Improvement of SARS-COV-2 screening using pooled sampling testing in limited RT-qPCR resources.

Background: COVID-19 is a worldwide pandemic representing the most challenging global health crisis currently. Screening tests availability are a problematic task due to resource-limited abilities of some countries using RT-qPCR technique for SARS-COV-2 detection.

Objective: To cope with these health emergencies, in particular with this COVID-19 pandemic, states with low molecular diagnostic resources must optimize their capacity in molecular tests.

A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure-activity relationship modeling.

Over the past decade, rapid development in biological and chemical technologies such as high-throughput screening, parallel synthesis, has been significantly increased the amount of data, which requires the creation and the integration of new analytical methods, especially deep learning models. Recently, there is an increasing interest in deep learning utilization in computer-aided drug discovery due to its exceptional successful application in many fields. The present work proposed a natural language processing approach, based on embedding deep neural networks.

Association of TERT, OGG1, and CHRNA5 Polymorphisms and the Predisposition to Lung Cancer in Eastern Algeria.

Lung cancer remains the most common cancer in the world. The genetic polymorphisms (rs2853669 in TERT, rs1052133 in OGG1, and rs16969968 in CHRNA5 genes) were shown to be strongly associated with the risk of lung cancer. Our study's aim is to elucidate whether these polymorphisms predispose Eastern Algerian population to non-small-cell lung cancer (NSCLC).

Optimization of the chromone scaffold through QSAR and docking studies: Identification of potent inhibitors of ABCG2.

The membrane transporter BCRP/ABCG2 has emerged as a privileged biological target for the development of small compounds capable of abolishing multidrug resistance. In this context, the chromone skeleton was found as an excellent scaffold for the design of ABCG2 inhibitors. With the aims of optimizing and developing more potent modulators of the transporter, we herewith propose a multidisciplinary medicinal chemistry approach performed on this promising scaffold.