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[Display omitted] • QSAR model based on 5z-scale was established to predict ACE-I pentapeptides. • Two novel ACE-I peptides were obtained by QSAR model and in silico screening. • PLITT showed the strongest ACE-I activity. • Hydrogen bonding interactions played a critical role in ACE inhibition. • The inhibition mode of PLITT was a mixture of non-competition and competition. Food-derived angiotensin-converting enzyme-inhibitory (ACE-I) peptides have attracted extensive attention. Herein, the ACE-I peptides from Scomber japonicus muscle hydrolysates were screened, and their mechanisms of action and inhibition stability were explored. The quantitative structure–activity relationship (QSAR) model based on 5z-scale metrics was developed to rapidly screen for ACE-I peptides. Two novel potential ACE-I peptides (LTPFT, PLITT) were predicted through this model coupled with in silico screening, of which PLITT had the highest activity (IC 50 : 48.73 ± 7.59 μM). PLITT inhibited ACE activity with a mixture of non-competitive and competitive mechanisms, and this inhibition mainly contributed to the hydrogen bonding based on molecular docking study. PLITT is stable under high temperatures, pH, glucose, and NaCl. The zinc ions (Zn2+) and copper ions (Cu2+) enhanced ACE-I activity. The study suggests that the QSAR model is effective in rapidly screening for ACE-I inhibitors, and PLITT can be supplemented in foods to lower blood pressure. [ABSTRACT FROM AUTHOR]

Sophoridine, which is derived from the Leguminous plant Sophora alopecuroides L., has certain pharmacological activity as a new anticancer drug. Herein, a series of novel N-substituted sophoridine derivatives was designed, synthesized and evaluated with anticancer activity. Through QSAR prediction models, it was discovered that the introduction of a benzene ring as a main pharmacophore and reintroduced into a benzene in para position on the phenyl ring in the novel sophoridine derivatives improved the anticancer activity effectively. In vitro, 28 novel compounds were evaluated for anticancer activity against four human tumor cell lines (A549, CNE-2, HepG-2, and HEC-1-B). In particular, Compound 26 exhibited remarkable inhibitory effects, with an IC 50 value of 15.6 μM against HepG-2 cells, surpassing cis-Dichlorodiamineplatinum (II). Molecular docking studies verified that the derivatives exhibit stronger binding affinity with DNA topoisomerase I compared to sophoridine. In addition, 26 demonstrated significant inhibition of DNA Topoisomerase I and could arrest cells in G 0 /G 1 phase. This study provides valuable insights into the design and synthesis of N-substituted sophoridine derivatives with anticancer activity. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract A myriad of phytochemicals may have potential to lead toxicity and endocrine disruption effects by interfering with nuclear hormone receptors. In this examination, the toxicity and estrogen receptor−binding abilities of a set of 2826 phytochemicals were evaluated. The endpoints mutagenicity, carcinogenicity (both CAESAR and ISS models), developmental toxicity, skin sensitization and estrogen receptor relative binding affinity (ER_RBA) were studied using the VEGA QSAR modeling package. Alongside the predictions, models were providing possible information for applicability domains and most similar compounds as similarity sets from their training sets. This information was subjected to perform the clustering and classification of chemicals using Self−Organizing Maps. The identified clusters and their respective indicators were considered as potential hotspot structures for the specified data set analysis. Molecular screening interpretations of models were exhibited accurate predictions. Moreover, the indication sets were defined significant clusters and cluster indicators with probable prediction labels (precision). Accordingly, developed QSAR models showed good predictive abilities and robustness, which observed from applicability domains, representation spaces, clustering and classification schemes. Furthermore, the designed new path could be useful as a valuable approach to determine toxicity levels of phytochemicals and other environmental pollutants and protect the human health. Highlights • Six toxic endpoints were elucidated for a set of 2826 phytochemicals. • Prediction probabilities of data set were defined by applicability domain analysis. • The specific cluster indicators were determined by using the Self-Organizing Maps. • Cluster indicators were represented as novel hotspot structures for clusters. [ABSTRACT FROM AUTHOR]

Based on modern literature data about biological activity of E7010 derivatives, a series of new sulfonamides as potential anticancer drugs were rationally designed by QSAR modeling methods Сlassification learning QSAR models to predict the tubulin polymerization inhibition activity of novel sulfonamides as potential anticancer agents were created using the Online Chemical Modeling Environment (OCHEM) and are freely available online on OCHEM server at https://ochem.eu/article/107790 . A series of sulfonamides with predicted activity were synthesized and tested against 60 human cancer cell lines with growth inhibition percent values. The highest antiproliferative activity against leukemia (cell lines K-562 and MOLT-4), non-small cell lung cancer (cell line NCI-H522), colon cancer (cell lines NT29 and SW-620), melanoma (cell lines MALME-3M and UACC-257), ovarian cancer (cell lines IGROV1 and OVCAR-3), renal cancer (cell lines ACHN and UO-31), breast cancer (cell line T-47D) was found for compounds 4 – 9 . According to the docking results the compounds 4–9 induce cytotoxicity by the disruption of the microtubule dynamics by inhibiting tubulin polymerization via effective binding into colchicine domain, similar the E7010. [ABSTRACT FROM AUTHOR]

In the paper first the two main learning strategies of the artificial neural networks (ANNs), the error-back propagation (EBP) and Kohonen self-organizing maps (SOM) are briefly described. Next, two nonstandard network layouts of the ANNs (bottle-neck and pyramidal decision tree) one for each of both learning strategies are suggested. In the last part, the use of counter-propagation (CP) ANN for handling chemical structures in QSAR modeling, classification and clustering is discussed. These concepts are of particular interest for the computer-aided drug research and in computer-aided toxicology. In the case study the results of fish toxicity research are described. [ABSTRACT FROM AUTHOR]

