COMPUTATIONAL MODELING OF ANTIMALARIAL 10-SUBSTITUTED DEOXOARTEMISININS

2012 ◽  
Vol 11 (02) ◽  
pp. 241-263 ◽  
Author(s):  
MARIA DA GLÓRIA G. CRISTINO ◽  
CARLA CAROLINA F. DE MENESES ◽  
MALÚCIA MARQUES SOEIRO ◽  
JOÃO ELIAS V. FERREIRA ◽  
ANTONIO FLORÊNCIO DE FIGUEIREDO ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Electrostatic Potential ◽  
Molecular Electrostatic Potential ◽  
Nearest Neighbor ◽  
Antimalarial Activity ◽  
Principal Component ◽  
Structural Features ◽  
Orbital Energy ◽  
Stepwise Discriminant Analysis ◽  
K Nearest Neighbor

Nineteen 10-substitued deoxoartemisinin derivatives and artemisinin with activity against D-6 strains of malarial falciparum designated as Sierra Leone are studied. We use molecular electrostatic potential maps in an attempt to identify key structural features of the artemisinins that are necessary for their activities and molecular docking to investigate the interaction with the molecular receptor (heme). Chemometric modeling: Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), K-Nearest Neighbor (KNN), Soft Independent Modeling of Class Analogy (SIMCA) and Stepwise Discriminant Analysis (SDA) are employed to reduce dimensionality and investigate which subset of descriptors are responsible for the classification between more active (MA) and less active (LA) artemisinins. The PCA, HCA, KNN, SIMCA and SDA studies showed that the descriptors LUMO (Lowest Unoccupied Molecular Orbital) energy, DFeO1 (Distance between the O 1 atom from ligand and iron atom from heme), X1A (Average Connectivity Index Chi-1) and Mor15u (Molecular Representation of Structure Based on Electron Diffraction) code of signal 15, unweighted, are responsible for separating the artemisinins according to their degree of antimalarial activity. The prediction study was done with a new set of eight artemisinins by using the chemometric methods and five of them were predicted as active against D-6 strains of falciparum malaria. In order to verify if the key structural features that are necessary for their antimalarial activities were investigated for the interaction with the heme, we also carried out calculations of the molecular electrostatic potential (MEP) and molecular docking. MEP maps and molecular docking were analyzed for more active compounds of the prediction set.

scholarly journals Molecular electrostatic potential and pattern recognition models to design potentially active pentamidine derivatives against Trypanosoma brucei rhodesiense

2021 ◽  
Vol 10 (12) ◽  
pp. e261101220207
Author(s):  
Luã Felipe Souza de Oliveira ◽  
Hérica Coelho Cordeiro ◽  
Helieverton Geraldo de Brito ◽  
Ana Cecília Barbosa Pinheiro ◽  
Marcos Antonio Barros dos Santos ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Electrostatic Potential ◽  
Molecular Electrostatic Potential ◽  
Principal Component ◽  
Structural Features ◽  
Data Matrix ◽  
Orbital Energy ◽  
Molecular Volume ◽  
Stepwise Discriminant Analysis ◽  
K Nearest Neighbor

Molecular electrostatic potential (MEP) and pattern recognition (PR) were used to draw potentially active pentamidine derivatives against Trypanosome brucei rhodesiense (T. b. rhodesiense). PR models: Principal Component Analysis, PCA model; Hierarchical Cluster Analysis, HCA model; K-Nearest Neighbor, KNN model; Soft Independent Modeling of Class Analogy, SIMCA model; and Stepwise Discriminant Analysis, SDA model, were built by reducing the dimensionality of a data matrix to twenty-eight pentamidine derivatives and allowed the compounds to be classified into two classes: more active and less active, according to their degrees of activity against T. b. rhodesiense. The study outlined that the properties HOMO (highest occupied molecular orbital) energy, VOL (molecular volume), and ASA_P (water accessible surface area of all polar (½qi½³0. 2) atoms) are the most relevant for the construction of the models. The key structural features required for biological activity investigated through MEP were used as guidelines in the design of thirteen new compounds, which were evaluated by PR models as more active or less active against T. b. rhodesiense. The application of PR models indicated nine promising compounds (29, 30, 31, 32, 33, 36, 37, 39, and 40) for synthesis and biological assays.

