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.

In recent years, artificial intelligence (AI) has emerged as a valuable resource for teaching and learning, and it has also shown promise as a tool to help solve problems. A tool that has gained attention in education is ChatGPT, which supports teaching and learning through AI. This research investigates the difficulties faced by ChatGPT in comprehending and responding to chemistry problems pertaining to the topic of Introduction to Material Science. By employing the theoretical framework proposed by Holme et al., encompassing categories such as transfer, depth, predict/explain, problem solving, and translate, we evaluate ChatGPT’s conceptual understanding difficulties. We presented ChatGPT with a set of thirty chemistry problems within the Introduction to Material Science domain and tasked it with generating solutions. Our findings indicated that ChatGPT encountered significant conceptual knowledge difficulties across various categories, with a notable emphasis on representations and depth, where difficulties in representations hindered effective knowledge transfer.

Cancer is a complex interaction among multiple signaling pathways involving a variety of target molecules. Cancer causes morbidity and mortality in millions of people worldwide, and due to its prevalence, the discovery of novel anticancer drugs is urgently required. Nature is considered an important source of the discovery of anticancer treatments, and many of the cytotoxic medicines in clinics today are derived from plants and other natural sources. Reactive oxygen species (ROS) induce a variety of human cancers, and antioxidants or scavengers are used to counteract them. The current study reports on the screening of extracts from 57 plants that are used in the galilee district as a food and/or for traditional medicine. Investigating the free radical scavenging capacity and these plants, and their cytotoxicity, may prove helpful to high‑throughput screening projects that use antioxidants and cytotoxic natural products. The current study assessed the correlation between free radical scavenging and cytotoxicity. Correlational analysis is important for increasing the efficiency of the screening process. In the present study, free radical scavenging was assessed using a DPPH assay, while cytotoxicity was measured using a XTT assay. A total of 9 extracts were indicated to exhibit EC50 values <250 µg/ml, and 4 others exhibited a high antioxidant content, with EC50 values, for free radical scavenging, of <0.5 µg/ml. An in‑depth analysis of the results revealed that the extracts of plants that exhibit an EC50 of free radical scavenging ≤10 µg/ml show a degree of enrichment toward increased cytotoxicity. It is recommended that future studies test the validity of the conclusions of the current study on other cancer cell‑lines, and isolate and identify the bioactive agents that are found in the most cytotoxic extracts of plants.

Streptococcus mutans (S. mutans) bacterium is the most well recognized pathogen involved in pathogenesis of dental caries. Its virulence arises from its ability to produce a biofilm and acidogenicity, causing tooth decay. Discovery of natural products capable to inhibit biofilm formation is of high importance for developing health care products. To the best of our knowledge, in all previous scientific reports, a colorimetric assay was applied to test the effect of sumac and methyl gallate (MG) on S. mutans adherence. Quantitative assessment of the developed biofilm should be further performed by applying an optical profilometry assay, and by testing the effect on both surface roughness and thickness parameters of the biofilm. To the best of our knowledge, this is the first study to report the effect of sumac extract and its constituent MG on biofilm formation using an optical profilometry assay. Testing antibacterial activity of the sumac extract and its fractions revealed that MG is the most bioactive component against S. mutans bacteria. It reduced S. mutans biofilm biomass on the polystyrene surface by 68‑93%, whereas 1 mg/ml MG was able to decrease the biofilm roughness and thickness on the glass surface by 99%. MG also prevented a decrease in pH level by 97%. These bioactivities of MG occurred in a dose‑dependent manner and were significant vs. untreated bacteria. The findings are important for the development of novel pharmaceuticals and formulations of natural products and extracts that possess anti‑biofilm activities with primary applications for oral health, and in a broader context, for the treatment of various bacterial infections.

In developing this study, we hypothesized that the integration of computerized techniques and modeling tools into traditional face-to-face instruction can produce a better hybrid model of teaching, capable of motivating students and improving their attitude toward science in general, and toward chemistry in particular. We tested how molecular visualizations and three-dimensional simulations affect students’ conceptual understanding of chemistry and their attitudes toward learning chemistry. During the academic year 2016-2017, we incorporated Avogadro software into teaching and tested how it affected students’ performance on chemistry exams in their courses. The difference in average scores between the two groups (8.2 points for the experimental group and 6.4 points for the control group) was significant. Student feedback following the initiative was positive and encouraging. Most students indicated that learning chemistry with Avogadro was extremely helpful, bringing the microscopic world of molecules closer to them, and they felt that they would like to see such software integrated into their chemistry studies from day one. Other parameters will be tested in continuation of this study, such as students' attitudes toward learning chemistry and their inquiry skills.