Properties and Drug-likeness of Compounds That Inhibit Ebola Virus Disease (EVD)

Aims: To present the molecular structures of compounds that has been shown to inhibit the proliferation of Ebola virus. To elucidate the molecular properties of these virus inhibiting compounds. Study Design: The molecular properties of virus inhibiting compounds are elucidated and compiled. Pattern recognition methods and statistical analysis are applied to determine optimal properties of this group of compounds. Place and Duration of Study: Chemistry Department, Durham Science Center, University of Nebraska, Omaha NE. between December 2015 and February 2016. Methodology: A total of 60 compounds were identified as inhibiting the virus Ebola. The molecular properties such as Log P, molecular weight, and 7 other descriptors were elucidated utilizing heuristic methods. Structures are compared by applying classification methods with statistical tests to determine trends, underlying relationships, and pattern recognition. Results: For 60 compounds identified the averages determined: for Log P (3.51), polar surface area (89.45 Angstroms2), molecular weight (432.6), molecular volume (393.96 Angstroms3), and number of rotatable bonds (7). Molecular weight showed a strong positive correlation to number of oxygen and nitrogen atoms, number of rotatable bonds, and molecular volume. K-means clustering indicated seven clusters divided according to highest similarity of members in the cluster. Ranges found: formula weights (157.1 to 822.94), Log P (-2.24 to 8.93), polar surface area (6.48 to 267.04 A2), and number of atoms (11 to 58). Multiple regression analysis produced an algorithm to predict similar compounds. Conclusion: The formula weights and Log P values of Ebola virus inhibitors show a broad range in numerical values. Consistency in properties was identified by statistical analysis with grouping for similarity by K-means pattern recognition. Multiple regression analysis enables prediction of similar compounds as drug candidates. Only 29 compounds showed zero violations of rule of 5, an indication of favorable drug-likeness. These compounds are highly varied in structures and properties

RESPIRATORY RESPONSES TO ACUTE INTERMITTENT HYPOXIA AND HYPERCAPNIA IN AWAKE RATS

This article deals with the recognition of early changes in the breathing pattern, in response to acute intermittent stimuli in awake rats. Two different types of stimuli were given: 9% hypoxia in N 2 and 10% hypercapnia in O 2 . Animals were exposed to 3 consecutive cycles consisting of 3-min stimulus period separated by 8-min normoxic recovery intervals. Features of the breathing pattern, such as respiratory frequency, tidal volume, minute ventilation, inspiration and expiration times, peak inspiratory and expiratory flows, were measured by whole body plethysmography. The data were analyzed with the use of pattern recognition methods. We conclude that the overall respiratory changes were rather slight. However, computerized analysis using a k-nearest neighbor decision rule (k-NN) allowed for a good recognition of the respiratory responses to the stimuli. The misclassification rate (E r ) varied from 5 to 10%. After feature selection, E r decreased below 1%. The k-NN classifier differentiated correctly also the type of intermittent stimulus. Our experimental results demonstrate usefulness of pattern recognition algorithms in studying respiratory effects in biological models

Quantum support vector data description for anomaly detection

Anomaly detection is a critical problem in data analysis and pattern recognition, finding applications in various domains. We introduce quantum support vector data description (QSVDD), an unsupervised learning algorithm designed for anomaly detection. QSVDD utilizes a shallow-depth quantum circuit to learn a minimum-volume hypersphere that tightly encloses normal data, tailored for the constraints of noisy intermediate-scale quantum (NISQ) computing. Simulation results on the MNIST and Fashion MNIST image datasets demonstrate that QSVDD outperforms both quantum autoencoder and deep learning-based approaches under similar training conditions. Notably, QSVDD offers the advantage of training an extremely small number of model parameters, which grows logarithmically with the number of input qubits. This enables efficient learning with a simple training landscape, presenting a compact quantum machine learning model with strong performance for anomaly detection.Comment: 14 pages, 5 figure

Design of Novel Anticancer Drugs Utilizing Busulfan for Optimizing Pharmacological Properties and Pattern Recognition Techniques for Elucidation of Clinical Efficacy

Chronic myelogenous leukemia (CML) is a disorder in which an excessive number of blood stem cells develop into the white blood cell group called granulocytes. The anticancer drug Busulfan is a cell cycle non-specific alkylating agent which is utilized to maintain white blood cell counts below 15000 cells/microliter. The side effects induced by busulfan are significant and affirms the intimation for new drug constructs. Fifteen analogous compounds were generated from the molecular structure of busulfan . These compounds retain the double methanesulfonate functional groups descriptive of this class of alkylating anticancer drugs. However, the carbon chain substituent separating the methanesulfonate is highly modified in order to allow significant changes in drug properties that may produce favorable characteristics that benefit clinical application. Important properties such as Log P, polar surface area, formula weight, molecular volume, Log BB, and violations of the Rule of 5 were determined to ascertain similarities and differences. All fifteen analog compounds retained zero violations of the Rule of 5, which suggests favorable properties for useful drug availability. Values of Log BB and BB remained the same throughout at -0.841 and 0.144, respectively. In addition, values of polar surface area and number of oxygens and nitrogens remained the same throughout at 86.752 A3 and 6, respectively. However, formula weight, number of atoms, number of rotatable bonds varied significantly with Log P varying across a broad range (-0.428 to 2.734). The variance in Log P within this group of methane sulfonate compounds allows new and potentially highly beneficial pharmacological properties for clinical application. Pattern recognition techniques such as cluster analysis, non-metric multidimensional scaling, discriminant analysis, and K-means cluster analysis discerned underlying relationships within this group of anticancer drugs and to the parent busulfan. This work shows that pattern recognition methods combined with molecular modeling can discover and elucidate novel drug designs for the clinical treatment of CML

Pyrimethamine Based Anti-protozoan Agents from Isostere and Heuristic Structure-similarity Search

Aims: To generate new medicaments for control and treatment of the parasitic protozoan Toxoplasma gondii. Study Design: Structure similarity search and isostere search was conducted over a broad range of structure categories. Correlation and highest similarity scores were implemented to select the best drug candidates. Place and Duration of Study: University of Nebraska, Department of Chemistry, Durham Science Center, 6001 Dodge Street, Omaha Nebraska 68182, from June 2016 to February 2017. Methodology: Utilizing pyrimethamine as the parent compound, a broad range of similar structures and isosteres were found by applying search methods. The compounds having the highest correlation and similarity scores were selected for the study of molecular properties. The molecular properties were determined and examined for underlying relationships by pattern recognition hierarchical cluster analysis and K-means cluster analysis. Results: Thirty compounds were identified to have a very high level of structure similarity or isosteric relationship to pyrimethamine. The molecular structures and molecular properties are presented for all compounds, inclusive of pyrimethamine. Hierarchical cluster analysis and K-means cluster analysis indicated compounds with highest underlying similarity to pyrimethamine. Box plots showed the over-all distribution of important pharmaceutical properties, such as molecular weight, Log P, polar surface area, number of rotatable bonds, molecular volume, and number of hydrogen bond donors. Structure components are compared to elucidate potential clinical activity. Multiple regression is applied on all compounds to generate a numerical relationship for prediction of similar compounds. Save for only one isostere, all compounds showed zero violations of the Rule of 5, indicating favorable drug-likeness and bioavailability. Conclusion: Thirty compounds highly analogous to pyrimethamine were identified following heuristic search course. The molecular properties were determined for all compounds and indicated genuine potential for treatment of toxoplasmosis. Correlation of structure and pattern recognition methods indicated 30 compounds of clinical potential and property analogy to pyrimethamine