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AbstractBioactive peptides (BPs) are short amino acid sequences that that are known to exhibit physiological characteristics such as antioxidant, antimicrobial, antihypertensive and antidiabetic properties, suggesting that they could be exploited as functional foods in the nutraceutical industry. These BPs can be derived from a variety of food sources, including milk, meat, marine, and plant proteins. In the past decade, various methods including in silico, in vitro, and in vivo techniques have been explored to unravel underlying mechanisms of BPs. To forecast interactions between peptides and their targets, in silico methods such as BIOPEP, molecular docking and Quantitative Structure-Activity Relationship modeling have been employed. Additionally, in vitro research has examined how BPs affect enzyme activities, protein expressions, and cell cultures. In vivo studies on the contrary have appraised the impact of BPs on animal models and human subjects. Hence, in the light of recent literature, this review examines the multifaceted aspects of BPs production from milk, meat, marine, and plant proteins and their potential bioactivities. We envisage that the various concepts discussed will contribute to a better understanding of the food derived BP production, which could pave a way for their potential applications in the nutraceutical industry. [ABSTRACT FROM AUTHOR]

PD-L1 is a key immunotarget involved in binding to its receptor PD-1. PD-L1/PD-1 interface blocking using antibodies (or small molecules) is the central area of interest for tumor suppression in various cancers. Blocking the PD-L1/PD-1 pathway in the tumor cells results in its immune activation and destruction, and thereby restoring the T-cell proliferation and cytokine production. The active binding site interface residues of PD-L1/PD-1 were experimentally known and proven by structural biology and site-directed mutagenesis studies. Structure-based molecular design technique was employed to identify the inhibitors for blocking the PD-L1/PD-1 interface. Nine hits to leads were identified from the SPECS small molecule database by machine learning, molecular docking, and molecular dynamics simulation techniques. Following this, a machine learning-assisted QSAR modeling approach was implemented using ChEMBL database to gain insights into the inhibitory potential of PD-L1 inhibitors and predict the activity of our previously screened nine hit molecules. The best leads identified in the present study bind strongly with the active sites of PD-L1/PD-1 interface residues, which include A121, M115, I116, S117, I54, Y56, D122, and Y123. These computational leads are considered promising molecules for further in vitro and in vivo analysis to be developed as potential PD-L1 checkpoint inhibitors to cure different types of cancers. [ABSTRACT FROM AUTHOR]

The use of 3D printing technologies by industry and consumers is expanding. However, the approaches to assess the risk of lung carcinogenesis from the emissions of 3D printers have not yet been developed. The objective of the study was to demonstrate a methodology for modeling lung cancer risk related to specific exposure levels as derived from an experimental study of 3D printer emissions for various types of filaments (ABS, PLA, and PETG). The emissions of 15 filaments were assessed at varying extrusion temperatures for a total of 23 conditions in a Class 1,000 cleanroom following procedures described by ANSI/CAN/UL 2904. Three approaches were utilized for cancer risk estimation: (a) calculation based on PM2.5 and PM10 concentrations, (b) a proximity assessment based on the pulmonary deposition fraction, and (c) modeling based on the mass‐weighted aerodynamic diameter of particles. The combined distribution of emitted particles had the mass median aerodynamic diameter (MMAD) of 0.35 μm, GSD 2.25. The average concentration of PM2.5 was 25.21 μg/m3. The spline‐based function of aerodynamic diameter allowed us to reconstruct the carcinogenic potential of seven types of fine and ultrafine particles (crystalline silica, fine TiO2, ultrafine TiO2, ambient PM2.5 and PM10, diesel particulates, and carbon nanotubes) with a correlation of 0.999, P < 0.00001. The central tendency estimation of lung cancer risk for 3D printer emissions was found at the level of 14.74 cases per 10,000 workers in a typical exposure scenario (average cumulative exposure of 0.3 mg/m3 – years), with the lowest risks for PLA filaments, and the highest for PETG type. The approaches to assess risk of lung carcinogenesis for particles emitted by 3D printers have been proposed. The emission from 15 filaments were assessed at varying extrusion temperatures. The spline‐based function of aerodynamic diameter allowed for reconstruction of the carcinogenic potential of seven types of fine and ultrafine particles. The central tendency estimation of lung cancer risk for 3D printer emissions was found at the level of 14.74 cases per 10,000 workers for average cumulative exposure of 0.3 mg/m3 – years. [ABSTRACT FROM AUTHOR]

Several anticorrosive treatments have been proposed over time to create protective layers to hinder the corrosion phenomenon. In recent years, organic molecules from plant extracts and expired drugs have been tested due to their potential corrosion inhibition properties. However, direct corrosion inhibition efficiency (IE%) evaluation requires costly reactants and a specific experimental setup. Quantitative-structure activity relationship (QSAR) proposes modeling IE% in terms of variables measured in previous experiments or determined by theoretical approaches. Computed descriptors, such as ionization energy (I), electronic affinity (A), or global hardness, were added to a database of physicochemical properties. This work compares several methodologies to obtain precise yet portable mathematical models for predicting corrosion inhibition efficiency. As an original approach from this research group, nonlinear autoregressive moving average with exogenous inputs (NARMAX), using forward regression with orthogonal least squares (FROLS), models were implemented as a robust method to get nonlinear portable models and to determine the most important variables impacting IE%. Contrastingly, ordinary least squares (OLS) methodology was employed with the novelty of applying power series expansions from the promoted FROLS variables for linear and polynomial regression with only one independent variable, which resulted in clearer graph visualization of trends and the ease of proposing thumb rules based on raw information. Finally, IBM Watson was also compared as a robust yet non-portable and highly parametrized alternative to conventional mathematical approaches, based on extra trees regressor (ETR). The models were compared using mean absolute percentage error (MAPE), mean-squared error (MSE), and root-mean-squared error (RMSE). Overall, models with fewer variables and up to second-order terms show improved performance. The main tendencies of IE%, drawn by inferences for 630 substances by second-order NARX, are analyzed. Also, the determinant role of the highest occupied molecular orbital energy was reported. Experimentalists can take advantage of a “cost-free” general approach that can obtain estimations for IE% values with errors of about 6 %, in particular the second-order NARX model.

