Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow.

With the increased availability of chemical data in public databases, innovative techniques and algorithms have emerged for the analysis, exploration, visualization, and extraction of information from these data. One such technique is chemical grouping, where chemicals with common characteristics are categorized into distinct groups based on physicochemical properties, use, biological activity, or a combination. However, existing tools for chemical grouping often require specialized programming skills or the use of commercial software packages.

Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery.

Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to understand the structure-activity relationships that exist between biomolecules and druggable targets. More recently, these methods have also seen application for phenotypic screening data for neglected diseases such as tuberculosis and malaria.

New insight into the synthesis, morphological architectures and biomedical applications of elemental selenium nanostructures.

Selenium nanoparticles have been shown to be versatile in their applications by being used in catalysis, solar cells, electronic devices and especially in medical applications such as antiviral, anticancer, antitumor and antibacterial agents in different concentrations. They have also shown enhanced drug and gene delivery by conjugating with drug molecules and showing high synergistic effects. After realising their usefulness in the biomedical field, we have made a sincere effort to correlate and consolidate the recent developments made in their synthesis methods, structural features and biological applications.