Cancer is considered one of the primary diseases that cause morbidity and mortality in millions of people worldwide and due to its prevalence, there is undoubtedly an unmet need to discover novel anticancer drugs. However, the traditional process of drug discovery and development is lengthy and expensive, so the application of in silico techniques and optimization algorithms in drug discovery projects can provide a solution, saving time and costs. A set of 617 approved anticancer drugs, constituting the active domain, and a set of 2,892 natural products, constituting the inactive domain, were employed to build predictive models and to index natural products for their anticancer bioactivity. Using the iterative stochastic elimination optimization technique, we obtained a highly discriminative and robust model, with an area under the curve of 0.95. Twelve natural products that scored highly as potential anticancer drug candidates are disclosed. Searching the scientific literature revealed that few of those molecules (Neoechinulin, Colchicine, and Piperolactam) have already been experimentally screened for their anticancer activity and found active. The other phytochemicals await evaluation for their anticancerous activity in wet lab.
The proposed anti-inflammatory model can be utilized for the virtual screening of large chemical databases and for indexing natural products for potential anti-inflammatory activity.
New generations are using and playing with mobile and computer applications extensively. These applications are the outcomes of programming work that involves skills, such as computational and algorithmic thinking. Learning programming is not easy for students children. In recent years, academic institutions like the Massachusetts Institute of Technology (MIT) and hi-tech companies, such as Google and Khan Academy, have introduced online environments to facilitate the teaching and learning of programming. Most of these programming environments are web-based, and interactive and are supported with visual multimedia features. Therefore, they have become easy to use, very attractive and helpful for teaching children how to program and to develop their algorithmic and computational thinking skills. The proposed presentation will describe research that examined the teaching of a course to primary school children based on three on-line interactive environments: "Plastelina" for logic games, "Code with Anna and Elsa" via the Hour of Code project block-oriented programming environment, for block programming and "Turtle Academy" for textual programming in the Logo language. The current research included the development, implementation and evaluation of the course at an elementary school. In addition, it was aimed at investigating the pupils' attitudes toward the learning of computer programming, both before and after participation in the course. The results revealed that the pupils' attitudes towards programming remained positive also also after the participation in the course. It was also found that programming improved children's problemsolving skills.
Abstract:The majority of the currently used cosmetics and drugs are natural products-based compounds or their derivatives. This could add weight to the argument that natural based products are inherently better tolerated in the body than synthetic chemicals and have higher chance to be approved as new drugs. The present study was undertaken to analyze a natural product database compared to synthetic chemicals and to search for discriminative physicochemical properties that may probably help in differentiating between natural and synthetic compounds. We have formulated rules to assess the natural likeness of chemicals and thereby discriminate between natural-based and synthetic chemicals. A Mathews Correlation Coefficient of 0.5 was obtained; nearly 81% of natural-based products and 68% of synthetic chemicals were precisely classified using this filter. The property criteria for drug-likeness and lead-likeness are more pronounced in natural products rather than synthetic ones. The fraction of synthetic chemicals which are natural-like could have higher chance to be successful drug.
In this paper we propose an approach to organizing Adaptive Problem-Based Learning (PBL) leading to the development of Higher-Order Thinking (HOT) skills and collaborative skills in students. Adaptability of PBL is expressed by changes in fixed instructor assessments caused by the dynamics of developing HOT skills needed for problem solving, flexible choice of control tests and problems for students, and adaptive formation of HOT skills within heterogeneous collaborative groups. It induces the students to develop HOT skills and collaborative skills through a combination of personalized and collaborative PBL. Adaptability of PBL is realized by taking into account values of the proposed coefficient of HOT skills development. The two-stage process of adaptive PBL allows guided development of HOT skills in students during study of a subject. Attention in the first stage is devoted to development of analytical HOT skills in students through personalized PBL. The main attention on the second stage is devoted to the development of creative HOT skills and collaborative skills in students through collaborative PBL. The proposed approach provides effective development of HOT skills and collaborative skills in students owing to: availability of a two-stage adaptive PBL process, complex and adaptive assessment of HOT skills, dynamic choice of control tests and problems for students, adaptive formation of HOT skills heterogeneous collaborative groups, and management of HOT skills development of students.
The human histamine H4 receptor (hH4R), a member of the G-protein coupled receptors (GPCR) family, is an increasingly attractive drug target. It plays a key role in many cell pathways and many hH4R ligands are studied for the treatment of several inflammatory, allergic and autoimmune disorders, as well as for analgesic activity. Due to the challenging difficulties in the experimental elucidation of hH4R structure, virtual screening campaigns are normally run on homology based models. However, a wealth of information about the chemical properties of GPCR ligands has also accumulated over the last few years and an appropriate combination of these ligand-based knowledge with structure-based molecular modeling studies emerges as a promising strategy for computer-assisted drug design. Here, two chemoinformatics techniques, the Intelligent Learning Engine (ILE) and Iterative Stochastic Elimination (ISE) approach, were used to index chemicals for their hH4R bioactivity. An application of the prediction model on external test set composed of more than 160 hH4R antagonists picked from the chEMBL database gave enrichment factor of 16.4. A virtual high throughput screening on ZINC database was carried out, picking ∼4000 chemicals highly indexed as H4R antagonists' candidates. Next, a series of 3D models of hH4R were generated by molecular modeling and molecular dynamics simulations performed in fully atomistic lipid membranes. The efficacy of the hH4R 3D models in discrimination between actives and non-actives were checked and the 3D model with the best performance was chosen for further docking studies performed on the focused library. The output of these docking studies was a consensus library of 11 highly active scored drug candidates. Our findings suggest that a sequential combination of ligand-based chemoinformatics approaches with structure-based ones has the potential to improve the success rate in discovering new biologically active GPCR drugs and increase the enrichment factors in a synergistic manner.
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