Validación Chilena del Cuestionario de Evaluación de Apego en el adulto CaMir versión abreviada

El Cuestionario de Evaluación de Apego en el adulto CaMir, es un instrumento de auto-reporte basado en la Teoría del Apego que evalúa los modelos de relación en adultos y permite describir las estrategias de apego. El objetivo de la presente investigación fue desarrollar una versión breve para el contexto chileno, para lo cual se llevaron a cabo dos estudios. En el Estudio 1 se obtuvo una versión reducida del CaMir bajo una estrategia de validación cruzada. En el Estudio 2, se aplicó la versión reducida obtenida en el estudio 1 a una amplia muestra de adultos/as chilenos/as (n=1246). Los resultados muestran evidencias que apoyan la estructura interna propuesta, y los análisis de invarianza apoyan la existencia de equivalencia/invarianza de medida entre hombres y mujeres. Adicionalmente, esta versión obtuvo correlaciones significativas con la Escala de Dificultades de Regulación Emocional, el cuestionario de Experiencias en Relaciones Cercanas y la Escala de Depresión del Centro de Estudios Epidemiológicos. En síntesis, los resultados muestran que esta nueva versión abreviada del CaMir, es un instrumento apropiado para el estudio de los modelos de relación y las estrategias de apego en adultos/as chilenos/as.

The CaMir Adult Attachment Assessment Questionnaire is a self-report instrument based on Attachment Theory that assesses relationship models in adults and allows describing attachment strategies. The objective of this research was to develop a short version for the Chilean context. The results of two studies are presented. In Study 1, a reduced version of CaMir was obtained under a cross-validation strategy. In Study 2, the reduced version obtained in Study 1 was applied to a large sample of Chilean adults (n=1246). The results show evidence that supports the proposed internal structure, and the invariance analysis support the existence of equivalence/invariance of measurement between men and women. Additionally, this version obtained significant correlations with the Difficulties in Emotion Regulation Scale, the Experiences in Close Relationships questionnaire and the Center for Epidemiologic Studies Depression Scale. In summary, the results show that this new abbreviated version of the CaMir is an appropriate instrument for the study of relationship models and attachment strategies in Chilean adults.

Comparison of supercritical fluid chromatographic methods to predict the skin permeability of pharmaceutical and cosmetic compounds.

Supercritical fluid chromatography (SFC) was explored as an alternative for liquid chromatography to predict the skin permeability of pharmaceutical and cosmetic compounds. Nine dissimilar stationary phases were applied to screen a test set of 58 compounds. The experimental retention factors (log k), in addition to two sets of theoretical molecular descriptors, were applied to model the skin permeability coefficient. Different modelling approaches, i.e. multiple linear regression (MLR) and partial least squares (PLS) regression, were used. In general, the MLR models performed better than the PLS models for a given descriptor set. The results obtained on a cyanopropyl (CN) column provided the best correlation with the skin permeability data. The retention factors obtained on this column were included in a simple MLR model, together with the octanol-water partition coefficient and the number of atoms (r² = 0.81, RMSEC = 0.537 or 20.5% and RMSECV = 0.580 or 22.1%). The overall best MLR model included the chromatographic descriptor from a phenyl column and 18 descriptors (r² = 0.98, RMSEC = 0.167 or 6.2% and RMSECV = 0.238 or 8.9%). This model showed a good fit, on top of very good predictive features. However, stepwise MLR models with a reduced complexity could also be determined, with the best performance parameters obtained with the CN-column based retention and eight descriptors (r² = 0.95, RMSEC = 0.282 or 10.7% and RMSECV = 0.353 or 13.4%). SFC thus provides a suitable alternative to the liquid chromatographic techniques previously applied to model the skin permeability.

The secret of reversed-phase/weak cation exchange retention mechanisms in mixed-mode liquid chromatography applied for small drug molecule analysis.