ABSTRACT Global challenges presented by multidrug-resistant Acinetobacter baumannii infections have stimulated the development of new treatment strategies. We reported that outer membrane protein W (OmpW) is a potential therapeutic target in A. baumannii. Here, a library of 11,648 natural compounds was subjected to a primary screening using quantitative structure-activity relationship (QSAR) models generated from a ChEMBL data set with >7,000 compounds with their reported minimal inhibitory concentration (MIC) values against A. baumannii followed by a structure-based virtual screening against OmpW. In silico pharmacokinetic evaluation was conducted to assess the drug-likeness of these compounds. The ten highest-ranking compounds were found to bind with an energy score ranging from −7.8 to −7.0 kcal/mol where most of them belonged to curcuminoids. To validate these findings, one lead compound exhibiting promising binding stability as well as favorable pharmacokinetics properties, namely demethoxycurcumin, was tested against a panel of A. baumannii strains to determine its antibacterial activity using microdilution and time-kill curve assays. To validate whether the compound binds to the selected target, an OmpW-deficient mutant was studied and compared with the wild type. Our results demonstrate that demethoxycurcumin in monotherapy and in combination with colistin is active against all A. baumannii strains. Finally, the compound was found to significantly reduce the A. baumannii interaction with host cells, suggesting its anti-virulence properties. Collectively, this study demonstrates machine learning as a promising strategy for the discovery of curcuminoids as antimicrobial agents for combating A. baumannii infections.IMPORTANCEAcinetobacter baumannii presents a severe global health threat, with alarming levels of antimicrobial resistance rates resulting in significant morbidity and mortality in the USA, ranging from 26% to 68%, as reported by the Centers for Disease Control and Prevention (CDC). To address this threat, novel strategies beyond traditional antibiotics are imperative. Computational approaches, such as QSAR models leverage molecular structures to predict biological effects, expediting drug discovery. We identified OmpW as a potential therapeutic target in A. baumannii and screened 11,648 natural compounds. We employed QSAR models from a ChEMBL bioactivity data set and conducted structure-based virtual screening against OmpW. Demethoxycurcumin, a lead compound, exhibited promising antibacterial activity against A. baumannii, including multidrug-resistant strains. Additionally, demethoxycurcumin demonstrated anti-virulence properties by reducing A. baumannii interaction with host cells. The findings highlight the potential of artificial intelligence in discovering curcuminoids as effective antimicrobial agents against A. baumannii infections, offering a promising strategy to address antibiotic resistance.

Ulvan, a sulfated heteropolysaccharide with structural and functional properties of interest for various uses, was extracted from the green seaweed Ulva papenfussii. U. papenfussii is an unexplored Ulva species found in the South China Sea along the central coast of Vietnam. Based on dry weight, the ulvan yield was ~15% (w/w) and the ulvan had a sulfate content of 13.4 wt%. The compositional constitution encompassed L-Rhamnose (Rhap), D-Xylose (Xylp), D-Glucuronic acid (GlcAp), L-Iduronic acid (IdoAp), D-Galactose (Galp), and D-Glucose (Glcp) with a molar ratio of 1:0.19:0.35:0.52:0.05:0.11, respectively. The structure of ulvan was determined using High-Performance Liquid Chromatography (HPLC), Fourier Transform Infrared Spectroscopy (FT-IR), and Nuclear Magnetic Resonance spectroscopy (NMR) methods. The results showed that the extracted ulvan comprised a mixture of two different structural forms, namely ("A3s") with the repeating disaccharide [→4)-β-D-GlcAp-(1→4)-α-L-Rhap 3S-(1→]n, and ("B3s") with the repeating disaccharide [→4)-α-L-IdoAp-(1→4)-α-L-Rhap 3S(1→]n. The relative abundance of A3s, and B3s was 1:1.5, respectively. The potential anticarcinogenic attributes of ulvan were evaluated against a trilogy of human cancer cell lineages. Concomitantly, Quantitative Structure–Activity Relationship (QSAR) modeling was also conducted to predict potential adverse reactions stemming from pharmacological interactions. The ulvan showed significant antitumor growth activity against hepatocellular carcinoma (IC50 ≈ 90 µg/mL), human breast cancer cells (IC50 ≈ 85 µg/mL), and cervical cancer cells (IC50 ≈ 67 µg/mL). The QSAR models demonstrated acceptable predictive power, and seven toxicity indications confirmed the safety of ulvan, warranting its candidacy for further in vivo testing and applications as a biologically active pharmaceutical source for human disease treatment. [ABSTRACT FROM AUTHOR]

Ulvan, a sulfated heteropolysaccharide with structural and functional properties of interest for various uses, was extracted from the green seaweed Ulva papenfussii. U. papenfussii is an unexplored Ulva species found in the South China Sea along the central coast of Vietnam. Based on dry weight, the ulvan yield was ~15% (w/w) and the ulvan had a sulfate content of 13.4 wt%. The compositional constitution encompassed L-Rhamnose (Rhap), D-Xylose (Xylp), D-Glucuronic acid (GlcAp), L-Iduronic acid (IdoAp), D-Galactose (Galp), and D-Glucose (Glcp) with a molar ratio of 1:0.19:0.35:0.52:0.05:0.11, respectively. The structure of ulvan was determined using High-Performance Liquid Chromatography (HPLC), Fourier Transform Infrared Spectroscopy (FT-IR), and Nuclear Magnetic Resonance spectroscopy (NMR) methods. The results showed that the extracted ulvan comprised a mixture of two different structural forms, namely (“A3s”) with the repeating disaccharide [→4)-β-D-GlcAp-(1→4)-α-L-Rhap 3S-(1→]n, and (“B3s”) with the repeating disaccharide [→4)-α-L-IdoAp-(1→4)-α-L-Rhap 3S(1→]n. The relative abundance of A3s, and B3s was 1:1.5, respectively. The potential anticarcinogenic attributes of ulvan were evaluated against a trilogy of human cancer cell lineages. Concomitantly, Quantitative Structure–Activity Relationship (QSAR) modeling was also conducted to predict potential adverse reactions stemming from pharmacological interactions. The ulvan showed significant antitumor growth activity against hepatocellular carcinoma (IC50 ≈ 90 µg/mL), human breast cancer cells (IC50 ≈ 85 µg/mL), and cervical cancer cells (IC50 ≈ 67 µg/mL). The QSAR models demonstrated acceptable predictive power, and seven toxicity indications confirmed the safety of ulvan, warranting its candidacy for further in vivo testing and applications as a biologically active pharmaceutical source for human disease treatment.

