, a unique plant metabolite database aimed to categorize the diverse phytochemical space by providing 3D-structural information along with physicochemical and pharmaceutical properties of the most relevant natural products. PSC-db may be utilized, for example, in qualitative estimation of biological activities (Quantitative Structure-Activity Relationship, QSAR) or massive docking campaigns to identify new bioactive compounds, as well as potential binding sites in target proteins. PSC-db has been implemented using the open-source PostgreSQL database platform where all compounds with their complementary and calculated information (classification, redundant names, unique IDs, physicochemical properties, etc.) were hierarchically organized. The source organism for each compound, as well as its biological activities against protein targets, cell lines and different organism were also included. PSC-db is freely available for public use and is hosted at the Universidad de Talca.


Asunto(s)
Bases de Datos de Compuestos Químicos , Fitoquímicos/química , Plantas/química , Simulación del Acoplamiento Molecular , Fitoquímicos/metabolismo , Plantas/metabolismo , Relación Estructura-Actividad Cuantitativa
12.
Ecotoxicol Environ Saf ; 214: 112114, 2021 May.
Artículo en Inglés | MEDLINE | ID: mdl-33711575

RESUMEN

Endocrine disrupting chemicals can mimic, block, or interfere with hormones in organisms and subsequently affect their development and reproduction, which has raised significant public concern over the past several decades. To investigate (quantitative) structure-activity relationship, 8280 compounds were compiled from the Tox21 10K compound library. The results show that 50% activity concentrations of agonists are poorly related to that of antagonists because many compounds have considerably different activity concentrations between the agonists and antagonists. Analysis on the chemical classes based on mode of action (MOA) reveals that estrogen receptor (ER) is not the main target site in the acute toxicity to aquatic organisms. Binomial analysis of active and inactive ER agonists/antagonists reveals that ER activity of compounds is dominated by octanol/water partition coefficient and excess molar refraction. The binomial equation developed from the two descriptors can classify well active and inactive ER chemicals with an overall prediction accuracy of 73%. The classification equation developed from the molecular descriptors indicates that estrogens react with the receptor through hydrophobic and π-n electron interactions. At the same time, molecular ionization, polarity, and hydrogen bonding ability can also affect the chemical ER activity. A decision tree developed from chemical structures and their applications reveals that many hormones, proton pump inhibitors, PAHs, progestin, insecticides, fungicides, steroid and chemotherapy medications are active ER agonists/antagonists. On the other hand, many monocyclic/nonaromatic chain compounds and herbicides are inactive ER compounds. The decision tree and binomial equation developed here are valuable tools to predict active and inactive ER compounds.


Asunto(s)
Disruptores Endocrinos/clasificación , Antagonistas de Estrógenos/clasificación , Estrógenos/clasificación , Receptores Estrogénicos/antagonistas & inhibidores , Árboles de Decisión , Disruptores Endocrinos/química , Disruptores Endocrinos/farmacología , Antagonistas de Estrógenos/química , Antagonistas de Estrógenos/farmacología , Estrógenos/química , Estrógenos/farmacología , Relación Estructura-Actividad Cuantitativa , Bibliotecas de Moléculas Pequeñas
13.
SAR QSAR Environ Res ; 32(4): 293-315, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-33655818

RESUMEN

Adipocyte fatty-acid binding protein (A-FABP) plays a central role in many aspects of metabolic diseases. It is an important target in drug design for treatment of FABP-related diseases. In this study, molecular dynamics (MD) simulations followed by calculations of molecular mechanics generalized Born surface area (MM-GBSA) and principal components analysis (PCA) were implemented to decipher molecular mechanism correlating with binding of inhibitors 57Q, 57P and L96 to A-FABP. The results show that van der Waals interactions are the leading factors to control associations of 57Q, 57P, and L96 with A-FABP, which reveals an energetic basis for designing of clinically available inhibitors towards A-FABP. The information from PCA and cross-correlation analysis rationally unveils that inhibitor bindings affect conformational changes of A-FABP and change relative movements between residues. Decomposition of binding affinity into contributions of individual residues not only detects hot spots of inhibitor/A-FABP binding but also shows that polar interactions of the positively charged residue Arg126 with three inhibitors provide a significant contribution for stabilization of the inhibitor/A-FABP bindings. Furthermore, the binding strength of L96 to residues Ser55, Phe57 and Lys58 are stronger than that of inhibitors 57Q and 57P to these residues.