Two-parameter KNN algorithm and its application in recognition of brand rice

2021 ◽  
pp. 1-7
Author(s):  
Zhu Siyu ◽  
He Chongnan ◽  
Song Mingjuan ◽  
Li Linna
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Pattern Class ◽  
Fisher Discriminant ◽  
Two Parameter

In response to the frequent counterfeiting of Wuchang rice in the market, an effective method to identify brand rice is proposed. Taking the near-infrared spectroscopy data of a total of 373 grains of rice from the four origins (Wuchang, Shangzhi, Yanshou, and Fangzheng) as the observations, kernel principal component analysis(KPCA) was employed to reduce the dimensionality, and Fisher discriminant analysis(FDA) and k-nearest neighbor algorithm (KNN) were used to identify brand rice respectively. The effects of the two recognition methods are very good, and that of KNN is relatively better. Howerver the shortcomings of KNN are obvious. For instance, it has only one test dimension and its test of samples is not delicate enough. In order to further improve the recognition accuracy, fuzzy k-nearest neighbor set is defined and fuzzy probability theory is employed to get a new recognition method –Two-Parameter KNN discrimination method. Compared with KNN algorithm, this method increases the examination dimension. It not only examines the proportion of the number of samples in each pattern class in the k-nearest neighbor set, but also examines the degree of similarity between the center of each pattern class and the sample to be identified. Therefore, the recognition process is more delicate and the recognition accuracy is higher. In the identification of brand rice, the discriminant accuracy of Two-Parameter KNN algorithm is significantly higher than that of FDA and that of KNN algorithm.

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

scholarly journals Analisis Metode Pengenalan Wajah Two Dimensial Principal Component Analysis (2DPCA) dan Kernel Fisher Discriminant Analysis Menggunakan Klasifikasi KNN (K- Nearest Neighbor)

2020 ◽  
Vol 2 (2) ◽  
pp. 29-38
Author(s):  
Abdur Rohman Harits Martawireja ◽  
Hilman Mujahid Purnama ◽  
Atika Nur Rahmawati
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis

Pengenalan wajah manusia (face recognition) merupakan salah satu bidang penelitian yang penting dan belakangan ini banyak aplikasi yang menerapkannya, baik di bidang komersil ataupun di bidang penegakan hukum. Pengenalan wajah merupakan sebuah sistem yang berfungsikan untuk mengidentifikasi berdasarkan ciri-ciri dari wajah seseorang berbasis biometrik yang memiliki keakuratan tinggi. Pengenalan wajah dapat diterapkan pada sistem keamanan. Banyak metode yang dapat digunakan dalam aplikasi pengenalan wajah untuk keamanan sistem, namun pada artikel ini akan membahas tentang dua metode yaitu Two Dimensial Principal Component Analysis dan Kernel Fisher Discriminant Analysis dengan metode klasifikasi menggunakan K-Nearest Neigbor. Kedua metode ini diuji menggunakan metode cross validation. Hasil dari penelitian terdahulu terbukti bahwa sistem pengenalan wajah metode Two Dimensial Principal Component Analysis dengan 5-folds cross validation menghasilkan akurasi sebesar 88,73%, sedangkan dengan 2-folds validation akurasi yang dihasilkan sebesar 89,25%. Dan pengujian metode Kernel Fisher Discriminant dengan 2-folds cross validation menghasilkan akurasi rata rata sebesar 83,10%.

scholarly journals Investigation of Some Antiviral N-Heterocycles as COVID 19 Drug: Molecular Docking and DFT Calculations

2020 ◽  
Vol 21 (11) ◽  
pp. 3922 ◽  
Author(s):  
Mohamed Hagar ◽  
Hoda A. Ahmed ◽  
Ghadah Aljohani ◽  
Omaima A. Alhaddad
Keyword(s):  
Dipole Moment ◽  
Molecular Docking ◽  
Dft Calculations ◽  
Binding Affinity ◽  
Electrostatic Potential ◽  
Density Functional ◽  
Molecular Electrostatic Potential ◽  
Hydrophobic Interactions ◽  
Chemical Reactivity ◽  
Main Protease

The novel coronavirus, COVID-19, caused by SARS-CoV-2, is a global health pandemic that started in December 2019. The effective drug target among coronaviruses is the main protease Mpro, because of its essential role in processing the polyproteins that are translated from the viral RNA. In this study, the bioactivity of some selected heterocyclic drugs named Favipiravir (1), Amodiaquine (2), 2′-Fluoro-2′-deoxycytidine (3), and Ribavirin (4) was evaluated as inhibitors and nucleotide analogues for COVID-19 using computational modeling strategies. The density functional theory (DFT) calculations were performed to estimate the thermal parameters, dipole moment, polarizability, and molecular electrostatic potential of the present drugs; additionally, Mulliken atomic charges of the drugs as well as the chemical reactivity descriptors were investigated. The nominated drugs were docked on SARS-CoV-2 main protease (PDB: 6LU7) to evaluate the binding affinity of these drugs. Besides, the computations data of DFT the docking simulation studies was predicted that the Amodiaquine (2) has the least binding energy (−7.77 Kcal/mol) and might serve as a good inhibitor to SARS-CoV-2 comparable with the approved medicines, hydroxychloroquine, and remdesivir which have binding affinity −6.06 and −4.96 Kcal/mol, respectively. The high binding affinity of 2 was attributed to the presence of three hydrogen bonds along with different hydrophobic interactions between the drug and the critical amino acids residues of the receptor. Finally, the estimated molecular electrostatic potential results by DFT were used to illustrate the molecular docking findings. The DFT calculations showed that drug 2 has the highest of lying HOMO, electrophilicity index, basicity, and dipole moment. All these parameters could share with different extent to significantly affect the binding affinity of these drugs with the active protein sites.