A myriad of tobacco-associated chemicals may have possibilities to developmental/reproductive axis and endocrine-disruption impacts. Mostly they breach the biotransformation of cholesterol in mitochondria by interfering with steroidogenic pathway genes, prompting to adverse effects in steroid biosynthesis; however, studies are scanty. The quantitative structure–activity relationship (QSAR) modeling and comparative docking strategies were used to understand structural features of dataset compounds that influence developmental/reproductive toxicity and estrogen and androgen receptor–binding abilities, and to predict binding levels of toxicants with steroidogenic acute regulatory protein (StAR) and cholesterol side-chain cleavage enzyme (CYP11A1) active sites. Developed QSAR models presented good robustness and predictive ability that were determined from the applicability domain and, clustering and classification of chemicals by performing self-organizing maps. Accordingly, the exorbitant amount of polycyclic aromatic hydrocarbons (PAHs) and a limited number of other chemicals including N-nitrosamines and nicotine was represented as potential developmental/reproductive toxicants as well as estrogen and androgen receptor binders. From the docking analysis, hydrogen bonding, nonpolar, atomic π-stacking, and π-cation interactions were found between PAHs (bay and fjord structural pockets) and functional hotspot residues of StAR and CYP11A1, which strengthened the subtle structural changes at domains. These govern barrier effects to cholesterol binding and/or locking cholesterol to complicate its ejection from the Ω1 loop of StAR, and further mitigates steroid biosynthesis through cholesterol by CYP11A1; therefore, they are presumably considered as block-cluster mechanisms. These outcomes are significant to be hopeful to estimate developmental/reproductive toxicity and endocrine-disruption activities of other environmental pollutants, and could be useful for further assessment to discover binding mechanisms of PAHs with other steroidogenesis pathway genes. [ABSTRACT FROM AUTHOR]

New models for ACE2 receptor binding, based on QSAR and docking algorithms were developed, using XRD structural data and ChEMBL 26 database hits as training sets. The selectivity of the potential ACE2-binding ligands towards Neprilysin (NEP) and ACE was evaluated. The Enamine screening collection (3.2 million compounds) was virtually screened according to the above models, in order to find possible ACE2-chemical probes, useful for the study of SARS-CoV2-induced neurological disorders. An enzymology inhibition assay for ACE2 was optimized, and the combined diversified set of predicted selective ACE2-binding molecules from QSAR modeling, docking, and ultrafast docking was screened in vitro. The in vitro hits included two novel chemotypes suitable for further optimization. [ABSTRACT FROM AUTHOR]

[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]

Human African trypanosomiasis (HAT), a fatal infection caused by Trypanosoma brucei (T. brucei) is considered as a neglected disease in the tropical areas, and newer agents with unique mechanism of action are urgently needed. In this work, 65 Imidazopyridine analogues from known literature were selected for building statistically robust genetic algorithm (GA) based QSAR models. Furthermore, values for the various cross-validation properties supported its statistical robustness (model 1, R tr 2 = 0. 8 5 3 7 , RMSE cv = 0. 3 5 8 6 , MAE cv = 0. 2 6 4 2 , CCC cv = 0. 8 9 5 3 , and Q LMO 2 = 0. 7 8 8 7). Our in silico ADMET analysis revealed that a designed molecule, S10 may act as potent lead (T. brucei, pEC 5 0 (μ M), predicted = 8. 2 0 0) with better pharmacokinetics, no carcinogenicity, class III acute oral toxicity, minimal OCT1 and OCT2 inhibitions and no eye corrosion profiles. Our molecular docking analysis (on 42 drug targets) for a dataset and designed molecules demonstrated higher binding affinity of Imidazopyridine analogues with T. brucei farnesyl diphosphate synthase (TbFPPS) (PDB id: 2I19). This observation was further supported by 100 ns molecular dynamics analysis retaining better stability of complex. Thus, imidazopyridine analogues would provide a promising scaffold for the development of anti-HAT agents. Human African trypanosomiasis (HAT), a fatal infection caused by Trypanosoma brucei, is considered as a neglected disease in the tropical areas, and newer agents with unique mechanism of action are urgently needed. In this present work, 65 Imidazopyridine analogues from known literature were selected for building statistically robust genetic algorithm (GA) based QSAR models. Furthermore, values for the various cross-validation properties supported its statistical robustness. Our in-silico ADMET analysis revealed that a designed molecule, S10 may act as potent lead (T. brucei, pEC 50 (μM), predicted = 8.200) with better pharmacokinetics, no carcinogenicity, class III acute oral toxicity, minimal OCT1 and OCT2 inhibitions, and no eye corrosion profiles. [ABSTRACT FROM AUTHOR]

With the consolidation of deep learning in drug discovery, several novel algorithms for learning molecular representations have been proposed. Despite the interest of the community in developing new methods for learning molecular embeddings and their theoretical benefits, comparing molecular embeddings with each other and with traditional representations is not straightforward, which in turn hinders the process of choosing a suitable representation for Quantitative Structure–Activity Relationship (QSAR) modeling. A reason behind this issue is the difficulty of conducting a fair and thorough comparison of the different existing embedding approaches, which requires numerous experiments on various datasets and training scenarios. To close this gap, we reviewed the literature on methods for molecular embeddings and reproduced three unsupervised and two supervised molecular embedding techniques recently proposed in the literature. We compared these five methods concerning their performance in QSAR scenarios using different classification and regression datasets. We also compared these representations to traditional molecular representations, namely molecular descriptors and fingerprints. As opposed to the expected outcome, our experimental setup consisting of over |$25 000$| trained models and statistical tests revealed that the predictive performance using molecular embeddings did not significantly surpass that of traditional representations. Although supervised embeddings yielded competitive results compared with those using traditional molecular representations, unsupervised embeddings tended to perform worse than traditional representations. Our results highlight the need for conducting a careful comparison and analysis of the different embedding techniques prior to using them in drug design tasks and motivate a discussion about the potential of molecular embeddings in computer-aided drug design. [ABSTRACT FROM AUTHOR]

New models for ACE2 receptor binding, based on QSAR and docking algorithms were developed, using XRD structural data and ChEMBL 26 database hits as training sets. The selectivity of the potential ACE2-binding ligands towards Neprilysin (NEP) and ACE was evaluated. The Enamine screening collection (3.2 million compounds) was virtually screened according to the above models, in order to find possible ACE2-chemical probes, useful for the study of SARS-CoV2-induced neurological disorders. An enzymology inhibition assay for ACE2 was optimized, and the combined diversified set of predicted selective ACE2-binding molecules from QSAR modeling, docking, and ultrafast docking was screened in vitro. The in vitro hits included two novel chemotypes suitable for further optimization.

