Deriving general structure-activity/selectivity relationship patterns for different subfamilies of cyclin-dependent kinase inhibitors using machine learning methods.

Cyclin-dependent kinases (CDKs) play essential roles in regulating the cell cycle and are among the most critical targets for cancer therapy and drug discovery. The primary objective of this research is to derive general structure-activity relationship (SAR) patterns for modeling the selectivity and activity levels of CDK inhibitors using machine learning methods. To accomplish this, 8592 small molecules with different binding affinities to CDK1, CDK2, CDK4, CDK5, and CDK9 were collected from Binding DB, and a diverse set of descriptors was calculated for each molecule.

Combining ion mobility spectrometry and chemometrics for detecting synthetic colorants in black tea: A reliable and fast method.

Black tea () is a widely consumed beverage and is subjected to adulteration. In this study, the combination of ion mobility spectrometry and machine learning techniques was employed to detect synthetic colorants in black tea. To accomplish our objective, six synthetic colorants (carmine, carmoisine, indigo carmine, brilliant blue, sunset yellow, and tartrazine) were added to pure tea at different concentrations.

Detection of fraud in lime juice using pattern recognition techniques and FT-IR spectroscopy.

The lime juice is one of the products that has always fallen victim to fraud by manufacturers for reducing the cost of products. The aim of this research was to determine fraud in distributed lime juice products from different factories in Iran. In this study, 101 samples were collected from markets and also prepared manually and finally derived into 5 classes as follows: two natural classes (, ), including 17 samples, and three reconstructed classes, including 84 samples (made from Spanish concentrate, Chinese concentrate, and concentrate containing adulteration compounds).

Probing heat and oxidation induced conformational changes of molecular chaperone artemin by excitation-emission fluorescence spectroscopy.

Artemin is a potent molecular chaperone, which protects Artemia embryos undergoing encystment against extreme environmental stresses. In the present work, we have examined the structural changes of artemin from A. urmiana upon exposure to oxidant and heat, by using CD measurements as well as excitation-emission fluorescence spectroscopy as a powerful tool for monitoring the conformational transitions and molecular interactions in proteins.

Synthesis, crystal structure, insecticidal activities, molecular docking and QSAR studies of some new phospho guanidines and phospho pyrazines as cholinesterase inhibitors.

Novel phospho guanidine and phospho pyrazine derivatives were synthesized and characterized by P, C, HNMR and IR spectroscopy to obtain novel and human-safe insecticides. Compound 35 [(CHNNH)P(O)(CH)] was investigated by X-ray crystallography. The inhibitory effects of synthesized compounds were evaluated on human and insect acetylcholinesterase (AChE) using in vitro Ellman method.

Synthesis, crystal structure and biological evaluation of new phosphoramide derivatives as urease inhibitors using docking, QSAR and kinetic studies.

In an attempt to achieve a new class of phosphoramide inhibitors with high potency and resistance to the hydrolysis process against urease enzyme, we synthesized a series of bisphosphoramide derivatives (01-43) and characterized them by various spectroscopic techniques. The crystal structures of compounds 22 and 26 were investigated using X-ray crystallography. The inhibitory activities of the compounds were evaluated against the jack bean urease and were compared to monophosphoramide derivatives and other known standard inhibitors.

Metabolic Fingerprinting of Seminal Plasma from Non-obstructive Azoospermia Patients: Positive Versus Negative Sperm Retrieval.

Background: Non-obstructive azoospermia (NOA) occurs in approximately 10% of infertile men. Retrieval of the spermatozoa from the testicle of NOA patients is an invasive approach. Seminal plasma is an excellent source for exploring to find the biomarkers for presence of spermatozoa in testicular tissue.

Identification of molecular features necessary for selective inhibition of B cell lymphoma proteins using machine learning techniques.

Selective inhibition of Bcl-2 and Bcl-x proteins due to their dual inhibition toxicity plays an important role in treatment of cancer and chemotherapy effectiveness; therefore, in the last decade, discovery of selective inhibitors for Bcl-2 and Bcl-x proteins has become a significant and important research topic. The present contribution paves the way for characterization of molecular features which induce selectivity for inhibition of Bcl-2 and Bcl-x. In this line, a total of 1534 molecules related to inhibition of Bcl-2 and Bcl-x proteins were collected from Binding Database.

Motor Oil Classification Using Color Histograms and Pattern Recognition Techniques.

Motor oil classification is important for quality control and the identification of oil adulteration. In this work, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure.

The molecular cues for the biological effects of ionizing radiation dose and post-irradiation time on human breast cancer SKBR3 cell line: A Raman spectroscopy study.