Asunto(s)
Proteínas de Unión a Ácidos Grasos/química , Simulación de Dinámica Molecular , Análisis de Componente Principal , Relación Estructura-Actividad Cuantitativa
14.
SAR QSAR Environ Res ; 32(4): 269-291, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-33687299

RESUMEN

A library of 9-arylimino derivatives of noscapine was developed by coupling of Schiff base containing imine groups. Virtual screening using molecular docking with tubulin revealed three molecules, 12-14 that bind with high affinity. An improved predicted free energy of binding (FEB) of -5.390, -6.506 and -6.679 kcal/mol for the molecules 12-14 was found compared to noscapine (-5.135 kcal/mol). Furthermore, molecular dynamics simulation in combination with Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) revealed robust binding free energy of -166.03, -169.75 and -170.63 kcal/mol for the molecules 12, 13 and 14, respectively. These derivatives were strategically synthesized and experimentally validated for their anticancer activity. Tubulin binding assay revealed substantial binding of molecules 12-14 with purified tubulin. Further, their anticancer activity was demonstrated using two cancer cell lines (MCF-7 and MDAMB-231) and a panel of primary breast tumour cells. All these derivatives inhibited cellular proliferation in all the cancer cells that ranged between 30.1 and 5.8 µM, which is 1.7 to 7.52 fold lower than that of noscapine. Further, these novel derivatives arrest cell cycle in the G2/M-phase followed by induction of apoptosis. Thus, 9-arylimino noscapinoids 12-14 have a great potential to be a novel therapeutic agent for breast cancers.


Asunto(s)
Antineoplásicos/farmacología , Noscapina/análogos & derivados , Noscapina/farmacología , Apoptosis/efectos de los fármacos , Ciclo Celular/efectos de los fármacos , Proliferación Celular/efectos de los fármacos , Humanos , Células MCF-7/efectos de los fármacos , Simulación del Acoplamiento Molecular , Simulación de Dinámica Molecular , Relación Estructura-Actividad Cuantitativa , Tubulina (Proteína)/química , Células Tumorales Cultivadas/efectos de los fármacos
15.
SAR QSAR Environ Res ; 32(4): 317-331, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-33730950

RESUMEN

DNA replication is not only the basis of biological inheritance but also the most fundamental process in all living organisms. It plays a crucial role in the cell-division cycle and gene expression regulation. Hence, the accurate identification of the origin of replication sites (ORIs) has a great meaning for further understanding the regulatory mechanism of gene expression and treating genic diseases. In this paper, a novel, feasible and powerful model, namely, iORI-ENST is designed for identifying ORIs. Firstly, we extract the different features by incorporating mono-nucleotide binary encoding and dinucleotide-based spatial autocorrelation. Subsequently, elastic net is utilized as the feature selection method to select the optimal feature set. And then stacking learning is employed to predict ORIs and non-ORIs, which contains random forest, adaboost, gradient boosting decision tree, extra trees and support vector machine. Finally, the ORI sites are identified on the benchmark datasets S1 and S2 with their accuracies of 91.41% and 95.07%, respectively. Meanwhile, an independent dataset S3 is employed to verify the validation and transferability of our model and its accuracy reaches 91.10%. Comparing with state-of-the-art methods, our model achieves more remarkable performance. The results show our model is a feasible, effective and powerful tool for identifying ORIs. The source code and datasets are available at https://github.com/YingyingYao/iORI-ENST.


Asunto(s)
Origen de Réplica , Máquina de Vectores de Soporte , Relación Estructura-Actividad Cuantitativa , Programas Informáticos
16.
SAR QSAR Environ Res ; 32(4): 247-268, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-33749419

RESUMEN

The dependence of statistical validation parameters was investigated on the size of the sample taken in fit of multivariate linear curves. We observed that R2 and related internal parameters were misleading as they overestimated the goodness-of-fit of models at small sample size. Cross-validation metrics showed correct trends. It was possible to scale the leave-one-out and the leave-many-out results close to identical by correcting the degrees of freedom of the models. y and x-randomized validation parameters were calculated and the methods provided close to identical results. We suggest to use the simplest methods in both cases. The external parameters followed correct trends with respect to the sample size, but their sensitivity differed. We plotted the Roy-Ojha metrics in 2D and we coloured them with respect to other external parameters to provide an easy classification of models. The rank correlations were calculated between the performance parameters. Up to a sample size, goodness-of-fit and robustness were distinguishable, but above a certain sample size, the parameters were redundant. The external-internal pairs were weakly correlated. Our data show that all the three aspects of validation are necessary at small sample sizes, but the internal check of robustness is not informative above a given sample size.


Asunto(s)
Modelos Lineales , Relación Estructura-Actividad Cuantitativa , Tamaño de la Muestra
17.
SAR QSAR Environ Res ; 32(4): 333-352, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-33784906

RESUMEN

DprE1 is a potential target of resistant tuberculosis (TB), especially multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB. 2-benzoxazolinone is a closely related bioisostere of some scaffolds such as benzoxazoles, benzimidazole, benzothiazolinone, and benzothiazoles that have been previously explored against DprE1. Thus, a ligand-based quantitative pharmacophore model (AHRR.8) of DprE1 was developed and this pharmacophore model was utilized in activity profiling of some 2-benzoxazolinones from an in-house database using virtual screening. Obtained hits were subject to molecular docking, molecular dynamics (MD), and MM/GBSA calculations, which resulted in benzoyl-substituted derivatives of 2-benzoxazolinone showing strong interactions with the key amino acid residues in the active site of DprE1. Based on in silico results, the top five hits were duly synthesized and evaluated against the XDR-TB strain. This study is an initial effort to explore 2-benzoxazolinones against XDR-TB, which can be submitted further to lead optimization for refining the results.