Resolving complex sample mixtures by liquid chromatography in a single run is challenging. The so-called mixed-mode liquid chromatography (MMLC) which combines several retention mechanisms within a single column, can provide resource-efficient separation of solutes of diverse nature. The Acclaim Mixed-Mode WCX-1 column, encompassing hydrophobic and weak cation exchange interactions, was employed for the analysis of small drug molecules. The stationary phase's interaction abilities were assessed by analysing molecules of different ionisation potentials. Mixed Quantitative Structure-Retention Relationship (QSRR) models were developed for revealing significant experimental parameters (EPs) and molecular features governing molecular retention. According to the plan of Face-Centred Central Composite Design, EPs (column temperature, acetonitrile content, pH and buffer concentration of aqueous mobile phase) variations were included in QSRR modelling. QSRRs were developed upon the whole data set (global model) and upon discrete parts, related to similarly ionized analytes (local models) by applying gradient boosted trees as a regression tool. Root mean squared errors of prediction for global and local QSRR models for cations, anions and neutrals were respectively 0.131; 0.105; 0.102 and 0.042 with the coefficient of determination 0.947; 0.872; 0.954 and 0.996, indicating satisfactory performances of all models, with slightly better accuracy of local ones. The research showed that influences of EPs were dependant on the molecule's ionisation potential. The molecular descriptors highlighted by models pointed out that electrostatic and hydrophobic interactions and hydrogen bonds participate in the retention process. The molecule's conformation significance was evaluated along with the topological relationship between the interaction centres, explicitly determined for each molecular species through local models. All models showed good molecular retention predictability thus showing potential for facilitating the method development.

Multi-Targeting Approach in Glioblastoma Using Computer-Assisted Drug Discovery Tools to Overcome the Blood-Brain Barrier and Target EGFR/PI3Kp110ß Signaling.

The epidermal growth factor receptor (EGFR) is upregulated in glioblastoma, becoming an attractive therapeutic target. However, activation of compensatory pathways generates inputs to downstream PI3Kp110ß signaling, leading to anti-EGFR therapeutic resistance. Moreover, the blood-brain barrier (BBB) limits drugs' brain penetration. We aimed to discover EGFR/PI3Kp110ß pathway inhibitors for a multi-targeting approach, with favorable ADMET and BBB-permeant properties. We used quantitative structure-activity relationship models and structure-based virtual screening, and assessed ADMET properties, to identify BBB-permeant drug candidates. Predictions were validated in in vitro models of the human BBB and BBB-glioma co-cultures. The results disclosed 27 molecules (18 EGFR, 6 PI3Kp110ß, and 3 dual inhibitors) for biological validation, performed in two glioblastoma cell lines (U87MG and U87MG overexpressing EGFR). Six molecules (two EGFR, two PI3Kp110ß, and two dual inhibitors) decreased cell viability by 40-99%, with the greatest effect observed for the dual inhibitors. The glioma cytotoxicity was confirmed by analysis of targets' downregulation and increased apoptosis (15-85%). Safety to BBB endothelial cells was confirmed for three of those molecules (one EGFR and two PI3Kp110ß inhibitors). These molecules crossed the endothelial monolayer in the BBB in vitro model and in the BBB-glioblastoma co-culture system. These results revealed novel drug candidates for glioblastoma treatment.

Predicting skin permeability of pharmaceutical and cosmetic compounds using retention on octadecyl, cholesterol-bonded and immobilized artificial membrane columns.

In this study, the retention on three types of columns, an immobilized artificial membrane (IAM), a cholesterol-bonded and an octadecyl (C18) column, was applied for the prediction of skin permeability. The first two columns are biomimicking ones, which have certain components of the skin bound to the stationary phase, and were applied in HPLC, while the sub-2 µm C18 column was studied in UHPLC because of its fast features. Fifty-eight compounds were analyzed applying different mobile-phase compositions, with varying percentages of organic modifier on every column, to extrapolate the retention factor to a theoretically purely aqueous mobile phase (log kw). The retention factors, along with two sets of theoretical molecular descriptors, were used to model the skin permeability coefficient (log Kp) using multiple linear regression (MLR) and partial least squares (PLS) regression modelling. Although the retention factors (log k) on the IAM column showed a better correlation with the skin permeability, the overall best model was obtained by applying a stepwise MLR approach on the UHPLC parameters combined with some theoretical descriptors. This model showed a good fit, and on top has potential to accurately predict skin permeability values. Furthermore, the UHPLC method has the advantage of being fast and can thus be classified as a high-throughput approach.