scholarly journals A Study on the Psychological Analysis System Using Machine Learning

2018 ◽  
Vol 7 (3.33) ◽  
pp. 128
Author(s):  
Ki Young Lee ◽  
Kyu Ho Kim ◽  
Jeong Jin Kang ◽  
Sung Jai Choi ◽  
Yong Soon Im ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Smart Phone ◽  
Principal Component ◽  
Digital Data ◽  
Expression Recognition ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Psychological Analysis ◽  
Analysis System

Real-time facial expression recognition and analysis technology is recently drawing attention in areas of computer vision, computer graphics, and HCI. Recognition of user’s emotion on the basis of video and voice is drawing particular interest. The technology may help managers of households or hospitals. In the present study, video and voice were converted into digital data through MATLAB by using PCA(Principal Component Analysis), LDA(Linear Discriminant Analysis), KNN(K Nearest Neighbor) algorithms to analyze emotions through machine learning. The manager of the psychological analysis counseling system may understand a user’s emotion in an smart phone environment. This system of the present study may help the manager to have a smooth conversation or develop a smooth relationship with a user on the basis of the provided psychological analysis results. 

scholarly journals Discrimination of Chinese Liquors Based on Electronic Nose and Fuzzy Discriminant Principal Component Analysis

Foods ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 38 ◽  
Author(s):  
Xiaohong Wu ◽  
Jin Zhu ◽  
Bin Wu ◽  
Chao Zhao ◽  
Jun Sun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Electronic Nose ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Knn Classifier ◽  
Extraction Algorithm ◽  
Leave One Out

The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nose system can quickly detect different types of Chinese liquors according to their aromas. In this study, an e-nose system was designed to identify six types of Chinese liquors, and a novel feature extraction algorithm, called fuzzy discriminant principal component analysis (FDPCA), was developed for feature extraction from e-nose signals by combining discriminant principal component analysis (DPCA) and fuzzy set theory. In addition, principal component analysis (PCA), DPCA, K-nearest neighbor (KNN) classifier, leave-one-out (LOO) strategy and k-fold cross-validation (k = 5, 10, 20, 25) were employed in the e-nose system. The maximum classification accuracy of feature extraction for Chinese liquors was 98.378% using FDPCA, showing this algorithm to be extremely effective. The experimental results indicate that an e-nose system coupled with FDPCA is a feasible method for classifying Chinese liquors.

A Robust Physical Exercise Recognition System Using Machine Learning Approach

2020 ◽  
Vol 1 (1) ◽  
pp. 17-21
Author(s):  
Steve Oscar ◽  
◽  
Mohammed Nazim Uddin ◽  
Keyword(s):  
Physical Exercise ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Recognition System ◽  
Accelerometer Data ◽  
K Nearest Neighbor ◽  
Machine Learning Approach ◽  
Principal Component Analysis Algorithm ◽  
K Nearest Neighbor Algorithm

Modern life is becoming more linked to our devices, and work is being done in a more regulated way. As life became more complicated, it is becoming challenging to keep track of human health and fitness, leading to unexpected illnesses and diseases. Moreover, a lack of activity monitoring and corresponding reminders is preventing the adoption of a healthier lifestyle. This research provides a practical approach for identifying Human Activity by using accelerometer data obtained from wearable devices. The model automatically finds patterns among 33 different physical exercises such as running, rowing, cycling, jogging, etc. and correctly identifies them. The principal component analysis algorithm was used on the statistical features to make the system more robust. Classification of the physical exercise was performed on the reduced features using WEKA. The overall accuracy of 85.51% was obtained using the 10-Fold Cross-Validation method and K nearest Neighbor Algorithm while 84% accuracy for Random Forest. The accuracy obtained was better than previous models and could improve recognition systems in monitoring user activity more precisely.

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