Growing attention has been focussed towards a considerable amount of experimental and theoretical study of anti toxoplasmosis compounds having inhibitory activities against toxoplasmosis. Toxoplasmosis is a dangerous disease in both urban as well as rural areas. It is caused by Toxoplasma gondii. The life cycle involves feline species such as both domestic and wild cats, and other felids such as lions, etc. There are various targets for developing anti toxoplasmosis agents. One of the most promising targets is dihydrofolate reductase (DHFR). 1,3,5-Triazine compounds have been reported to inhibit the T. gondii. But there is hardly any formulation of quantitative structure-activity relationship (QSAR) involving 1,3,5-triazine inhibitors to date. Therefore, it is our target in the present study to develop QSAR models based on the computed theoretical molecular descriptors to scale the essential features responsible for the DHFR inhibition. These screened features of the selected compounds will help to design the potent congeneric series. [ABSTRACT FROM AUTHOR]

α-Glucosidase is considered a prime drug target for Diabetes Mellitus and its inhibitors are used to delay carbohydrate digestion for the treatment of diabetes mellitus. With the aim to design α-glucosidase inhibitors with novel chemical scaffolds, three folds ligand and structure based virtual screening was applied. Initially linear quantitative structure activity relationship (QSAR) model was developed by a molecular operating environment (MOE) using a training set of thirty-two known inhibitors, which showed good correlation coefficient (r2 = 0.88), low root mean square error (RMSE = 0.23), and cross-validated correlation coefficient r2 (q2 = 0.71 and RMSE = 0.31). The model was validated by predicting the biological activities of the test set which depicted r2 value of 0.82, indicating the robustness of the model. For virtual screening, compounds were retrieved from zinc is not commercial (ZINC) database and screened by molecular docking. The best docked compounds were chosen to assess their pharmacokinetic behavior. Later, the α-glucosidase inhibitory potential of the selected compounds was predicted by their mode of binding interactions. The predicted pharmacokinetic profile, docking scores and protein-ligand interactions revealed that eight compounds preferentially target the catalytic site of α-glucosidase thus exhibit potential α-glucosidase inhibition in silico. The α-glucosidase inhibitory activities of those Hits were predicted by QSAR model, which reflect good inhibitory activities of these compounds. These results serve as a guidelines for the rational drug design and development of potential novel anti-diabetic agents.

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]

Epigallocatechin gallate (EGCG) is a major polyphenols of green tea may have the possibility to inhibit epidermal growth factor receptor (EGFR) activity and lead to reduce non-small cell lung cancer (NSCLC) progression. However, EGCG has some toxic features; moreover, there is a lack of explorations into the molecular interaction mechanisms of EGCG and the EGFR. In this examination, integration of quantitative structure–activity relationship (QSAR) modeling, pharmacophore-based virtual screening, and ensemble docking approaches were used to predict potential novel EGCG analogs as effective EGFR inhibitors. QSAR modeling of logP and logS predictions and toxicity endpoint investigation for a set of 82 compounds were shown good predictive ability and robustness from the applicability domain and confusion matrix elucidations. Virtual screening and docking studies revealed that seven high potential EGCG analogs as strong EGFR binders. Molecular interactions interpretations indicated some insights into the structural features of ligands that efficiently interfere with mutation possible residues (Gly719 and Thr790) of the EGFR. The hydrogen bonds, hydrophobic interactions, atomic π-cation interactions and salt bridges of ligands are contributing additional stability to receptor structure, which can lead to blocking the intracellular protein-tyrosine kinase activity, including EGFR associated pathways activation in NSCLC. Therefore, this can characterize as a block-cluster mechanism between EGCG analogs and EGFR complexes. In silico anti-EGFR and anticancer activity predictions suggested that, ligands could act as promising pharmacological, anticancer, and drug-like templates of EGFR towards moderating the NSCLC progressions. These results and provided pinpoints could be beneficial to recognize probable therapeutic targets for NSCLC therapy. [ABSTRACT FROM AUTHOR]

Abstract A myriad of polychlorinated biphenyls (PCBs) may have potential to reproductive axis and endocrine disruptions. PCBs mostly breach the cholesterol biotransformation in mitochondria through interfering with steroidogenic genes and lead to adverse consequences in steroidogenesis; however, studies are scanty. In this examination, the combinations of quantitative structure–activity relationship (QSAR) modeling and ensemble docking approaches was performed to envisage structural properties of PCBs that influence the developmental toxicity, estrogen receptor−mediated impacts, and to provide a better comprehension of binding levels between PCBs, steroidogenic acute regulatory protein (StAR) and cholesterol side−chain cleavage enzyme (CYP11A1). Prognostic QSAR models were illustrated with good robustness and predictive ability. Models provide extensive data on applicability domain and similarities between PCBs and training set compounds was used to implement for clustering and classification of toxic PCBs by employing Self−Organizing Maps. Docking and interaction profiles interpretations provided various insights into the structural features of PCBs that influence the cholesterol binding to StAR and CYP11A1 domains. The non−polar, atomic π − stacking and halogen bonds of PCBs with novel hotspots residues of StAR and CYP11A1 was indicated subtle conformational changes that barrier to cholesterol binding and/or locks to cholesterol ejection from Ω1−loop of StAR, and inhibits cholesterol to pregnenolone biosynthesis by CYP11A1; thus, these are probably revealed as block−cluster mechanisms. These outcomes are potential to be useful to predict developmental toxicity, endocrine disruption potencies and anti-steroidogenic activities of other environmental pollutants and provided sorted pinpoints for further evaluation of interaction mechanisms of PCBs with other sterodogenic genes. Highlights • Developmental toxicity and endocrine disruption potencies of PCBs congeners were assessed. • PCBs reflect binding modes with StAR and CYP11A1 were analyzed. • PCBs signified barrier properties for cholesterol binding and adverse steroidogenic pathway impacts were revealed. [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]

