Number Of Hydrogen Bond Acceptors Research Articles

Modeling the Blood-Brain Barrier Permeability of Potential Heterocyclic Drugs via Biomimetic IAM Chromatography Technique Combined with QSAR Methodology.

Penetration through the blood-brain barrier (BBB) is desirable in the case of potential pharmaceuticals acting on the central nervous system (CNS), but is undesirable in the case of drug candidates acting on the peripheral nervous system because it may cause CNS side effects. Therefore, modeling of the permeability across the blood-brain barrier (i.e., the logarithm of the brain to blood concentration ratio, log BB) of potential pharmaceuticals should be performed as early as possible in the preclinical phase of drug development. Biomimetic chromatography with immobilized artificial membrane (IAM) and the quantitative structure-activity relationship (QSAR) methodology were successful in modeling the blood-brain barrier permeability of 126 drug candidates, whose experimentally-derived lipophilicity indices and computationally-derived molecular descriptors (such as molecular weight (MW), number of rotatable bonds (NRB), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), topological polar surface area (TPSA), and polarizability (α)) varied by class. The QSARs model established by multiple linear regression showed a positive effect of the lipophilicity (log kw, IAM) and molecular weight of the compound, and a negative effect of the number of hydrogen bond donors and acceptors, on the log BB values. The model has been cross-validated, and all statistics indicate that it is very good and has high predictive ability. The simplicity of the developed model, and its usefulness in screening studies of novel drug candidates that are able to cross the BBB by passive diffusion, are emphasized.

Synthesis, photophysical properties, anticancer evaluation, and molecular docking studies of new pyrimidine linked 4-arylidene-thiazolidin-4-ones as potent anticancer agents.

The reaction of 4-(chloroacetamido)pyrimidine (1) with ammonium thiocyanate gave 2-(pyrimidin-4-ylimino)thiazolidin-4-one (2), which, when condensed with four substituted benzaldehyde analogues, gave the consequent 5-arylidine-2-(pyrimidin-4-ylimino)thiazolidin-4-ones 3a-d. In addition, the absorbance and fluorescence behaviours of pyrimidinylimino-thiazolidin-4-one hybrids 3a-d in various organic solvents were investigated. The emphasis was on studying UV absorption capacities and the effect of various structural components on photophysical qualities such as the 5-arylidene-2-(pyrimidin-4-ylimino)thiazolidin-4-ones and N,N-dimethylamino tail. The cytotoxic effect of four pyrimidinylimino-thiazolidin-4-one hybrids 3a-d on tumour cell lines (HepG2, HCT-116, PC3, MCF-7) and a normal cell line (WI38) is investigated in this work. The cytotoxicity was measured by comparing the half-maximal inhibitory concentration (IC50 ) to the reference medication, 5-fluorouracil. The findings indicate that these hybrid compounds had varying cytotoxic effects on the cell lines examined; hybrids 3b and 3c demonstrated significant anticancer activity against MCF-7 with IC50 values of 7.53 ±0.43 and 9.17 ±0.31 μM, respectively. The inhibitory efficacy of various synthesized hybrids on the epidermal growth factor receptor (EGFR) kinase was investigated. EGFR is a crucial target in cancer treatment because inhibiting it may reduce tumour development and proliferation. The IC50 value was used to calculate the inhibitory activity, which is the concentration of inhibitor necessary to induce half-maximal inhibition of EGFR kinase activity. In addition, the predicted ADME results show that pyrimidinylimino-thiazolidin-4-one hybrids have good pharmacokinetic properties; hybrid 3d is more lipophilic than the other compounds. It has a medium molecular weight, a small number of hydrogen bond acceptors and donors, and a large number of aromatic heavy atoms. Moreover, molecular docking simulations revealed precise information on the interactions of pyrimidinylimino-thiazolidin-4-one hybrids 3a-d and 5-Fu with their respective protein targets. These interactions point to possible pathways for their biological activities and call for more testing to establish their effectiveness as bioactive molecules or therapeutic candidates.

