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The identification of lead compounds usually includes a step of chemical diversity generation. Its rationale may be supported by both qualitative (SAR) and quantitative (QSAR) approaches, offering models of the putative ligand-receptor interactions. In both scenarios, our understanding of which interactions functional groups can perform is mostly based on their chemical nature (such as electronegativity, volume, melting point, lipophilicity etc.) instead of their dynamics in aqueous, biological solutions (solvent accessibility, lifetime of hydrogen bonds, solvent structure etc.). As a consequence, it is challenging to predict from 2D structures which functional groups will be able to perform interactions with the target receptor, at which intensity and relative abundance in the biological environment, all of which will contribute to ligand potency and intrinsic activity. With this in mind, the aim of this work is to assess properties of aromatic rings, commonly used for drug design, in aqueous solution through molecular dynamics simulations in order to characterize their chemical features and infer their impact in complexation dynamics. For this, common aromatic and heteroaromatic rings were selected and received new atomic charge set based on the direction and module of the dipole moment from MP2/6-31G* calculations, while other topological terms were taken from GROMOS53A6 force field. Afterwards, liquid physicochemical properties were simulated for a calibration set composed by nearly 40 molecules and compared to their respective experimental data, in order to validate each topology. Based on the reliance of the employed strategy, we expanded the dataset to more than 100 aromatic rings. Properties in aqueous solution such as solvent accessible surface area, H-bonds availability, H-bonds residence time, and water structure around heteroatoms were calculated for each ring, creating a database of potential interactions, shedding light on features of drugs in biological solutions, on the structural basis for bioisosterism and on the enthalpic/entropic costs for ligand-receptor complexation dynamics.

Background COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. Purpose This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. Methods Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT-PCR results through Naïve-Bayes algorithms. Different scarcity scenarios were simulated, including symptomatic essential workforce management and absence of diagnostic tests. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. Results Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity, yielding a 100% sensitivity and 22.6% specificity with a prior of 0.9999; 76.7% for both sensitivity and specificity with a prior of 0.2933; and 0% sensitivity and 100% specificity with a prior of 0.001. Regarding background scarcity context, resources allocation can be significantly improved when model-based patient selection is observed, compared to random choice. Conclusions Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency.

When Clostridium formicoaceticum was grown on fumarate or L-malate crude cell extracts contained a high fumarate reductase activity. Using reduced methyl viologen as electron donor the specific activity amounted to 2-3.5 U per mg of protein. Reduced benzyl viologen, FMNH2 and NADH could also serve as electron donors but the specific activities were much lower. The NADH-dependent activity was strictly membrane-bound and rather labile. Its specific activity did not exceed 0.08 U per mg of particle protein. Fumarate reductase activity was also found in cells of C. formicoaceticum grown on fructose, gluconate, glutamate and some other substrates. The methyl viologen-dependent fumarate reductase activity could almost completely be measured with intact cells whereas only about 25% of the cytoplasmic acetate kinase activity was detected with cell suspensions. The preparation of spheroplasts from cells of C. formicoaceticum in 20 mM HEPES-KOH buffer containing 0.6 M sucrose and 1 mM dithioerythritol resulted in the specific release of 88% of the fumarate reductase activity into the spheroplast medium. Only small amounts of the cytoplasmic proteins malic enzyme and acetate kinase were released during this procedure. There results indicate a peripheral location of the fumarate reductase of C. formicoaceticum on the membrane.