The performance of quantitative structure−activity relationship (QSAR) models largely depends on the relevance of the selected molecular representation used as input data matrices. This work presents a thorough comparative analysis of two main categories of molecular representations (vector space and metric space) for fitting robust machine learning models in QSAR problems. For the assessment of these methods, seven different molecular representations that included RDKit descriptors, five different fingerprints types (MACCS, PubChem, FP2-based, Atom Pair, and ECFP4), and a graph matching approach (non-contiguous atom matching structure similarity; NAMS) in both vector space and metric space, were subjected to state-of-art machine learning methods that included different dimensionality reduction methods (feature selection and linear dimensionality reduction). Five distinct QSAR data sets were used for direct assessment and analysis. Results show that, in general, metric-space and vector-space representations are able to produce equivalent models, but there are significant differences between individual approaches. The NAMS-based similarity approach consistently outperformed most fingerprint representations in model quality, closely followed by Atom Pair fingerprints. To further verify these findings, the metric space-based models were fitted to the same data sets with the closest neighbors removed. These latter results further strengthened the above conclusions. The metric space graph-based approach appeared significantly superior to the other representations, albeit at a significant computational cost.

The mosquito, Anopheles sp. causes mosquito-transmitted diseases to human. The present study was determined percentage mortality of larvae (Anopheles stephensi) by leaf extract (Eucalyptus grandis) in aqueous medium and the inhibitory effects of selected bioactive compounds present in the leaf against the mosquito acetylcholinesterase through an in silico approach. The acetylcholinesterase (receptor) was obtained (PDB ID: 2AZG) from the Protein Data Bank (PDB) and the canonical SMILES of selected eleven phytoligands were obtained from PubChem database. The three-dimensional (3D) structure of phytoligands were procured from online CORINA software. The softwares, PyRx (Version 0.8) for receptor-ligand binding and T.E.S.T. (Version 4.1) for QSAR modelling to know predictive toxicity were used in the present study. The present results indicated that the percentage mortality was observed to the larvae (A. stephensi) at higher concentration ranges 70-100% during 48h exposure by the aqueous leaf extract of E. grandis. The binding interactions resulted six compounds may be reversible inhibitors find the binding just opening of the active site while five compounds may be irreversible inhibitors to obtain binding opposite side of the active site. The rat oral LD50 values range between 1383.11 to 4286.15 mg/kg, which may be indicated low toxic compounds with biodegradable capacity. It is concluded that experimental bioassay and in silico study, individual and/or combinations of phytoligands of E. grandis might be used as bio-larvicide to develop A. stephensi immobility. It is suggested experimental bioassay with each bioactive compound and molecular dynamics study for the validation of the present data. [ABSTRACT FROM AUTHOR]

The enormous production of fruit waste and the generation of countless organic micropollutants are serious environmental problems. To solve the problems, the biowastes, i.e., orange, mandarin, and banana peels, were used as biosorbents to remove the organic pollutants. In this application, the difficult challenge is knowing the degree of adsorption affinity of biomass for each type of micropollutant. However, since there are numerous micropollutants, it requires enormous material consumption and labor to physically estimate the adsorbability of biomass. To address this limitation, quantitative structure-adsorption relationship (QSAR) models for the adsorption assessment were established. In this process, the surface properties of each adsorbent were measured with instrumental analyzers, their adsorption affinity values for several organic micropollutants were determined through isotherm experiments, and QSAR models for each adsorbent were developed. The results showed that the tested adsorbents had significant adsorption affinity for cationic and neutral micropollutants, while the anionic one had low adsorption. As a result of the modeling, it was found that the adsorption could be predicted for a modeling set with an R2 of 0.90–0.915, and the models were validated via the prediction of a test set that was not included in the modeling set. Also, using the models, the adsorption mechanisms were identified. It is speculated that these developed models can be used to rapidly estimate adsorption affinity values for other micropollutants. • Fruit-based biowaste can be recycled for the removal of organic micropollutants. • Adsorption of cationic micropollutants by the biosorbents is notable. • QSAR models for predicting the adsorption of fruit-based biosorbents for micropollutants. • Fruit-based biosorbents mostly absorb micropollutants due to their hydrophobicity. • The adsorption characteristics of the investigated fruit-based biosorbents are comparable. [ABSTRACT FROM AUTHOR]

Estrogen receptors (ERα) are a critical target for drug design as well as a potential source of toxicity when activated unintentionally. Thus, evaluating potential ERα binding agents is critical in both drug discovery and chemical toxicity areas. Using computational tools, e.g. Quantitative Structure-Activity Relationship (QSAR) models, can predict potential ERα binding agents before chemical synthesis. The purpose of this project was to develop enhanced predictive models of ERα binding agents by utilizing advanced cheminformatics tools that can integrate publicly available bioassay data. The initial ERα binding agent data set, consisting of 446 binders and 8,307 non-binders, was obtained from the Tox21 Challenge project organized by the NIH Chemical Genomics Center (NCGC). After removing the duplicates and inorganic compounds, this data set was used to create a training set (259 binders and 259 non-binders). This training set was used to develop QSAR models using chemical descriptors. The resulting models were then used to predict the binding activity of 264 external compounds, which were available to us after the models were developed. The cross-validation results of training set [Correct Classification Rate (CCR)) = 0.72] were much higher than the external predictivity of the unknown compounds (CCR= 0.59). To improve the conventional QSAR models, all compounds in the training set were used to search PubChem and generate a profile of their biological responses across thousands of bioassays. The most important bioassays were prioritized to generate a similarity index that was used to calculate the biosimilarity score between each two compounds. The nearest neighbors for each compound within the set were then identified and its ERα binding potential was predicted by its nearest neighbors in the training set. The hybrid model performance (CCR=0.94 for cross validation; CCR=0.68 for external prediction) showed significant improvement over the original QSAR models, particularly for the activity cliffs that induce prediction errors. The results of this study indicate that the response profile of chemicals from public data provides useful information for modeling and evaluation purposes. The public big data resources should be considered along with chemical structure information when predicting new compounds, such as unknown ERα binding agents.