Radiotherapy is one of the main modalities of cancer treatment. The utility of Raman spectroscopy (RS) for detecting the distinct radiobiological responses in human cancer cells is currently under investigation. RS holds great promises to provide good opportunities for personalizing radiotherapy treatments.

Untargeted metabolomic profiling of seminal plasma in nonobstructive azoospermia men: A noninvasive detection of spermatogenesis.

Male factor infertility is involved in almost half of all infertile couples. Lack of the ejaculated sperm owing to testicular malfunction has been reported in 6-10% of infertile men, a condition named nonobstructive azoospermia (NOA). In this study, we investigated untargeted metabolomic profiling of the seminal plasma in NOA men using gas chromatography-mass spectrometry and advance chemometrics.

Estimating complicated baselines in analytical signals using the iterative training of Bayesian regularized artificial neural networks.

The present work deals with the development of a new baseline correction method based on the comparative learning capabilities of artificial neural networks. The developed method uses the Bayes probability theorem for prevention of the occurrence of the over-fitting and finding a generalized baseline. The developed method has been applied on simulated and real metabolomic gas-chromatography (GC) and Raman data sets.

Concentration profiling of minerals in iliac crest bone tissue of opium addicted humans using inductively coupled plasma and discriminant analysis techniques.

Opium addiction is one of the main health problems in developing countries and induces serious defects on the human body. In this work, the concentrations of 32 minerals including alkaline, heavy and toxic metals have been determined in the iliac crest bone tissue of 22 opium addicted individuals using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The bone tissues of 30 humans with no physiological and metabolomic diseases were used as the control group.

Assessment of the orthogonality in two-dimensional separation systems using criteria defined by the maximal information coefficient.

A novel method based on the maximal information coefficient (MIC) is developed to assess the orthogonality of comprehensive two-dimensional separation systems. The proposed method is based on a modification of Marriott's method, which was previously reported in 2013. Marriott's method relies on the calculation of two separate parameters.

CS-MINER: A Tool for Association Mining in Binding-Database.

This paper introduces the algorithms, implementation strategies, features, and applications of CS-MINER, a tool for visualization and analysis of drug-like chemical space. The CS-MINER is the abstract abbreviation for Chemical Space Miner and correlates the medicinal target space and chemical space, in a systematic way. The database in this software consists of a large collection of drug-like molecules.

1H NMR based metabolic profiling in Crohn's disease by random forest methodology.

The present study was designed to search for metabolic biomarkers and their correlation with serum zinc in Crohn's disease patients. Crohn's disease (CD) is a form of inflammatory bowel disease that may affect any part of the gastrointestinal tract and can be difficult to diagnose using the clinical tests. Thus, introduction of a novel diagnostic method would be a major step towards CD treatment.

Integrated One-Against-One Classifiers as Tools for Virtual Screening of Compound Databases: A Case Study with CNS Inhibitors.

A total of 21 833 inhibitors of the central nervous system (CNS) were collected from Binding-database and analyzed using discriminant analysis (DA) techniques. A combination of genetic algorithm and quadratic discriminant analysis (GA-QDA) was proposed as a tool for the classification of molecules based on their therapeutic targets and activities. The results indicated that the one-against-one (OAO) QDA classifiers correctly separate the molecules based on their therapeutic targets and are comparable with support vector machines.

Chromatographic fingerprint analysis of secondary metabolites in citrus fruits peels using gas chromatography-mass spectrometry combined with advanced chemometric methods.

Multivariate curve resolution (MCR) and multivariate clustering methods along with other chemometric methods are proposed to improve the analysis of gas chromatography-mass spectrometry (GC-MS) fingerprints of secondary metabolites in citrus fruits peels. In this way, chromatographic problems such as baseline/background contribution, low S/N peaks, asymmetric peaks, retention time shifts, and co-elution (overlapped and embedded peaks) occurred during GC-MS analysis of chromatographic fingerprints are solved using the proposed strategy. In this study, first, informative GC-MS fingerprints of citrus secondary metabolites are generated and then, whole data sets are segmented to some chromatographic regions.

Navigating Drug-Like Chemical Space of Anticancer Molecules Using Genetic Algorithms and Counterpropagation Artificial Neural Networks.

A total of 6289 drug-like anticancer molecules were collected from Binding database and were analyzed by using the classification techniques. The collected molecules were encoded to a diverse set of descriptors, spanning different physical and chemical properties of the molecules. A combination of genetic algorithms and counterpropagation artificial neural networks was used for navigating the generated drug-like chemical space and selecting the most relevant molecular descriptors.