The inactivation of eggs of helminthes under the action of narrowband ultraviolet radiation of excilamps

The inactivation of eggs of Opisthorchis felineus and Diphyllobothrium latum in the water under the action of UV excilamps at 222 and 282 nm in dependence on the surface dose of radiation was studied. It was observed that the water disinfection from eggs of helminthes was more efficient at 222 nm, than at 282 nm. At the surface dose up to 5 mJ/cm2 of UV radiation at 222 nm up to 85 % of Opisthorchis felineus eggs were inactivated. At the comparable surface dose of UV radiation at 222 nm up to 56 % of Diphyllobothrium latum eggs were inactivated

GraphDelta : MPNN scoring function for the affinity prediction of protein-ligand complexes

In this work, we present graph-convolutional neural networks for the prediction of binding constants of protein-ligand complexes. We derived the model using multi task learning, where the target variables are the dissociation constant (Kd), inhibition constant (Ki), and half maximal inhibitory concentration (IC50). Being rigorously trained on the PDBbind dataset, the model achieves the Pearson correlation coefficient of 0.87 and the RMSE value of 1.05 in pK units, outperforming recently developed 3D convolutional neural network model Kdeep.

Morphology of the surface of technically pure titanium VT1-0 after electroexplosive carbonization with a weighed zirconium oxide powder sample and electron beam treatment

Titanium is carbonized by the electroexplosive method. Formation of a surface alloyed layer and a coating on the treated surface is established by the methods of transmission electron microscopy. The morphology and elemental composition of the alloyed layer are analyzed. A dependence of the structure of the modified layer subjected to electron gun treatment on the absorbed power density is revealed

Recommender systems in antiviral drug discovery

Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: Collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes ("interactions") for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery

3D matters! 3D-RISM and 3D convolutional neural network for accurate bioaccumulation prediction

In this work, we present a new method for predicting complex physicalchemical properties of organic molecules. The approach utilizes 3D convolutional neural network (ActivNet4) that uses solvent spatial distributions around solutes as input. These spatial distributions are obtained by a molecular theory called threedimensional reference interaction site model (3D-RISM). We have shown that the method allows one to achieve a good accuracy of prediction of bioconcentration factor (BCF) which is difficult to predict by direct application of methods of molecular theory or simulations. Our research demonstrates that combination of molecular theories with modern machine learning approaches can be effectively used for predicting properties that are otherwise inaccessible to purely theory-based models

A survey of multi-task learning methods in chemoinformatics

Despite the increasing volume of available data, the proportion of experimentally measured data remains small compared to the virtual chemical space of possible chemical structures. Therefore, there is a strong interest in simultaneously predicting different ADMET and biological properties of molecules, which are frequently strongly correlated with one another. Such joint data analyses can increase the accuracy of models by exploiting their common representation and identifying common features between individual properties. In this work we review the recent developments in multi-learning approaches as well as cover the freely available tools and packages that can be used to perform such studies

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495

Exploring Chemical Reaction Space With Reaction Difference Fingerprints and Parametric t-SNE

Humans prefer visual representations for the analysis of large databases. In this work, we suggest a method for the visualization of the chemical reaction space. Our technique uses the t-SNE approach that is parameterized by a deep neural network (parametric t-SNE). We demonstrated that the parametric t-SNE combined with reaction difference fingerprints could provide a tool for the projection of chemical reactions onto a low-dimensional manifold for easy exploration of reaction space. We showed that the global reaction landscape, been projected onto a 2D plane, corresponds well with already known reaction types. The application of a pretrained parametric t-SNE model to new reactions allows chemists to study these reactions in a global reaction space. We validated the feasibility of this approach for two marketed drugs: darunavir and oseltamivir. We believe that our method can help to explore reaction space and will inspire chemists to find new reactions and synthetic ways. </div

BigSolDB: Solubility Dataset of Compounds in Organic Solvents and Water in a Wide Range of Temperatures

Solubility is crucial in organic chemistry and holds significant value in the field of medicinal chemistry. Employing computational and QSPR modeling for solubility estimation is favorable as it reduces experimental costs. However, high-quality experimental data is essential for training these QSPR models. In our study, we compiled a dataset consisting of 54,273 experimental solubility values within a temperature range of 243.15 to 403.15 K in various organic solvents and water. This dataset can be used as a reference for individual values or training solubility QSPR models. We conducted a statistical analysis and identified prevalent patterns in the data. Furthermore, we developed an interactive, parametric t-SNE-based tool to explore the chemical space of solutes. Utilizing this tool, we characterized common scaffolds in the dataset and demonstrated that the chemical space of solutes is extensive and diverse