Predicting blood-brain barrier (BBB) permeability is essential to drug development, as a molecule cannot exhibit pharmacological activity within the brain parenchyma without first transiting this barrier. Understanding the process of permeation, however, is complicated by a combination of both limited passive diffusion and active transport. Our aim here was to establish predictive models for BBB drug permeation that include both active and passive transport. A database of 153 compounds was compiled using in vivo surface permeability product (logPS) values in rats as a quantitative parameter for BBB permeability. The open source Chemical Development Kit (CDK) was used to calculate physico-chemical properties and descriptors. Predictive computational models were implemented by machine learning paradigms (decision tree induction) on both descriptor sets. Models with a corrected classification rate (CCR) of 90% were established. Mechanistic insight into BBB transport was provided by an Ant Colony Optimization (ACO)-based binary classifier analysis to identify the most predictive chemical substructures. Decision trees revealed descriptors of lipophilicity (aLogP) and charge (polar surface area), which were also previously described in models of passive diffusion. However, measures of molecular geometry and connectivity were found to be related to an active drug transport component.

Activating Liver X receptors (LXRs) represents a promising therapeutic option for dyslipidemia. However, activating LXRα may cause undesired lipogenic effects. Discovery of highly LXRβ-selective agonists without LXRα activation were indispensable for dyslipidemia. In this study, in silico approaches were applied to develop highly potent LXRβ-selective agonists based on a series of newly reported 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione-based LXRα/β dual agonists. Initially, Kohonen and stepwise multiple linear regression SW-MLR were performed to construct models for LXRβ agonists and LXRα agonists based on the structural characteristics of LXRα/β dual agonists, respectively. The obtained LXRβ agonist model gave a good predictive ability (R2train = 0.837, R2test = 0.843, Q2LOO = 0.715), and the LXRα agonist model produced even better predictive ability (R2train = 0.968, R2test = 0.914, Q2LOO = 0.895). Also, the two QSAR models were independent and can well distinguish LXRβ and LXRα activity. Then, compounds in the ZINC database met the lower limit of structural similarity of 0.7, compared to the 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione scaffold subjected to our QSAR models, which resulted in the discovery of ZINC55084484 with an LXRβ prediction value of pEC50 equal to 7.343 and LXRα prediction value of pEC50 equal to −1.901. Consequently, nine newly designed compounds were proposed as highly LXRβ-selective agonists based on ZINC55084484 and molecular docking, of which LXRβ prediction values almost exceeded 8 and LXRα prediction values were below 0.

Ligand based virtual screening (LBVS) approaches could be broadly divided into those relying on chemical similarity searches and those employing Quantitative Structure-Activity Relationship (QSAR) models. We have compared the predictive power of these approaches using some datasets of compounds tested against several G-Protein Coupled Receptors (GPCRs). The k-Nearest Neighbors ( kNN) QSAR models were built for known ligands of each GPCR target independently, with a fraction of tested ligands for each target set aside as a validation set. The prediction accuracies of QSAR models for making active/inactive calls for compounds in both training and validation sets were compared to those achieved by the Prediction of Activity Spectra for Substances' (PASS) and the Similarity Ensemble Approach (SEA) tools both available online. Models developed with the kNN QSAR method showed the highest predictive power for almost all tested GPCR datasets. The PASS software, which incorporates multiple end-point specific QSAR models demonstrated a moderate predictive power, while SEA, a chemical similarity based approach, had the lowest prediction power. Our studies suggest that when sufficient amount of data is available to develop and rigorously validate QSAR models such models should be chosen as the preferred virtual screening tool in ligand-based computational drug discovery as compared to chemical similarity based approaches. [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]

Antioxidative peptides derived from food proteins have been screened and characterized in in vitro assays, and several quantitative structure-activity relationship (QSAR) models have been established to understand the structure-activity relationship of the selected antioxidant peptides. However, a systematic evaluation of each fragment that could be produced by a certain protein is still lacking. In this study, all the tripeptides that could possibly derive from bovine beta-lactoglobulin based on its amino acid sequence have been synthesized, and their antioxidant activities were measured using a ferric-reducing antioxidant power assay. Three of the peptides (i.e., LTC, CQC, and GTW) exhibited higher activities than that of glutathione. A QSAR model was also computed using multiple linear regression with divided physicochemical property scores descriptors, and the validity of the model was confirmed by cross-validation and Y-scrambling. The result of the QSAR modeling indicated that the electronic and hydrogen-bonding properties of the amino acids in the tripeptide sequences, as well as the steric properties of the amino acid residues at the C- and N-termini, played an important role in the antioxidant activities of the tripeptides. The antioxidant activities of the tripeptides were generally higher with Cys and Trp amino acid residues in the sequence. The structural characteristics we found may contribute to the further research of antioxidative mechanism. [ABSTRACT FROM AUTHOR]

The structure-acute-toxicity relationship of organic acids and their derivatives was studied. The effect of the structure of 88 organic compounds on the acute toxicity upon i.p. administration in mice was studied using 2D simplex representation of the molecular structure and partial least-squares methods. Satisfactory quantitative structure-activity relationship (QSAR) models were constructed and analyzed to reveal the structural fragments that enhance toxicity. [ABSTRACT FROM AUTHOR]

Quantitative structure-activity relationship (QSAR) models have gained popularity in the pharmaceutical industry due to their potential to substantially decrease drug development costs by reducing expensive laboratory and clinical tests. QSAR modeling consists of two fundamental steps, namely, descriptor discovery and model building. Descriptor discovery methods are either based on chemical domain knowledge or purely data-driven. The former, chemoinformatics-based, and the latter, substructures-based, methods for QSAR modeling, have been developed quite independently. As a consequence, evaluations involving both types of descriptor discovery method are rarely seen. In this study, a comparative analysis of chemoinformatics-based and substructure-based approaches is presented. Two chemoinformatics-based approaches; ECFI and SELMA, are compared to five approaches for substructure discovery; CP, graphSig, MFI, MoFa and SUBDUE, using 18 QSAR datasets. The empirical investigation shows that one of the chemo-informatics-based approaches, ECFI, results in significantly more accurate models compared to all other methods, when used on their own. Results from combining descriptor sets are also presented, showing that the addition of ECFI descriptors to any other descriptor set leads to improved predictive performance for that set, while the use of ECFI descriptors in many cases also can be improved by adding descriptors generated by the other methods. [ABSTRACT FROM AUTHOR]