In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives.

This chapter aims to introduce the reader to the basic principles of environmental risk assessment of chemicals and highlights the usefulness of tiered approaches within weight of evidence approaches in relation to problem formulation i.e., data availability, time and resource availability. In silico models are then introduced and include quantitative structure-activity relationship (QSAR) models, which support filling data gaps when no chemical property or ecotoxicological data are available. In addition, biologically-based models can be applied in more data rich situations and these include generic or species-specific models such as toxicokinetic-toxicodynamic models, dynamic energy budget models, physiologically based models, and models for ecosystem hazard assessment i.e. species sensitivity distributions and ultimately for landscape assessment i.e. landscape-based modeling approaches. Throughout this chapter, particular attention is given to provide practical examples supporting the application of such in silico models in real-world settings. Future perspectives are discussed to address environmental risk assessment in a more holistic manner particularly for relevant complex questions, such as the risk assessment of multiple stressors and the development of harmonized approaches to ultimately quantify the relative contribution and impact of single chemicals, multiple chemicals and multiple stressors on living organisms.

Evaluating micellar liquid chromatographic methods on octadecyl particle-based and monolithic columns to predict the skin permeation of drug and cosmetic molecules.

A micellar liquid chromatographic method was developed to assist in the modeling of the skin permeability of pharmaceutical and cosmetic compounds. The composition of the mobile phase was determined by means of a two-factor central composite design, after which it was tested on both a particle-based and monolithic column. The latter provided the opportunity to increase the flow rate from 1 to 8 mL/min without reaching too high backpressures. The micellar conditions allowed analyzing a large test set of compounds with diverse characteristics with just one mobile-phase composition. The obtained experimental chromatographic descriptors besides two sets of theoretical molecular descriptors were used to model the skin permeability coefficient log Kp, applying multiple linear regression and partial least squares regression approaches. The micellar method on the monolithic column provided useful models with similar or even slightly better performance parameters than the method on the particle-based column. Furthermore, a much faster analysis can be achieved when applying a flow rate of 8 mL/min, making the micellar monolithic method ideal to estimate skin permeability.

Adaptación y validación preliminar argentina del cuestionario de evaluación del apego (CaMir) / Adaptació i validació preliminar argentina del qüestionari d'avaluació de l’aferrament (CaMir) / Preliminary Argentinean adaptation and validation of the bonding assessment questionnaire (CaMir)

El propósitode este estudio fue adaptar lingüísticamente y evaluar validez y fiabilidad del instrumento de evaluación demodelos individuales de relación del estilo de apego, CaMir (Cartes: Modèles Individuels de Relation), en unamuestra argentina de adolescentes y adultos. Se trabajó con una muestra de 549 sujetos de ambos sexos deentre 14 y 80 años. El análisis factorial exploratorio develó una estructura de cuatro factores y el análisis fac-torial confirmatorio corroboró la misma, así como los índices de fiabilidad de los factores. Se confirma que esun instrumento válido y confiable para evaluar los modelos individuales de relación en adolescentes y adultosargentinos. Finalmente, se señalan limitaciones y contribuciones.(AU)

The pur-pose of this study was to linguistically adapt and evaluate the validity and reliability of the individual modelsof attachment style relationship assessment instrument, CaMir (Cartes: Modèles Individuels de Relation), in anArgentinean sample of adolescents and adults. A sample of 549 subjects of both sexes aged between 14 and80 years was used. The exploratory factor analysis revealed a four-factor structure and the confirmatory factoranalysis corroborated it, as well as the reliability indices of the factors. It is confirmed that it is a valid and reliableinstrument to assess individual relationship patterns in Argentinean adolescents and adults. Finally, limitations andcontributions are pointed out.(AU)