Asunto(s)
Oxidorreductasas de Alcohol/química , Antituberculosos/química , Proteínas Bacterianas/química , Benzoxazoles/química , Simulación del Acoplamiento Molecular , Simulación de Dinámica Molecular , Simulación por Computador , Humanos , Relación Estructura-Actividad Cuantitativa
18.
Sci Total Environ ; 772: 145532, 2021 Jun 10.
Artículo en Inglés | MEDLINE | ID: mdl-33578164

RESUMEN

The application of nanomaterials is expanding. Therefore, it is necessary to investigate the relationship between the structure and toxicity of different nanomaterials. Quasi-SMILES is a line of symbols which are codes of corresponding conditions of experiments aimed to estimate the toxicity of ZnO nanoparticles towards the rat via intraperitoneal injections. By means of the Monte Carlo method, the so-called correlation weights for fragments of quasi-SMILES can be calculated. Having the numerical data on the correlation weights one can build up a one-variable model for the toxicity. The checking up of the approach with five random splits of all available data on results of thirty-six experiments into a sub-system of training and sub-system of validation has confirmed the significance of the statistical quality of models obtained with the above approach. The average determination coefficient equal to 0.957 (dispersion 0.010) and average root mean square error equal to 7.25 [mg/kg] (dispersion 0.59 [mg/kg]).


Asunto(s)
Nanopartículas , Óxido de Zinc , Animales , Método de Montecarlo , Nanopartículas/toxicidad , Relación Estructura-Actividad Cuantitativa , Ratas , Programas Informáticos , Óxido de Zinc/toxicidad
19.
SAR QSAR Environ Res ; 32(2): 85-110, 2021 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-33517778

RESUMEN

Tyrosinase is a key rate-limiting enzyme in the process of melanin synthesis, which is closely related to human pigmentation disorders. Tyrosinase inhibitors can down-regulate tyrosinase to effectively reduce melanin synthesis. In this work, we conducted structure-activity relationship (SAR) study on 1097 diverse mushroom tyrosinase inhibitors. We applied five kinds of machine learning methods to develop 15 classification models. Model 5B built by fully connected neural networks and ECFP4 fingerprints achieved the highest prediction accuracy of 91.36% and Matthews correlation coefficient (MCC) of 0.81 on the test set. The applicability domains (AD) of classification models were defined by d S T D - P R O method. Moreover, we clustered the 1097 inhibitors into eight subsets by K-Means to figure out inhibitors' structural features. In addition, 10 quantitative structure-activity relationship (QSAR) models were constructed by four machine learning methods based on 813 inhibitors. Model 6 J, the best QSAR model, was developed by fully connected neural networks with 50 RDKit descriptors. It resulted in a coefficient of determination (r 2) of 0.770 and a root mean squared error (RMSE) of 0.482 on the test set. The AD of Model 6 J was visualized by Williams plot. The models built in this study can be obtained from the authors.


Asunto(s)
Inhibidores Enzimáticos/química , Aprendizaje Automático , Monofenol Monooxigenasa/antagonistas & inhibidores , Redes Neurales de la Computación , Relación Estructura-Actividad , Relación Estructura-Actividad Cuantitativa
20.
SAR QSAR Environ Res ; 32(2): 151-174, 2021 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-33525942

RESUMEN

One step towards reduced animal testing is the use of in silico screening methods to predict toxicity of chemicals, which requires high-quality data to develop models that are reliable and clearly interpretable. We compiled a large data set of fish early life stage no observed effect concentration endpoints (FELS NOEC) based on published data sources and internal studies, containing data for 338 molecules. Furthermore, we developed a new quantitative structure-activity-activity relationship (QSAAR) model to inform estimation of this endpoint using a combination of dimensionality reduction, regularization, and domain knowledge. In particular, we made use of a sparse partial least squares algorithm (sPLS) to select relevant variables from a huge number of molecular descriptors ranging from topological to quantum chemical properties. The final QSAAR model is of low complexity, consisting of 2 latent variables based on 8 molecular descriptors and experimental Daphnia magna acute data (EC50, 48 h). We provide a mechanistic interpretation of each model parameter. The model performs well, with a coefficient of determination r 2 of 0.723 on the training set (cross-validated q 2 = 0.686) and comparable predictivity on a test data set of chemically related molecules with experimental Daphnia magna data (r 2 test = 0.687, RMSE = 0.793 log units).


Asunto(s)
Daphnia/efectos de los fármacos , Peces/metabolismo , Larva/efectos de los fármacos , Relación Estructura-Actividad Cuantitativa , Pruebas de Toxicidad/veterinaria , Animales , Análisis de los Mínimos Cuadrados , Modelos Biológicos , Pruebas de Toxicidad/instrumentación
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