High-performance biochar-loaded MgAl-layered double oxide adsorbents derived from sewage sludge towards nanoplastics removal: Mechanism elucidation and QSAR modeling

Utilization of sewage sludge for the fabrication of environmental functional materials is highly desirable to achieve pollution mitigation and resource recovery. In the present work, we introduced a novel MgAl-layered double oxide (LDO)@biochar composite adsorbent in-situ fabricated from Al-rich sewage sludge, and its excellent application in nanoplastics adsorption. Initially, fifteen model contaminants with varied conjugate structures, hydrogen bonding and ionic properties were selected for an investigation of adsorption behavior and adsorption selectivity on LDO@biochar. Structural variation of LDO@biochar suggested reconstruction of the layered double hydroxide (LDH) during the adsorption process due to the “memory effect”. Under the synergy of LDH and biochar, the contaminants were adsorbed via multiple adsorbent-adsorbate interactions, including anion exchange, electrostatic interaction, hydrogen bonding and π-π conjugation. Then, a quantitative structure-activity relationship (QSAR) model was constructed by integrating the number of hydrogen bond acceptors, polarity surface area, number of aromatic rings, and Fukui index f(−)x together to reflect the affinity of each contaminant to the adsorbent. Guided by the QSAR model, the negatively charged polystyrene nanoplastics with continuously conjugated aromatic rings were predicted to be effectively adsorbed on LDO@biochar. Experimental tests confirmed a great capacity of LDO@biochar towards the polystyrene nanoplastics, given the equilibrium adsorption capacity as high as 360 mg g−1 at 30–50 °C. This work not only opened up a new avenue for sustainable utilization of sewage sludge towards high-performance environmental functional materials, but also demonstrated the potential of the QSAR analysis as a rapid and accurate approach for guiding the application of an adsorbent to new emerging containments.

Machine learning assisted high-throughput computational screening of MOFs for the capture of chemical warfare agents from the air

To effectively capture the low-concentration chemical warfare agents (CWAs) and their simulants which are extremely harmful to human health and environment, the properties of thousands of Computation-Ready, Experimental Metal-Organic Frameworks (CoRE-MOFs) for the adsorption and separation of four CWAs and simulants (dimethyl methyl phosphonate, soman, mustard gas, and 2-chloroethyl ethyl sulfide) from the air were calculated by high-throughput computational screening. To reasonably identify the top-performing MOFs, the trade-off between selectivity and adsorption capacity (TSN) was introduced to measure the properties of MOFs. Five machine learning algorithms were employed to quantitatively evaluate the structure-performance relationships of MOFs for the adsorption of CWAs and validate that Extreme Gradient Boosting algorithms had the best prediction accuracy. Furthermore, four MOF descriptors (henry coefficient, number of hydrogen bonds, porosity, and volumetric surface area) were found to have significant influence on the properties of MOFs. Finally, it was determined that the number of hydrogen bond acceptors was a key factor governing the co-adsorption of CWAs and their simulants, and the similarities of adsorbents with good adsorption performance included Zn for metal center, trimesic acid for organic linker, and srs for topology. The microscopic insights obtained from our bottom-up approach are very helpful for the development of MOFs and other nanoporous materials for the capture of CWAs from the air.

Molecular Descriptors and QSAR Models for Sedative Activity of Sesquiterpenes Administered to Mice via Inhalation.

Essential oils are often utilized for therapeutic purposes and are composed of complex structural molecules, including sesquiterpenes, with high molecular weight and potential for stereochemistry. A detailed study on the properties of selected sesquiterpenes was conducted as part of a broader investigation on the effects of sesquiterpenes on the central nervous system. A set of 18 sesquiterpenes, rigorously selected from an original list of 114, was divided into 2 groups i.e., the training and test sets, with each containing 9 compounds. The training set was evaluated for the sedative activity in mice through inhalation, and all compounds were sedatives at any dose in the range of 4 × 10-4-4 × 10-2 mg/cage, except for curzerene. Molecular determinants of the sedative activities of sesquiterpenes were evaluated using quantitative structure-activity relationship (QSAR) and structure-activity relationship (SAR) analyses. An additional test set of six compounds obtained from the literature was utilized for validating the QSAR model. The parental carbonyl cation and an oxygen-containing groups are possible determinants of sedative activity. The QSAR study using multiple regression models could reasonably predict the sedative activity of sesquiterpenes with statistical parameters such as the correlation coefficient r2 = 0.82 > 0.6 and q2 LOO = 0.71 > 0.5 obtained using the leave-one-out cross-validation technique. Molar refractivity and the number of hydrogen bond acceptors were statistically important in predicting the activities. The present study could help predict the sedative activity of additional sesquiterpenes, thus accelerating the process of drug development.