El propòsitd'aquest estudi va ser adaptar lingüísticament i avaluar la validesa i la fiabilitat de l'instrument d'avaluació demodels individuals de relació de l'estil d'aferrament, CaMir (Cartes: Modèles Individuels de Relation), en una mos-tra argentina d'adolescents i adults. Es va treballar amb una mostra de 549 subjectes d'ambdós sexes d'entre14 i 80 anys. L'anàlisi factorial exploratòria va desvetllar una estructura de quatre factors i l'anàlisi factorial con-firmatori la va corroborar, així com els índexs de fiabilitat dels factors. Es confirma que és un instrument vàlidi fiable per avaluar els models individuals de relació en adolescents i adults argentins. Finalment, s'assenyalenlimitacions i contribucions.(AU)

The paradoxes of love in the Spanish family: a sociological approach.

This article defines the Spanish family in the context of the "Mediterranean model" and the "individualization society". The former is characterised by strong social interrelationships between family members and their emotional ties, while the latter is defined by the separateness of citizens and by institutionalising the basis of society in individuals rather than in the family. The work also describes how modern forms of love, both romantic and confluent, are institutionalized in this society, discussing if they coexist or not, how they exist, and which is the dominant form. Finally, it analyzes the degree of strength or fragility of the family institution and the affective relationships that sustain it.  The work concludes that the Spanish family is balancing between the strong resistance to disappear as an institution and its eclipse, crisis, or complete end. This is because, although the Spanish family still retains a large part of its former functions, at the same time as divorce is on the increase and family members are decreasing, it is increasingly ephemeral and a plurality of family forms have emerged that have broken with the traditional dominant model of lifelong romantic marriage. Moreover, the Spanish family is also among the "familist" model and the individual, while the way of loving fluctuates between the traditional patriarchal and the democratic, individual, and communitary. Thus, the thesis I propose qualifies and questions the majority of theoretical works on love and the Spanish family, which argue that the family is inscribed in the "Mediterranean model". As will be seen, the romantic relationships that have been institutionalised in the Spanish family are more paradoxical, insofar as they are still inscribed in that model, but they are rapidly approaching those of Northern Europe.

Feature importance of machine learning prediction models shows structurally active part and important physicochemical features in drug design.

The objective of this study was to obtain the indicators of physicochemical parameters and structurally active sites to design new chemical entities with desirable pharmacokinetic profiles by investigating the process by which machine learning prediction models arrive at their decisions, which are called explainable artificial intelligence. First, we developed the prediction models for metabolic stability, CYP inhibition, and P-gp and BCRP substrate recognition using 265 physicochemical parameters for designing the molecular structures. Four important parameters, including the well-known indicator h_logD, are common in some in vitro studies; as such, these can be used to optimize compounds simultaneously to address multiple pharmacokinetic concerns. Next, we developed machine learning models that had been programmed to show structurally active sites. Many types of machine learning models were developed using the results of in vitro metabolic stability study of around 30000 in-house compounds. The metabolic sites of in-house compounds predicted using some prediction models matched experimentally identified metabolically active sites, with a ratio of number of metabolic sites (predicted/actual) of over 90%. These models can be applied to several screening projects. These two approaches can be employed for obtaining lead compounds with desirable pharmacokinetic profiles efficiently.

Comparison of in-silico modelling and reversed-phase liquid chromatographic retention on an octadecyl silica column to predict skin permeability of pharmaceutical and cosmetic compounds.

