MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules.

Density Functional Theory (DFT) is extensively used in theoretical and computational chemistry to study molecular and crystal properties across diverse fields, including quantum chemistry, materials physics, catalysis, biochemistry, and surface science. Despite advances in DFT hardware and software for optimized geometries, achieving consensus in molecular structure comparisons with experimental counterparts remains a challenge. This difficulty is exacerbated by the lack of automated bond length comparison tools, resulting in labor-intensive and error-prone manual processes. To address these challenges, we propose MolGC, a Molecular Geometry Comparator algorithm that automates the comparison of optimized geometries from different theoretical levels. MolGC calculates the mean absolute error (MAE) of bond lengths by integrating data from various DFT software. It provides interactive and customizable visualization of geometries, enabling users to explore different views for enhanced analysis. In addition, it saves MAE computations for further analysis and offers a comprehensive statistical summary of the results. MolGC effectively addresses complex graph labeling challenges, ensuring accurate identification and categorization of bonds in diverse chemical structures. It achieves a 98.91% average rate in correct bond label assignments on an antibiotics dataset, showcasing its effectiveness for comparing molecular bond lengths across geometries of varying complexity and size. The executable file and software resources for running MolGC can be downloaded from https://github.com/AbimaelGP/MolGC/tree/main .

Deep learning algorithms applied to computational chemistry.

Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. However, no model has achieved perfect performance in solving all problems, and the pros and cons of each approach remain unclear to those new to the field. Therefore, this paper aims to review deep learning algorithms that have been applied to solve molecular challenges in computational chemistry. We proposed a comprehensive categorization that encompasses two primary approaches; conventional deep learning and geometric deep learning models. This classification takes into account the distinct techniques employed by the algorithms within each approach. We present an up-to-date analysis of these algorithms, emphasizing their key features and open issues. This includes details of input descriptors, datasets used, open-source code availability, task solutions, and actual research applications, focusing on general applications rather than specific ones such as drug discovery. Furthermore, our report discusses trends and future directions in molecular algorithm design, including the input descriptors used for each deep learning model, GPU usage, training and forward processing time, model parameters, the most commonly used datasets, libraries, and optimization schemes. This information aids in identifying the most suitable algorithms for a given task. It also serves as a reference for the datasets and input data frequently used for each algorithm technique. In addition, it provides insights into the benefits and open issues of each technique, and supports the development of novel computational chemistry systems.

An Equilibrium State Diagram for Storage Stability and Conservation of Active Ingredients in a Functional Food Based on Polysaccharides Blends.

A functional food as a matrix based on a blend of carbohydrate polymers (25% maltodextrin and 75% inulin) with quercetin and Bacillus claussi to supply antioxidant and probiotic properties was prepared by spray drying. The powders were characterized physiochemically, including by moisture adsorption isotherms, X-ray diffraction (XRD), scanning electron microscopy (SEM), and modulated differential scanning calorimetry (MDSC). The type III adsorption isotherm developed at 35 °C presented a monolayer content of 2.79 g of water for every 100 g of dry sample. The microstructure determined by XRD presented three regions identified as amorphous, semicrystalline, and crystalline-rubbery states. SEM micrographs showed variations in the morphology according to the microstructural regions as (i) spherical particles with smooth surfaces, (ii) a mixture of spherical particles and irregular particles with heterogeneous surfaces, and (iii) agglomerated irregular-shape particles. The blend's functional performance demonstrated antioxidant activities of approximately 50% of DPPH scavenging capacity and viability values of 6.5 Log10 CFU/g. These results demonstrated that the blend displayed functional food behavior over the complete interval of water activities. The equilibrium state diagram was significant for identifying the storage conditions that promote the preservation of functional food properties and those where the collapse of the microstructure occurs.

Disinfection mechanism of the photocatalytic activity of SnO2 thin films against Candida albicans, proposed from experimental and simulated perspectives.

Nosocomial infections are an important health problem and cause of complications and death in hospitalized patients. This problem should be solved from the preventive angle, avoiding the spread of infections by designing disinfection methods based on the photocatalytic activity of semiconductor materials such as tin oxide (SnO2). The antimicrobial activity of UV light was tested by using inoculation with Candida albicans ATCC10231 on SnO2 thin films and counting colony forming units (CFU). The interaction of UV light with SnO2 was analyzed by density functional theory (DFT) and the extension to the Hubbard model (DFT+U) schemes to predict the electron behavior at the subatomic level. After exposure to UV light, C. albicans showed a reduction of 36.5% in viable cells, and when SnO2 was included, cell viability was reduced by 60.2%. Measurements of the electronic structure obtained by the first-principle calculations under the DFT and DFT+U schemes showed that the O-p orbitals mediate the oxidation process in the bulk semiconductor. By including the surface effects when cleaving the (1 0 0) plane, the three orbitals O-p, Sn-p, and Sn-s are the mediators. SnO2 films are promising antimicrobial coatings because UV light has a synergic activity with thin films, resulting in faster disinfection.

Cytotoxicity of protein-carbon nanotubes on J774 macrophages is a functionalization grade-dependent effect.

Carbon nanotubes (CNTs) are used as carriers in medicine due to their ability to be functionalized with chemical substances. However, cytotoxicity analysis is required prior to use for in vivo models. The aim of this study was to evaluate the cytotoxic effect of CNTs functionalized with a 46 kDa surface protein from Entamoeba histolytica (P46-CNTs) on J774A macrophages. With this purpose, CNTs were synthesized by spray pyrolysis and purified (P-CNTs) using sonication for 48 h. A 46 kDa protein, with a 4.6-5.4 pI range, was isolated from E. histolytica HM1:IMSS strain trophozoites using an OFFGEL system. The P-CNTs were functionalized with the purified 46 kDa protein, classified according to their degree of functionalization, and characterized by Raman and Infrared spectroscopy. In vitro cytotoxicity was evaluated by MTT, apoptosis, and morphological assays. The results demonstrated that P46-CNTs exhibited cytotoxicity dependent upon the functionalized grade. Contrary to what was expected, P46-CNTs with a high grade of functionalization were more toxic to J774 macrophages than P46-CNTs with a low grade of functionalization, than P-CNTs, and had a similar level of toxicity as UP-CNT. This suggests that the nature of the functionalized protein plays a key role in the cytotoxicity of these nanoparticles.