Predictability and prediction reliability are of utmost important to characterize a good Quantitative structure-activity relationships (QSAR) model. However, validation methods are insufficient to guarantee the prediction reliability of QSAR models. Moreover, high dimensional samples also pose great challenge to traditional methods in terms of predictive power. Therefore, this study presents a predictive classifier (i.e., TreeEC) that can assess prediction reliability with high confidence, especially for facing high dimensional QSAR data. Two approaches for assessing prediction reliability are provided, i.e., applicability domain and prediction confidence. We demonstrate that the applicability domain has difficulty to guarantee the models' prediction reliability, where samples intensively close to the domain center are often poor predicted than those outside the domain. Instead, prediction confidence is more promising for assessing prediction reliability. Based on a large data set assessed by prediction confidence, external samples assessed with high confidence greater than 95 % can be reliably predicted with an accuracy of 94 %, in contrast to the average accuracy of 84 %. We also illustrate that TreeEC are less affected by high dimensionality than other popular methods according to 11 public data sets. A free version of TreeEC with a user-friendly interface can also be downloading from website . [ABSTRACT FROM AUTHOR]

Abstract: Exposure to chemicals occurs often as mixtures. Presented in this paper is information on alkoxyethanols and the impact they might have on human health in combination with some commonly found aliphatic and aromatic compounds. Our studies to evaluate the joint toxicity of these chemicals among themselves and in combination with other chemicals reveal a variety of possible outcomes depending on the exposure scenario. The interactions are predominantly based on metabolic pathways and are common among several solvents and organic compounds. Quantitative structure activity relationship (QSAR) analysis can be used with high confidence to identify chemicals that will interact to influence overall joint toxicity. Potential human exposure to a combination of alkoxyethanol, toluene and substituted benzenes may increase reproductive and developmental disease conditions. Inheritable gene alterations result in changes in the enzyme function in different subpopulations causing variations in quantity and/or quality of particular isoenzymes. These changes are responsible for differential metabolism of chemicals in species, genders, and life stages and are often the basis of a population’s susceptibility. Unique genotypes introduced as a function of migration can alter the genetic makeup of any given population. Hence special consideration should be given to susceptible populations while conducting chemical health risk assessments. [Copyright &y& Elsevier]

Predicting blood-brain barrier (BBB) permeability is essential to drug development, as a molecule cannot exhibit pharmacological activity within the brain parenchyma without first transiting this barrier. Understanding the process of permeation, however, is complicated by a combination of both limited passive diffusion and active transport. Our aim here was to establish predictive models for BBB drug permeation that include both active and passive transport. A database of 153 compounds was compiled using in vivo surface permeability product (logPS) values in rats as a quantitative parameter for BBB permeability. The open source Chemical Development Kit (CDK) was used to calculate physico-chemical properties and descriptors. Predictive computational models were implemented by machine learning paradigms (decision tree induction) on both descriptor sets. Models with a corrected classification rate (CCR) of 90% were established. Mechanistic insight into BBB transport was provided by an Ant Colony Optimization (ACO)-based binary classifier analysis to identify the most predictive chemical substructures. Decision trees revealed descriptors of lipophilicity (aLogP) and charge (polar surface area), which were also previously described in models of passive diffusion. However, measures of molecular geometry and connectivity were found to be related to an active drug transport component. [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]

Mandated efforts to assess chemicals for their potential to bioaccumulate within the environment are increasingly moving into the realm of data inadequacy. Consequently, there is an increasing reliance on predictive tools to complete regulatory requirements in a timely and cost-effective manner. The kinetic processes of absorption, distribution, metabolism, and elimination (ADME) determine the extent to which chemicals accumulate in fish and other biota. Current mathematical models of bioaccumulation implicitly or explicitly consider these ADME processes, but there is a lack of data needed to specify critical model input parameters. This is particularly true for compounds that are metabolized, exhibit restricted diffusion across biological membranes, or do not partition simply to tissue lipid. Here we discuss the potential of in vitro test systems to provide needed data for bioaccumulation modeling efforts. Recent studies demonstrate the utility of these systems and provide a ''proof of concept'' for the prediction models. Computational methods that predict ADME processes from an evaluation of chemical structure are also described. Most regulatory agencies perform bioaccumulation assessments using a weight-of-evidence approach. A strategy is presented for incorporating predictive methods into this approach. To implement this strategy it is important to understand the ''domain of applicability'' of both in vitro and structure-based approaches, and the context in which they are applied. [ABSTRACT FROM AUTHOR]

In this article, we present an automatic model generation process for building QSAR models using Gaussian Processes, a powerful machine learning modeling method. We describe the stages of the process that ensure models are built and validated within a rigorous framework: descriptor calculation, splitting data into training, validation and test sets, descriptor filtering, application of modeling techniques and selection of the best model. We apply this automatic process to data sets of blood–brain barrier penetration and aqueous solubility and compare the resulting automatically generated models with ‘manually’ built models using external test sets. The results demonstrate the effectiveness of the automatic model generation process for two types of data sets commonly encountered in building ADME QSAR models, a small set of in vivo data and a large set of physico-chemical data. [ABSTRACT FROM AUTHOR]

A combined approach of validated QSAR modeling and virtual screening was successfully applied to the discovery of novel tylophrine derivatives as anticancer agents. QSAR models have been initially developed for 52 chemically diverse phenanthrine-based tylophrine derivatives (PBTs) with known experimental EC50 using chemical topological descriptors (calculated with the MolConnZ program) and variable selection k nearest neighbor ( kNN) method. Several validation protocols have been applied to achieve robust QSAR models. The original dataset was divided into multiple training and test sets, and the models were considered acceptable only if the leave-one-out cross-validated R 2 ( q 2) values were greater than 0.5 for the training sets and the correlation coefficient R 2 values were greater than 0.6 for the test sets. Furthermore, the q 2 values for the actual dataset were shown to be significantly higher than those obtained for the same dataset with randomized target properties (Y-randomization test), indicating that models were statistically significant. Ten best models were then employed to mine a commercially available ChemDiv Database (ca. 500 K compounds) resulting in 34 consensus hits with moderate to high predicted activities. Ten structurally diverse hits were experimentally tested and eight were confirmed active with the highest experimental EC50 of 1.8 μM implying an exceptionally high hit rate (80%). The same ten models were further applied to predict EC50 for four new PBTs, and the correlation coefficient ( R 2) between the experimental and predicted EC50 for these compounds plus eight active consensus hits was shown to be as high as 0.57. Our studies suggest that the approach combining validated QSAR modeling and virtual screening could be successfully used as a general tool for the discovery of novel biologically active compounds. [ABSTRACT FROM AUTHOR]