This study focuses on the in-silico modelling of the skin permeability using a test set of pharmaceutical and cosmetic compounds. Two sets of theoretical molecular descriptors, obtained from the E-Dragon and Vega ZZ software programs, were used in the models. Different linear regression methods, i.e. Multiple Linear Regression (MLR) and Partial Least Squares (PLS) regression, were applied for modelling and estimating the skin permeability. The best model was obtained using a stepwise MLR approach on the E-Dragon descriptor set. In a second step, the retention of the test set compounds was measured on a C18 column at two pH levels: pH 5.5 and pH 7. Different organic-modifier fractions were applied in the mobile phase to be able to extrapolate the retention factors to a log kw value, with kw the estimated retention factor in an aqueous mobile phase without organic modifier. Thereafter it was examined whether combining this chromatographic descriptor with the theoretical descriptors could improve the modelling of the skin permeability. The chromatographic descriptor often did not show an added value compared to the models containing only theoretical descriptors. Therefore, the in-silico models were preferred, and these models could be useful to predict the skin permeability of pharmaceutical and cosmetic compounds.

Modeling Microbial Community Networks: Methods and Tools.

In the current research landscape, microbiota composition studies are of extreme interest, since it has been widely shown that resident microorganisms affect and shape the ecological niche they inhabit. This complex micro-world is characterized by different types of interactions. Understanding these relationships provides a useful tool for decoding the causes and effects of communities' organizations. Next-Generation Sequencing technologies allow to reconstruct the internal composition of the whole microbial community present in a sample. Sequencing data can then be investigated through statistical and computational method coming from network theory to infer the network of interactions among microbial species. Since there are several network inference approaches in the literature, in this paper we tried to shed light on their main characteristics and challenges, providing a useful tool not only to those interested in using the methods, but also to those who want to develop new ones. In addition, we focused on the frameworks used to produce synthetic data, starting from the simulation of network structures up to their integration with abundance models, with the aim of clarifying the key points of the entire generative process.

In-silico screening of database for finding potential sweet molecules: A combined data and structure based modeling approach.

In this study, we present a framework comprises of several independent modules which are built upon data based (structure activity relationship and classification model) and structure (molecular docking) based for identifying possible sweeteners from a vast database of natural molecules. A large database, Universal Natural Products Database (UNPD) consisting of 213,210 compounds was screened using the developed framework. At first, 10,184 molecules structurally similar to the known sweeteners were identified in the database. Further, 1924 molecules from these screened molecules were classified as sweet molecules. The shortlisted 1354 molecules were subjected to ADMET analysis. Finally, 60 molecules were arrived at with no toxicity and acceptable oral bioavailability as potential sweetener candidates. Further, molecular docking of these molecules on sweet taste receptor performed to obtain their binding energy, binding sites and correlation with sweetness index. The developed framework offers a convenient route for fast screening of molecules prior to synthesis and testing.

Modeling Crop Genetic Resources Phenotyping Information Systems.

Documentation of phenotype information is a priority need in biodiversity, crop modeling, breeding, ecology, and evolution research, for association studies, gene discovery, retrospective statistical analysis and data mining, QTL re-mapping, choosing cultivars, and planning crosses. Lack of access to phenotype information is still seen as a limiting factor for the use of plant genetic resources. Phenotype data are complex. Information on the context, under which they were collected, is indispensable, and the domain is continuously evolving. This study describes comprehensive data and object models supporting web interfaces for multi-site field phenotyping and data acquisition, which have been developed for Central Crop Databases within the European Cooperative Programme for Plant Genetic Resources over the years and which can be used as blueprints for phenotyping information systems. We start from the hypothesis, that entity relationship and object models useful for software development can picture domain expertise, similar as domain ontologies, and encourage a discussion of scientific information systems on modeling level. Starting from information requirements for statistical analysis, meta-analysis, and knowledge discovery, models are discussed in consideration of several standardization and modeling approaches including crop ontologies. Following an object-oriented modeling approach, we keep data and object models close together and to domain concepts. This will make database and software design better understandable and usable for domain experts and support a modular use of software artifacts to be shared across various domains of expertise. Classes and entities represent domain concepts with attributes naturally assigned to them. Field experiments with randomized plots, as typically used in the evaluation of plant genetic resources and in plant breeding, are in the focus. Phenotype observations, which can be listed as raw or aggregated data, are linked to explanatory metadata describing experimental treatments and agronomic interventions, observed traits and observation methodology, field plan and plot design, and the experiment site as a geographical entity. Based on clearly defined types, potential links to information systems in other domains (e.g., geographic information systems) can be better identified. Work flows are shown as web applications for the generation of field plans, field books, templates, upload of spreadsheet data, and images.

