QSAR MODELLING ER-BETA
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Estrogen receptors (ERs) are basically the group of proteins found inside and on the cells & Estrogen (17β-estradiol) is the activator of Ers. The estrogen belongs to the sex steroid hormones & plays crucial role in female reproductive system and also exerts important effects on non-reproductive targets such as bone, cardiovascular system and neural sites involved in cognition. ERβ is expressed in a variety of the normal and malignant tissues, modulation of immune responses. Quantitative Structure-Activity Relationship(QSAR) is an approach which is designed to find relationship between the chemical structure (structural related properties) and the biological activity (target property) of the studied compounds. So to identify & study various modulators for the ER-Beta and their function has been performed.
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QSAR MODELLING ER-BETA MODULATOR
This document discusses developing a quantitative structure-activity relationship (QSAR) model to analyze modulators of the estrogen receptor beta (ER-β). ER-β is expressed in various tissues and plays roles in diseases. The objectives are to study ER-β's role in diseases, identify potent ER-β inhibitors and their modulation activity, and develop 2D and 3D QSAR models. So far, regression analysis and ER-β receptor structures from databases have been studied. Further work involves identifying ER-β modulators, QSAR modeling techniques, factors influencing ER-β modulation, developing a QSAR model, analyzing results, and preparing a report.
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This document describes a study that uses machine learning classifiers to detect depression using data from Twitter posts. Several classifiers are trained and tested on a dataset of 20,000 tweets from various user profiles. Features like sentiment, word frequency, and user account data are extracted from the tweets. Various classifiers like Extra Tree Classifier, Logistic Regression, and Naive Bayes are compared for their ability to accurately detect depression. The Extra Tree Classifier is found to have the best performance with 94% accuracy and 97.29% precision.
Improve The Performance of K-means by using Genetic Algorithm for Classificat...
This document summarizes a research paper that used genetic algorithms to improve the performance of the K-means clustering algorithm for classifying heart attack cases. The paper first classified a dataset of 270 heart disease cases using only K-means, which achieved an accuracy of 68.1481%. It then proposed a two-stage method: 1) Using genetic algorithm to select important predictive features for classification. 2) Applying K-means clustering using only the selected features. This improved approach increased the classification accuracy to 84.0741%. The paper concluded that genetic algorithm effectively reduced irrelevant features and strengthened the performance of K-means classification for the heart disease dataset.
Rule-Based Mamdani-Type Fuzzy Modeling of Perceived Stress, And Cortisol Resp...
In this paper, Two Mamdani type fuzzy models (four inputs–one output and two inputs–one output) were developed to test the hypothesis that high job demands and low job control (job strain) are associated with elevated free cortisol levels early in the working day and with reduced variability across the day and to evaluate the contribution of anger expression to this pattern. The models were derived from multiple data sources including One hundred five school teachers (41 men and 64 women) classified 12 months earlier as high (N = 48) or low (N = 57) in job strain according to the demand/control model sampled saliva at 2-hour intervals from 8:00 to 8:30 hours to 22:00 to 22:30 hours on a working day. The quality of the model was determined by comparing predicted and actual fuzzy classification and defuzzification of the predicted outputs to get crisp values for correlating estimates with published values. A modified form of the Hamming distance measure is proposed to compare predicted and actual fuzzy classification. An entropy measure is used to describe the ambiguity associated with the predicted fuzzy outputs. The four input model predicted over 70% of the test data within one-half of a fuzzy class of the published data. The two input model predicted over 40% of the test data within one-half of a fuzzy class of the published data. Comparison of the models show that the four input model exhibited less entropy than the two input model.
Classification of Enzymes Using Machine Learning Based Approaches: A Review
This document summarizes machine learning techniques for classifying enzymes using their amino acid sequences. It first reviews different feature extraction and selection methods used to represent enzyme sequences as numerical data for machine learning models. It then describes several machine learning classification algorithms that have been applied to the problem, including support vector machines, random forests, neural networks, and k-nearest neighbors. Finally, it outlines the general multi-level classification approach used, where models first distinguish enzymes from non-enzymes, then classify enzymes by functional class, and further classify by sub-class. Overall accuracy of 100% was reported for the first level and over 88% for the second and third levels using random forests with optimal features selected by support vector machines.
L018147377
This document proposes an improved genetic algorithm called DGA that combines genetic algorithm and differential evolution. DGA uses adaptive differential evolution as its mutation operator instead of simple genetic algorithm's crossover and mutation. It also adds strategies of optimal reservation and worst elimination. Simulation results show DGA has stronger global optimization ability, faster convergence speed and better stability compared to simple genetic algorithm.
Development of Predictor for Sequence Derived Features From Amino Acid Sequen...