Insights into Chemical Structure-Based Modeling for New Sweetener Discovery.

The search for novel, natural, high-sweetness, low-calorie sweeteners remains open and challenging. In the present study, the structure-based machine learning modeling and sweetness recognition mechanism were investigated to assist this process. It was found that whether or not a compound was sweet was closely related to molecular connectivity and composition (the number of hydrogen bond acceptors and donors), tpsaEfficiency, structural complexity, and shape (nAtomP and Fsp3). While the relative sweetness of sweet compounds was more determined by the molecular properties (tpsaEfficiency and Log P), structural complexity and composition (nAtomP and ATSm 1). The built machine learning models exhibited very good performance for classifying the sweet/non-sweet compounds and predicting the relative sweetness of the compounds. Moreover, a specific binding pocket was found for sweet compounds, and the sweet compounds mainly interacted with the VFT domain of the T1R2-T1R3 through hydrogen bonds. In addition, the results indicated that among the sweet compounds, those that were sweeter bound to the VFT domain stronger than those that had low sweetness. This study provides very useful information for developing new sweeteners.

Targeting Transcriptional CDKs 7, 8, and 9 with Anilinopyrimidine Derivatives as Anticancer Agents: Design, Synthesis, Biological Evaluation and In Silico Studies.

Cyclin-dependent kinases (CDKs) are promising targets in chemotherapy. In this study, we report a series of 2-anilinopyrimidine derivatives with CDK inhibitory activity. Twenty-one compounds were synthesized and their CDK inhibitory and cytotoxic activities were evaluated. The representative compounds demonstrate potent antiproliferative activities toward different solid cancer cell lines and provide a promising strategy for the treatment of malignant tumors. Compound 5f was the most potent CDK7 inhibitor (IC50 = 0.479 µM), compound 5d was the most potent CDK8 inhibitor (IC50 = 0.716 µM), and compound 5b was the most potent CDK9 inhibitor (IC50 = 0.059 µM). All the compounds satisfied the Lipinski's rule of five (molecular weight < 500 Da, number of hydrogen bond acceptors <10, and octanol-water partition coefficient and hydrogen bond donor values below 5). Compound 5j is a good candidate for lead optimization because it has a non-hydrogen atom (N) of 23, an acceptable ligand efficiency value of 0.38673, and an acceptable ligand lipophilic efficiency value of 5.5526. The synthesized anilinopyrimidine derivatives have potential as anticancer agents.

Cheminformatic Analysis and Machine Learning Modeling to Investigate Androgen Receptor Antagonists to Combat Prostate Cancer.