Quantitative structure-property relationship modeling of the C60 fullerene derivatives as electron acceptors of polymer solar cells: Elucidating the functional groups critical for device performance.

Using the GUSAR 2013 program, we have performed a quantitative analysis of the "structure-power conversion efficiency (PCE)" on the series of 100 methano[60]fullerenes previously tested as acceptor components of bulk-heterojunction polymer organic solar cells (PSCs) utilizing the same donor polymer, viz. poly(3-hexylthiophene). Based on the MNA and QNA descriptors and self-consistent regression implemented in the program, six statistically significant consensus models for predicting the PCE values of the methano[60]fullerene-based PSCs have been constructed. The structural fragments of the fullerene compounds leading to an increase in the device performances are determined. Based on these structural descriptors, we have designed the three methano[60]fullerenes included in the training sets and characterized by poor optoelectrical properties is performed. As a result, two new compounds with potentially moderate efficiency have been proposed. This result opens opportunities of using the GUSAR 2013 program for modeling of the "structure-PCE" relationship for diverse compounds (not only fullerene derivatives).

Development of a new methodology for validating thermal storage media: Application to phase change materials.

Long-term stability and long-term performance of thermal storage media are a key issue that should be thoroughly analysed when developing storage systems. However, no testing protocol or guideline exists up to now for validating storage media, so that authors apply their own criteria, not only for designing testing procedures but also for predicting the material behaviour under long-term operation. This paper aims to cover this gap by proposing a methodology for validating thermal storage media; in particular, phase change materials (PCMs). This methodology consists of different stages that include PCM characterization, preliminary assessment tests, and accelerated life testing. For designing the accelerated life tests, lifetime relationship models have to be obtained in order to predict PCM long-term behaviour under service conditions from shorter tests performed under stress conditions. The approach followed in this methodology will be valid for materials to be used as sensible or thermochemical storage media, too.

Machine learning-enabled discovery and design of membrane-active peptides.

Antimicrobial peptides are a class of membrane-active peptides that form a critical component of innate host immunity and possess a diversity of sequence and structure. Machine learning approaches have been profitably employed to efficiently screen sequence space and guide experiment towards promising candidates with high putative activity. In this mini-review, we provide an introduction to antimicrobial peptides and summarize recent advances in machine learning-enabled antimicrobial peptide discovery and design with a focus on a recent work Lee et al. Proc. Natl. Acad. Sci. USA 2016;113(48):13588-13593. This study reports the development of a support vector machine classifier to aid in the design of membrane active peptides. We use this model to discover membrane activity as a multiplexed function in diverse peptide families and provide interpretable understanding of the physicochemical properties and mechanisms governing membrane activity. Experimental validation of the classifier reveals it to have learned membrane activity as a unifying signature of antimicrobial peptides with diverse modes of action. Some of the discriminating rules by which it performs classification are in line with existing "human learned" understanding, but it also unveils new previously unknown determinants and multidimensional couplings governing membrane activity. Integrating machine learning with targeted experimentation can guide both antimicrobial peptide discovery and design and new understanding of the properties and mechanisms underpinning their modes of action.

QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression.