Drug Discovery process include target identification i.e. to identify a target protein whose inhibition can destroy the pathogen. In testing phase, clinical and pre-clinical trials are done on the animals and then on humans. After the discovery process, the drug or medicine is made available for public use. But if the testing of the drug is ineffective or unable to yield the appropriate results, then the whole process need to be repeated. This makes the first stage of drug discovery the most important than the other stages. The present work will assist in the process of drug discovery. The present work involves the development of a model that extracts the sequence derived features from the given amino acid sequence using associative rule mining. Associative rule mining is a data mining technique useful to identify related items and to develop rules. In the present work, various parameters of the amino acid sequence are studied that affect the sequence-derived features and some of the equations and algorithms are implemented. Input is given through text file and collective results are obtained. MATLAB environment is used for the implementation. The results are compared with the previous bioinformatics tools. The model developed assists in protein class prediction process which assists drug discoverers in the drug discovery process.
Data Analysis. Predictive Analysis. Activity Prediction that a subject perfor...
This document summarizes an analysis that used data from smartphone sensors to predict human activities. Several classification techniques were tested on a dataset containing sensor measurements for activities like walking, sitting, and climbing stairs. The support vector machine (SVM) algorithm achieved the highest accuracy of 91.52% at predicting activities. Some activities like sitting and standing were more difficult to distinguish compared to laying down. Increasing the amount of training data could potentially improve prediction accuracy further.
Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Lev...
Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics. Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognomies in the movement configurations of autistic persons are not adequately studied, hindering the advances in understanding the etiology of motor mutilation. Subject aspects such as behavioral characters that influences ASD need further exploration. Presently, limited autism datasets concomitant with screening ASD are available, and a majority of them are genetic. Hence, in this study, we used a dataset related to autism screening enveloping ten behavioral and ten personal attributes that have been effective in diagnosing ASD cases from controls in behavior science. ASD diagnosis is time exhaustive and uneconomical. The burgeoning ASD cases worldwide mandate a need for the fast and economical screening tool. Our study aimed to implement an artificial neural network with the Levenberg- Marquardt algorithm to detect ASD and examine its predictive accuracy. Consecutively, develop a clinical decision support system for early ASD identification.
Effect of 3D parameters on Antifungal Activities of Some Heterocyclic Compounds
Quantitative Structure Activity Relationships (QSAR) of some heterocyclic compounds was studied using some 3D parameters. The QSAR models indicated that Dipole Y, Dipole mag., Y length and some indicator parameters are very effective in describing the antifungal activities of these compounds against Candida albicans in the training and external test set. The multiple regression analysis have produced well predictive statistically significant and cross validated QSAR models which help to explore some expectedly potent compounds.
Generic approach for predicting unannotated protein pair function using protein
This document discusses several approaches for predicting protein function, including methods that use amino acid sequences, protein structures, genomic sequences, phylogenetic data, microarray expression data, and protein interaction networks. It provides details on each type of data source and summarizes common computational techniques used for protein function prediction, such as homology-based approaches, clustering-based approaches, and classification-based approaches.
Qsar Studies on Gallic Acid Derivatives and Molecular Docking Studies of Bace...
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia.
The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely
accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes
under peptidase A1 family. In the present work, ligand based and structure based drug designing have been
reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive
model in order to predict biological activity and certain descriptors was reported to further enhance the
analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
Qsar studies on gallic acid derivatives and molecular docking studies of bace...
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia. The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely
accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes under peptidase A1 family. In the present work, ligand based and structure based drug designing have been reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive
model in order to predict biological activity and certain descriptors was reported to further enhance the
analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find
structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
Qsar Studies on Gallic Acid Derivatives and Molecular Docking Studies of Bace...
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia. The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes under peptidase A1 family. In the present work, ligand based and structure based drug designing have been
reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive model in order to predict biological activity and certain descriptors was reported to further enhance the analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find
structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
A method for detection and reduction of stress using EEG
This document proposes a method to detect stress using EEG signals and then introduce techniques to reduce stress. EEG signals would be used to measure brain activity and voltage fluctuations. A k-means clustering algorithm would classify subjects into stress level categories based on their EEG data. This detection method could then be used to develop products to reduce human stress over time by providing targeted recommendations. The goal is to help reduce the time and effort needed to determine effective stress management solutions.
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Depression Detection Using Various Machine Learning Classifiers
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Improve The Performance of K-means by using Genetic Algorithm for Classificat...
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Classification of Enzymes Using Machine Learning Based Approaches: A Review
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Data Analysis. Predictive Analysis. Activity Prediction that a subject perfor...
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Effect of 3D parameters on Antifungal Activities of Some Heterocyclic Compounds
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Generic approach for predicting unannotated protein pair function using protein
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Qsar Studies on Gallic Acid Derivatives and Molecular Docking Studies of Bace...
Qsar Studies on Gallic Acid Derivatives and Molecular Docking Studies of Bace...
Qsar studies on gallic acid derivatives and molecular docking studies of bace...
Qsar studies on gallic acid derivatives and molecular docking studies of bace...
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A method for detection and reduction of stress using EEG
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Recently uploaded
在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样
学校原件一模一样【微信：741003700 】《(salfor毕业证书)索尔福德大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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20240520 Planning a Circuit Simulator in JavaScript.pptx
Evaporation step counter work. I have done a physical experiment.