Prostate cancer (PCa) is a major leading cause of mortality of cancer among males. There have been numerous studies to develop antagonists against androgen receptor (AR), a crucial therapeutic target for PCa. This study is a systematic cheminformatic analysis and machine learning modeling to study the chemical space, scaffolds, structure-activity relationship, and landscape of human AR antagonists. There are 1678 molecules as final data sets. Chemical space visualization by physicochemical property visualization has demonstrated that molecules from the potent/active class generally have a mildly smaller molecular weight (MW), octanol-water partition coefficient (log P), number of hydrogen-bond acceptors (nHA), number of rotatable bonds (nRot), and topological polar surface area (TPSA) than molecules from intermediate/inactive class. The chemical space visualization in the principal component analysis (PCA) plot shows significant overlapping distributions between potent/active class molecules and intermediate/inactive class molecules; potent/active class molecules are intensively distributed, while intermediate/inactive class molecules are widely and sparsely distributed. Murcko scaffold analysis has shown low scaffold diversity in general, and scaffold diversity of potent/active class molecules is even lower than intermediate/inactive class molecules, indicating the necessity for developing molecules with novel scaffolds. Furthermore, scaffold visualization has identified 16 representative Murcko scaffolds. Among them, scaffolds 1, 2, 3, 4, 7, 8, 10, 11, 15, and 16 are highly favorable scaffolds due to their high scaffold enrichment factor values. Based on scaffold analysis, their local structure-activity relationships (SARs) were investigated and summarized. In addition, the global SAR landscape was explored by quantitative structure-activity relationship (QSAR) modelings and structure-activity landscape visualization. A QSAR classification model incorporating all of the 1678 molecules stands out as the best model from a total of 12 candidate models for AR antagonists (built on PubChem fingerprint, extra trees algorithm, accuracy for training set: 0.935, 10-fold cross-validation set: 0.735 and test set: 0.756). Deeper insights into the structure-activity landscape highlighted a total of seven significant activity cliff (AC) generators (ChEMBL molecule IDs: 160257, 418198, 4082265, 348918, 390728, 4080698, and 6530), which provide valuable SAR information for medicinal chemistry. The findings in this study provide new insights and guidelines for hit identification and lead optimization for the development of novel AR antagonists.

Evaluating the feasibility of SMILES-based autoencoders for drug discovery

The vast majority of molecules with desirable drug-like properties have not yet been discovered. With the advent of machine learning for de novo molecular generation, the process of designing these molecules has become increasingly efficient. However, to what extent are these machine learning models actually learning chemical properties versus memorizing the syntax of a training set? In this project, we trained a Simplified Molecular Input Line Entry System (SMILES)-based generative autoencoder for up to 200 epochs to investigate whether the latent space can separate molecules based on five chemical properties (partition coefficient, molecular weight, topological polar surface area, number of hydrogen bond donors, and number of hydrogen bond acceptors) and how generated molecules compare to the training set. We hypothesized that the model would preferentially encode molecular weight and that generated molecules would be similar to the training set. Consistent with our hypothesis, the model quickly learned to distinguish molecules primarily by their molecular weight, while other properties were considered to a lesser extent. Moreover, generated molecules were very similar to the training set both in terms of structure and properties. These results suggested that the model overfits the training set. In particular, the model best learns chemical properties that directly depend on atomic composition while it is difficult for the model to encode higher-level properties that rely on connectivity and structure. Our results may represent fundamental limitations of SMILES-based generative models and could assist in development of new research to mitigate these issues.

Combined Micellar Liquid Chromatography Technique and QSARs Modeling in Predicting the Blood-Brain Barrier Permeation of Heterocyclic Drug-like Compounds.

The quantitative structure-activity relationship (QSAR) methodology was used to predict the blood-brain permeability (log BB) for 65 synthetic heterocyclic compounds tested as promising drug candidates. The compounds were characterized by different descriptors: lipophilicity, parachor, polarizability, molecular weight, number of hydrogen bond acceptors, number of rotatable bonds, and polar surface area. Lipophilic properties of the compounds were evaluated experimentally by micellar liquid chromatography (MLC). In the experiments, sodium dodecyl sulfate (SDS) as the effluent component and the ODS-2 column were used. Using multiple linear regression and leave-one-out cross-validation, we derived the statistically significant and highly predictive quantitative structure-activity relationship models. Thus, this study provides valuable information on the expected properties of the substances that can be used as a support tool in the design of new therapeutic agents.

Machine Learning Modeling and Insights into the Structural Characteristics of Drug-Induced Neurotoxicity.