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]

PGHS-1 and PGHS-2 are the targets of nonsteroidal anti- inflammatory drugs (NSAIDs). It appears that the high degree of selectivity for inhibition of PGHS-2 shown by certain compounds is the result of two mechanisms (time-dependent and time-independent inhibition), by which they interact with each isoform. The fenamic acids can be divided into competitive inhibitors of substrate binding and competitive inhibitors that cause time-dependent losses of cyclooxygenase activity. The cyclooxygenase activity was measured by oxygen consumption following preincubation of the enzyme and the inhibitor for increasing periods of time. The rate constants associated with binding inhibition kinetics and structure-activity relationships were calculated for a large number of fenamates, diclofenac and indomethacin. The KI* values are similar but the individual rate constants are markedly different: KI is two-fold lower, and k2 is six-fold slower for diclofenac than for indomethacin. All the active time-dependent compounds show MEPs with a negative conical surface, with their vertex on the minimum of the carboxyl group, which extends around the first aromatic ring to the central region. The conical surface keeps an open angle of 61° or larger, and a close contact surface with the residues Ala527, Ileu523, Val349, and Ser530, in the zones surrounding the bridging amino group and the chlorine atoms for meclofenamate and diclofenac, or in the region around the carbonyl group for indomethacin. The KI* and IC50 values indicate that the interactions that promote the slow binding kinetics must be examined in relation to the reaction energies of formation (ΔHr) of an ionic bond between the deprotonated carboxylic acid group of acid NSAIDs with the monocationic guanidinum group of Arg120, the free energies of solvation in aqueous solution, and the molecular volumes measured. Presumably indomethacin, diclofenac and meclofenamate cause the enzyme to undergo a subtle conformational change to a form that binds compounds even more tightly, with some slight structural changes confined to reorientations of the Arg277 and Gln358 side chains. These results show that the model has reliably chosen regions of biological significance consistent with both the X-ray crystallographic and kinetic results. [ABSTRACT FROM AUTHOR]

α-Glucosidase is considered a prime drug target for Diabetes Mellitus and its inhibitors are used to delay carbohydrate digestion for the treatment of diabetes mellitus. With the aim to design α-glucosidase inhibitors with novel chemical scaffolds, three folds ligand and structure based virtual screening was applied. Initially linear quantitative structure activity relationship (QSAR) model was developed by a molecular operating environment (MOE) using a training set of thirty-two known inhibitors, which showed good correlation coefficient (r2 = 0.88), low root mean square error (RMSE = 0.23), and cross-validated correlation coefficient r2 (q2 = 0.71 and RMSE = 0.31). The model was validated by predicting the biological activities of the test set which depicted r2 value of 0.82, indicating the robustness of the model. For virtual screening, compounds were retrieved from zinc is not commercial (ZINC) database and screened by molecular docking. The best docked compounds were chosen to assess their pharmacokinetic behavior. Later, the α-glucosidase inhibitory potential of the selected compounds was predicted by their mode of binding interactions. The predicted pharmacokinetic profile, docking scores and protein-ligand interactions revealed that eight compounds preferentially target the catalytic site of α-glucosidase thus exhibit potential α-glucosidase inhibition in silico. The α-glucosidase inhibitory activities of those Hits were predicted by QSAR model, which reflect good inhibitory activities of these compounds. These results serve as a guidelines for the rational drug design and development of potential novel anti-diabetic agents. [ABSTRACT FROM AUTHOR]

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]

The performance of quantitative structure–activity relationship (QSAR) models largely depends on the relevance of the selected molecular representation used as input data matrices. This work presents a thorough comparative analysis of two main categories of molecular representations (vector space and metric space) for fitting robust machine learning models in QSAR problems. For the assessment of these methods, seven different molecular representations that included RDKit descriptors, five different fingerprints types (MACCS, PubChem, FP2-based, Atom Pair, and ECFP4), and a graph matching approach (non-contiguous atom matching structure similarity; NAMS) in both vector space and metric space, were subjected to state-of-art machine learning methods that included different dimensionality reduction methods (feature selection and linear dimensionality reduction). Five distinct QSAR data sets were used for direct assessment and analysis. Results show that, in general, metric-space and vector-space representations are able to produce equivalent models, but there are significant differences between individual approaches. The NAMS-based similarity approach consistently outperformed most fingerprint representations in model quality, closely followed by Atom Pair fingerprints. To further verify these findings, the metric space-based models were fitted to the same data sets with the closest neighbors removed. These latter results further strengthened the above conclusions. The metric space graph-based approach appeared significantly superior to the other representations, albeit at a significant computational cost. [ABSTRACT FROM AUTHOR]

Activating Liver X receptors (LXRs) represents a promising therapeutic option for dyslipidemia. However, activating LXRα may cause undesired lipogenic effects. Discovery of highly LXRβ-selective agonists without LXRα activation were indispensable for dyslipidemia. In this study, in silico approaches were applied to develop highly potent LXRβ-selective agonists based on a series of newly reported 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione-based LXRα/β dual agonists. Initially, Kohonen and stepwise multiple linear regression SW-MLR were performed to construct models for LXRβ agonists and LXRα agonists based on the structural characteristics of LXRα/β dual agonists, respectively. The obtained LXRβ agonist model gave a good predictive ability (R2train = 0.837, R2test = 0.843, Q2LOO = 0.715), and the LXRα agonist model produced even better predictive ability (R2train = 0.968, R2test = 0.914, Q2LOO = 0.895). Also, the two QSAR models were independent and can well distinguish LXRβ and LXRα activity. Then, compounds in the ZINC database met the lower limit of structural similarity of 0.7, compared to the 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione scaffold subjected to our QSAR models, which resulted in the discovery of ZINC55084484 with an LXRβ prediction value of pEC50 equal to 7.343 and LXRα prediction value of pEC50 equal to −1.901. Consequently, nine newly designed compounds were proposed as highly LXRβ-selective agonists based on ZINC55084484 and molecular docking, of which LXRβ prediction values almost exceeded 8 and LXRα prediction values were below 0. [ABSTRACT FROM AUTHOR]