• Novel functional soy chhurpi cheese was produced using L. delbrueckii strains. • Soy chhurpi produced using L. delbrueckii WS4 had highest yield and functionality. • Simulated in vitro GI digestion enhanced ACE-inhibitory and antioxidant effect. • Bioactive peptides with potential ACE-inhibitory and antioxidant activity detected. • Molecular docking and QSAR models predicted ACE-inhibitory peptides in soy chhurpi. A novel soy chhurpi product was developed by fermentation of soymilk using proteolytic Lactobacillus delbrueckii strains isolated from traditional chhurpi production of Sikkim Himalaya. Soymilk fermentation by L. delbrueckii WS4 was associated with the hydrolysis of globulin proteins, with observed antioxidant, and ACE-inhibitory activity which further increased upon simulated in vitro gastrointestinal digestion. Peptidomics analysis of soy chhurpi and its gastrointestinal digest resulted in the identification of bioactive peptides with ACE-inhibitory and antioxidant properties. In silico antihypertensive property prediction followed by molecular docking study demonstrated strong binding affinity of selected peptides with ACE. The glycinin-derived peptide, SVIKPPTDE escaped gastrointestinal digestion and demonstrated strong non-bond interactions with ACE catalytic residues. QSAR models predicted an ACE-inhibitory IC 50 of 21.29 µM for SVIKPPTDE. This is the first report on the production of novel functional soy chhurpi cheese using defined starter strains and the identification of bioactive peptides in undigested and gastrointestinal digested soy chhurpi. [ABSTRACT FROM AUTHOR]

Abstract: Validated predictive QSAR modeling was done on some N-aryl-oxazolidinone-5-carboxamides for higher anti-HIV protease activities. Stepwise regression developed significant models showing importance of atom based descriptors like RTSA indices, Wang–Ford charges and different whole molecular descriptors. The true predictabilities of QSAR models were justified by challenging these against an external dataset. A representative high active compound was predicted by this modeling. It showed that internal and external validations may lead to the same conclusion. [Copyright &y& Elsevier]

Abstract: The applicability of the newly developed RM1 and PM6 methods implemented in the semiempirical quantum chemistry mopac2009™ software package in modeling free radical scavenging activity of flavonoids was examined. Bond dissociation enthalpy (BDE) of OH groups could be calculated much faster than with DFT method but with similar quality. Despite the known shortcomings of the Trolox equivalent antioxidant capacity (TEAC) assay, we show that taking into account the hydrogen atom transfer (HAT) mechanism of free radical scavenging of flavonoids encoded by minimal BDE values (BDEmin) and the number of OH groups (nOH), as well as experimental data, reasonable QSAR models could be developed. For TEAC values of 38 flavonoids measured by the ABTS free radical, a model based on BDEmin and nOH was developed, having very good statistical parameters (r =0.983, r cv =0.976). The applicability of this model to three different data sets of flavonoids and reliability of TEAC values measured in distinct laboratories were discussed. Finally, a reasonably good model of experimental vitamin C equivalent antioxidant capacity (VCEAC) of 36 flavonoids was obtained (r =0.954, r cv =0.947), involving BDEmin and nOH as descriptors. Additionally, all presented models have comparable fit and cross-validated statistical parameters, as well as significant regression coefficients. [Copyright &y& Elsevier]

A Kohonen self-organizing map (SOM) is used to classify a data set consisting of dihydrofolate reductase inhibitors with the help of an external set of Dragon descriptors. The resultant classification is used to generate training, cross-validation (CV) and prediction sets for QSAR modeling using the ADAPT methodology. The results are compared to those of QSAR models generated using sets created by activity binning and a sphere exclusion method. The results indicate that the SOM is able to generate QSAR sets that are representative of the composition of the overall data set in terms of similarity. The resulting QSAR models are half the size of those published and have comparable RMS errors. Furthermore, the RMS errors of the QSAR sets are consistent, indicating good predictive capabilities as well as generalizability. [Copyright &y& Elsevier]

In the fields of pharmaceutical research and biomedical sciences, QSAR modeling is an established approach during drug discovery for prediction of biological activity of drug candidates. Yet, QSAR modeling poses a series of open challenges. First, chemical compounds are represented on a high-dimensional space and thus feature selection is typically applied, although this task entails a challenging combinatorial problem with potential loss of information. Second, the definition of the applicability domain of a QSAR model is a desirable aspect to determine the reliability of predictions on unseen chemicals, which is often difficult to assess due to the extent of the chemical space. Finally, interpretability of these models is also a critical issue for drug designers. The purpose of this work is to thoroughly assess the application of neural-based methods and recent advances deep learning for QSAR modeling. We hypothesize that neural-based methods can overcome the need to perform a descriptor selection phase. We developed three QSAR models based on neural networks for prediction of relevant chemical and biomedical properties that, in the absence of any feature selection step, can outperform the state-of-the-art models for such properties. We also implemented an embedded applicability domain technique based on network output probabilities that proved to be effective; its application improved the predictive performance of the model. Finally, we proposed the use of a post hoc feature analysis technique based on an aggregation of network weights, which enabled effective detection of relevant features in the model. • DNN-based models outperformed the baselines without the need for feature selection. • An embedded applicability domain method based on confidence estimation is proposed. • Post hoc analysis of network weights enables insight-gaining into the model. [ABSTRACT FROM AUTHOR]