Neurotoxicity can be resulted from many diverse clinical drugs, which has been a cause of concern to human populations across the world. The detection of drug-induced neurotoxicity (DINeurot) potential with biological experimental methods always required a lot of budget and time. In addition, few studies have addressed the structural characteristics of neurotoxic chemicals. In this study, we focused on the computational modeling for drug-induced neurotoxicity with machine learning methods and the insights into the structural characteristics of neurotoxic chemicals. Based on the clinical drug data with neurotoxicity effects, we developed 35 different classifiers by combining five different machine learning methods and seven fingerprint packages. The best-performing model achieved good results on both 5-fold cross-validation (balanced accuracy of 76.51%, AUC value of 0.83, and MCC value of 0.52) and external validation (balanced accuracy of 83.63%, AUC value of 0.87, and MCC value of 0.67). The model can be freely accessed on the web server DINeuroTpredictor (http://dineurot.sapredictor.cn/). We also analyzed the distribution of several key molecular properties between neurotoxic and non-neurotoxic structures. The results indicated that several physicochemical properties were significantly different between the neurotoxic and non-neurotoxic compounds, including molecular polar surface area (MPSA), AlogP, the number of hydrogen bond acceptors (nHAcc) and donors (nHDon), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). In addition, 18 structural alerts responsible for chemical neurotoxicity were identified. The structural alerts have been integrated with our web server SApredictor (http://www.sapredictor.cn). The results of this study could provide useful information for the understanding of the structural characteristics and computational prediction for chemical neurotoxicity.

A quantitative structural analysis of AR-42 derivatives as HDAC1 inhibitors for the identification of promising structural contributors

ABSTRACT Alteration and abnormal epigenetic mechanisms can lead to the aberration of normal biological functions and the occurrence of several diseases. The histone deacetylase (HDAC) family of enzymes is one of the prime regulators of epigenetic functions modifying the histone proteins, and thus, regulating epigenetics directly. HDAC1 is one of those HDACs which have important contributions to cellular epigenetics. The abnormality of HDAC is correlated to the occurrence, progression, and poor prognosis in several disease conditions namely neurodegenerative disorders, cancer cell proliferation, metastasis, chemotherapy resistance, and survival in various cancers. Therefore, the progress of potent and effective HDAC1 inhibitors is one of the prime approaches to combat such diseases. In this study, both regression and classification-based molecular modelling studies were conducted on some AR-42 derivatives as HDAC1 inhibitors to elucidate the crucial structural aspects that are responsible for regulating their biological responses. This study revealed that the molecular polarizability, van der Waals volume, the presence of aromatic rings as well as the higher number of hydrogen bond acceptors might affect prominently their inhibitory activity and might be responsible for proper fitting and interactions at the HDAC1 active site to pertain effective inhibition.

Quantitative Retention (Structure)-Activity Relationships in Predicting the Pharmaceutical and Toxic Properties of Potential Pesticides.

The micellar liquid chromatography technique and quantitative retention (structure)–activity relationships method were used to predict properties of carbamic and phenoxyacetic acids derivatives, newly synthesized in our laboratory and considered as potential pesticides. Important properties of the test substances characterizing their potential significance as pesticides as well as threats to humans were considered: the volume of distribution, the unbonded fractions, the blood–brain distribution, the rate of skin and cell permeation, the dermal absorption, the binding to human serum albumin, partitioning between water and plants’ cuticles, and the lethal dose. Pharmacokinetic and toxicity parameters were predicted as functions of the solutes’ lipophilicities and the number of hydrogen bond donors, the number of hydrogen bond acceptors, and the number of rotatable bonds. The equations that were derived were evaluated statistically and cross-validated. Important features of the molecular structure influencing the properties of the tested substances were indicated. The QSAR models that were developed had high predictive ability and high reliability in modeling the properties of the molecules that were tested. The investigations highlighted the applicability of combined chromatographic technique and QS(R)ARs in modeling the important properties of potential pesticides and reducing unethical animal testing.

New C2- and N3-Modified Thieno[2,3-d]Pyrimidine Conjugates with Cytotoxicity in the Nanomolar Range.