(Work in progress.)
Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
The binding of cosmological structures by massless topological defects
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Authoring a personal GPT for your research and practice: How we created the Q...
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Cytokines and their role in immune regulation.pptx
This presentation covers the content and information on "Cytokines " and their role in immune regulation .
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
3D Particle-In-Cell (PIC) algorithm,
Plasma expansion in the dipole magnetic field.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...Abdul Wali Khan University Mardan,kP,Pakistan
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skillsShallowest Oil Discovery of Turkiye.pptx
The Petroleum System of the Çukurova Field - the Shallowest Oil Discovery of Türkiye, Adana
Immersive Learning That Works: Research Grounding and Paths Forward
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
Micronuclei test.M.sc.zoology.fisheries.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Recently uploaded (20)
20240520 Planning a Circuit Simulator in JavaScript.pptx
20240520 Planning a Circuit Simulator in JavaScript.pptx
Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless Reproducibility
The binding of cosmological structures by massless topological defects
The binding of cosmological structures by massless topological defects
Authoring a personal GPT for your research and practice: How we created the Q...
Authoring a personal GPT for your research and practice: How we created the Q...
Basics of crystallography, crystal systems, classes and different forms
Basics of crystallography, crystal systems, classes and different forms
Cytokines and their role in immune regulation.pptx
Cytokines and their role in immune regulation.pptx
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
aziz sancar nobel prize winner: from mardin to nobel
aziz sancar nobel prize winner: from mardin to nobel
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
Immersive Learning That Works: Research Grounding and Paths Forward
Immersive Learning That Works: Research Grounding and Paths Forward
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...
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QSAR MODELLING ER-BETA
- 2. INTRODUCTION • Estrogen receptors (ERs) are basically the group of proteins found inside and on the cells & Estrogen (17β-estradiol) is the activator of Ers. • The estrogen belongs to the sex steroid hormones & plays crucial role in female reproductive system and also exerts important effects on non-reproductive targets such as bone, cardiovascular system and neural sites involved in cognition. ERβ is expressed in a variety of the normal and malignant tissues, modulation of immune responses. • Quantitative Structure-Activity Relationship(QSAR) is an approach which is designed to find relationship between the chemical structure (structural related properties) and the biological activity (target property) of the studied compounds. • So to identify & study various modulators for the ER-Beta and their function has been performed.
- 3. Objective To study the role & implication of estrogen receptor-β in various diseases. To identify different potent inhibitor’s which can modulate the activity of ER-β. To identify different type of QSAR modal development (2D & 3D). To develop a QSAR modal for the study of activity modulation as the function of structural property of ER-β.
- 4. Method Materials Structure of ER-β (obtained from PDB database). Modulators (obtained from zinc database). QSAR (done by multiple regression tools(XURU)). Methods Structure of modulators & ER-beta has been analyzed from Rcsb(Pdb database). Collection of literature & structure has been done. Development of QSAR modal for the study of modulator activity on ER-β structure has been performed.
- 5. Identifications of different ER- β modulators.
- 6. •List of ER- modulating ligand molecule and their corresponding activity. •Total 19 known modulators of ER- have been selected and downloaded from zinc database. Among which 80% of data were taken as training dataset and remaining as testing dataset. • Training dataset was used for the model development and testing dataset was used to test the model accuracy by calculating atious error like absolute error, RMS error and coefficient of correltion. The complete list of modulators is given in table.
- 7. Regression analysis by online tool http://www.xuru.org/rt/MLR.asp#Manually.
- 8. Result The following regression equation was generated after doing multi regression analysis. Y = -1.098918592 X1 +1.61848094 x 10-2 X2 + 1.104466751 x 10-1 X3 - 1.938974863 X4 – 1.514754566 x 10-1 X5 + 8.346848049 X6 + 5.296854305 x 10-2 X7 + 1.734491479 x 10-2 X8 – 5.390398764 x 10-1 X9 + 2.618696083. Coefficient from above equation was used to calculate the activity of test dataset in Microsoft excel 2007 and corresponding errors were measured between actual and calculated values. The table-3 on next slide shows the actual activity and calculated activity of testing dataset.
- 10. 0 0.5 1 1.5 2 2.5 3 3.5 1 2 3 4 Calculatedactivity Actual activity Correlation of actual vs. calculated activity Actual Calculated •A graph was plotted between actual activity and calculated activity as shown in figure 6 to better understand the correlation. •The higher accuracy of the model makes it suitable for the prediction of other modulator ligand molecules as well. This model can also be used to assess the effect of structural change in any descriptor mentioned above on their activity of respective compound. •After the structural analysis, it has been found that the xlogP value, number of Hydrogen bond donor, Net charge and number of rotatable bonds of any modulator highly affect the activity of that compound. •Any slight variation in these descriptors shows large change in the activity. Whereas the remaining descriptors having slightly less effect on the activity. Hence it can also be concluded that the activity of any compound is highly dependent upon its structural properties.