Modified quantitative structure retention relationships (QSRRs) are proposed and applied to describe two retention data sets: A set of 94 metabolites studied by a hydrophilic interaction chromatography system under organic content gradient conditions and a set of tryptophan and its major metabolites analyzed by a reversed-phase chromatographic system under isocratic as well as pH and/or simultaneous pH and organic content gradient conditions. According to the proposed modification, an additional descriptor is added to a conventional QSRR expression, which is the analyte retention time, tR(R), measured under the same elution conditions, but in a second chromatographic column considered as a reference one. The 94 metabolites were studied on an Amide column using a Bare Silica column as a reference. For the second dataset, a Kinetex EVO C18 and a Gemini-NX column were used, where each of them was served as a reference column of the other. We found in all cases a significant improvement of the performance of the QSRR models when the descriptor tR(R) was considered.

Prediction and mechanism elucidation of analyte retention on phospholipid stationary phases (IAM-HPLC) by in silico calculated physico-chemical descriptors.

The present study proposes a method for an in silico calculation of phospholipophilicity. Phospholipophilicity is intended as the measure of analyte affinity for phospholipids; it is currently assessed by HPLC measures of analyte retention on phosphatidylcholine-like stationary phases (IAM - Immobilized Artificial Membrane) resulting in log kWIAM values. Due to the amphipathic and electrically charged nature of phospholipids, retention on these stationary phases results from complex mechanisms, being affected not only by lipophilicity (as measured by n-octanol/aqueous phase partition coefficients, log P) but also by the occurrence of polar and/or electrostatic intermolecular interaction forces. Differently from log P, to date no method has been proposed for in silico calculation of log kWIAM. The study is aimed both at shedding new light into the retention mechanism on IAM stationary phases and at offering a high-throughput method to achieve such values. A wide set of physico-chemical and topological properties were taken into account, yielding a robust final model including four in silico calculated parameters (lipophilicity, hydrophilic/lipophilic balance, molecular size, and molecule flexibility). The here presented model was based on the analysis of 205 experimentally determined values, taken from the literature and measured by a single research group to minimize the interlaboratory variability; such model is able to predict phospholipophilicity values on both the two IAM stationary phases to date marketed, i.e. IAM.PC.MG and IAM.PC.DD2, with a fairly good degree (r2=0.85) of accuracy. The present work allowed the development of a free on-line service aimed at calculating log kWIAM values of any molecule included in the PubChem database, which is freely available at http://nova.disfarm.unimi.it/logkwiam.htm.

Prediction of selective estrogen receptor beta agonist using open data and machine learning approach.

BACKGROUND: Estrogen receptors (ERs) are nuclear transcription factors that are involved in the regulation of many complex physiological processes in humans. ERs have been validated as important drug targets for the treatment of various diseases, including breast cancer, ovarian cancer, osteoporosis, and cardiovascular disease. ERs have two subtypes, ER-α and ER-ß. Emerging data suggest that the development of subtype-selective ligands that specifically target ER-ß could be a more optimal approach to elicit beneficial estrogen-like activities and reduce side effects. METHODS: Herein, we focused on ER-ß and developed its in silico quantitative structure-activity relationship models using machine learning (ML) methods. RESULTS: The chemical structures and ER-ß bioactivity data were extracted from public chemogenomics databases. Four types of popular fingerprint generation methods including MACCS fingerprint, PubChem fingerprint, 2D atom pairs, and Chemistry Development Kit extended fingerprint were used as descriptors. Four ML methods including Naïve Bayesian classifier, k-nearest neighbor, random forest, and support vector machine were used to train the models. The range of classification accuracies was 77.10% to 88.34%, and the range of area under the ROC (receiver operating characteristic) curve values was 0.8151 to 0.9475, evaluated by the 5-fold cross-validation. Comparison analysis suggests that both the random forest and the support vector machine are superior for the classification of selective ER-ß agonists. Chemistry Development Kit extended fingerprints and MACCS fingerprint performed better in structural representation between active and inactive agonists. CONCLUSION: These results demonstrate that combining the fingerprint and ML approaches leads to robust ER-ß agonist prediction models, which are potentially applicable to the identification of selective ER-ß agonists.