The aim of the current study was to develop and explore a series of new cytotoxic agents based on the conjugation between the thieno[2,3-d]pyrimidine moiety and a second pharmacophore at the C2 or N3 position. As the thieno[2,3-d]pyrimidine core is a bioisostere of the 4-anilinoquinazoline, various new thienopyrimidine derivatives were synthesized by modifying the structure of the clinically used anticancer quinazoline EGFR inhibitors of the first generation - gefitinib, and second-generation - dacomitinib and canertinib. It was reported that some thieno[2,3-d]pyrimidine derivatives showed improved EGFR inhibitory activity. On the other hand, the benzimidazole heterocycle is present as a pharmacophore unit in the structure of many clinically used chemotherapeutic agents. Some 2-aminobenzimidazole derivatives, possessing anticancer activity, demonstrated EGFR inhibition and the benzimidazole derivative EGF816 is currently in the second phase of clinical trials. The objectives of the study were the design of a novel series thieno[2,3-d]pyrimidines, synthesis of the compounds and investigation of their effects towards human cancer HT-29, MDA-MB-231, HeLa, HepG2 and to normal human Lep3 cell lines. (American Type Culture Collection, ATCC, Rockville, MD, USA). The synthetic protocol implemented cyclocondensation of 2-amino-thiophenes and nitriles in an inert medium, aza- Michael addition to benzimidazole derivatives and nucleophylic substitution at the N3 place. MTS test was used in order to establish the cytotoxicity of the tested compounds. SAR analysis and in silico assessment of the inhibitory potential towards human oncogenic V599EB-Raf were performed using Molinspiration tool and Molecular Operating environment software. The MTS test data showed that almost all studied thieno[2,3-d]pyirimidines (9-13, 21-22 and 25) manifest high inhibitory effect on cell proliferation at nanomolar concentrations, whereas compounds 9 (IC50 = 130 nM) and 10 (IC50 = 261 nM) containing amino acid moiety, and 21 (IC50 = 108 nM) possessing two thienopyrimidine moieties attached to a 1,3-disubstituted benzimidazole linker, revealed many times lower toxicity against Lep3 cells compared to the cancer cells. Thienopyrimidines 11-13 possessed high selectivity against HeLa cells. Compound 13 showed high inhibitory activity against MDA-MB-231 and HepG2, with IC50 1.44 nM and 1.11 nM respectively. To outline the possible biological target of the studied coumpounds, their potential to interact with human oncogenic V599EB-Raf was explored by a docking study. As a result, it was suggested that the benzimidazolyl and glycyl fragments could enhance the binding ability of the new compounds by increasing the number of hydrogen bond acceptors and by stabilizing the inactive form of the enzyme. The thienopyrimidines tested in vitro for human cancer HT-29, MDA-MB-231, HeLa, HepG2 and normal human Lep3 cell lines demonstrated cytotoxicity in the nanomolar range. It was established that compounds 9, 10 and 21 showed many times lower toxicity against normal Lep3 cells that can provide a high selectivity towards all four cancer cell lines at small concentrations. Based on the analysis of the structure-activity relationship, the observed trends in the cytotoxicity could be related to the lipophilicity and the topological polar surface area of the tested compounds. The docking study on the potential of the new thieno[2,3-d]pyrimidine-4-ones to interact with mutant V599EB-Raf showed that the compounds might be able to stabilize the enzyme in its inactive form.

Synthesis, characterization and evaluation of thiopyrimidine derivatives as possible antimicrobial agents

A series of new thiopyrimidine derivatives have been synthesized via the reaction of Chalcones 3a-c with thiourea to give the corresponding pyrimidine thiones 4a-c. S-alkylation of pyrimidine thiones have resulted in novel 4,6-diaryl-2-alkyl thiopyrimidine 5a-i derivatives. Molecular properties like number of hydrogen bond acceptors, number of hydrogen bond donors, volume, polar surface area, molar refractivity, number of rotatable bonds and drug likeness for synthesized compounds have been predicted by using different softwares such as Molinspiration, Molsoft and Chemsketch. The newly synthesized 4,6-diaryl-2-alkyl thiopyrimidine derivatives 5a-i have been evaluated for their possible anti-microbial activity. Compounds 5b, 5d and 5e have revealed significant activity against E. coli, P. aerugenosa (Gram +ve) and B. subtilis, S. aureus (Gram −ve) species while compounds 5a, 5c, 5f-i are moderately active as compared to the standard drug Ciprofloxacin. Compounds 5c and 5g show potent anti-fungal activity against Penicillium species amongst the series in comparison to the standard Fluconazole.

Alcohol exacerbated biochemical and biophysical alterations in liver mitochondrial membrane of diabetic male wistar rats – A possible amelioration by green tea

Alcohol exacerbated biochemical and biophysical alterations in liver mitochondrial membrane of diabetic male wistar rats – A possible amelioration by green tea

In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity.

Drug induced nephrotoxicity is a major clinical challenge, and it is always associated with higher costs for the pharmaceutical industry and due to detection during the late stages of drug development. It is desirable for improving the health outcomes for patients to distinguish nephrotoxic structures at an early stage of drug development. In this study, we focused on in silico prediction and insights into the structural basis of drug induced nephrotoxicity, based on reliable data on human nephrotoxicity. We collected 565 diverse chemical structures, including 287 nephrotoxic drugs on humans in the real world, and 278 non-nephrotoxic approved drugs. Several different machine learning and deep learning algorithms were employed for in silico model building. Then, a consensus model was developed based on three best individual models (RFR_QNPR, XGBOOST_QNPR, and CNF). The consensus model performed much better than individual models on internal validation and it achieved prediction accuracy of 86.24% external validation. The results of analysis of molecular properties differences between nephrotoxic and non-nephrotoxic structures indicated that several key molecular properties differ significantly, including molecular weight (MW), molecular polar surface area (MPSA), AlogP, number of hydrogen bond acceptors (nHBA), molecular solubility (LogS), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). These molecular properties may be able to play an important part in the identification of nephrotoxic chemicals. Finally, 87 structural alerts for chemical nephrotoxicity were mined with f-score and positive rate analysis of substructures from Klekota-Roth fingerprint (KRFP). These structural alerts can well identify nephrotoxic drug structures in the data set. The in silico models and the structural alerts could be freely accessed via https://ochem.eu/article/140251 and http://www.sapredictor.cn, respectively. We hope the results should provide useful tools for early nephrotoxicity estimation in drug development.

Synthesis, DFT Study and Bioactivity Evaluation of New Butanoic Acid Derivatives as Antiviral Agents

In the present work, at first, density functional theory (DFT) calculations were utilized for the molecular design of the four new butanoic acid derivatives at B3LYP/6-31+G(d) level of theory. After DFT calculations, synthesis, FT-IR, 1H NMR, and 13C NMR spectra of corresponding molecules were presented. The NBO analysis and electronic properties of the four new synthesized butanoic acid (1, 2, 3, 4) were carried out to compare their stability and reactivity. Finally, the values of octanol/water partition coefficient (miLogP), the molecular polar surface area (TPSA), the number of atoms of the molecule (natoms), the number of hydrogen bond acceptors (nON), the number of hydrogen bond donors (nOHNH), the number of violations of the Ro5 (nviolations), the number of rotatable bonds (nrotb), the molecular volume (Vm), the molecular weight (MW) and bioactivity scores were estimated and discussed.

Accelerating Selective Oxidation of Biomass-Based Hydroxyl Compounds with Hydrogen Bond Acceptors.

Hydrogen-bonding-initiated self-association makes the valorization of biomass-based hydroxyl compounds a formidable challenge at high concentration. Apart from enhancing the dehydration reaction of hydroxyl compounds with the noncovalent medium effects, insights into how these effects can be exploited to optimize the oxidative reactivity of concentrated hydroxyl compounds remain unclear. Herein, we elucidate that deaggregation of hydroxyl groups with a catalytic number of hydrogen bond acceptors is essential in improving the reactivity of the aerobic oxidation of biomass-based neat aromatic alcohols over the vanadium-based catalyst. The neat 5-hydroxymethylfurfural (HMF) deaggregated with 25 mol % N,N-dimethylformamide (DMF) shows a >7-fold increase in reactivity to produce corresponding aldehydes with excellent selectivity, in stark contrast to the contrary deactivation of reaction in excessive DMF.

Modeling of CO2 absorption capabilities of amino acid solutions using a computational scheme

AbstractIn this communication, modeling of carbon dioxide absorption by various amino acid solutions is presented as a function of operational parameters using the Least‐Squares Support Vector Machine (LSSVM) algorithm integrated with three different evolutionary algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Hybrid GA and PSO (HGAPSO). A databank containing 255 data of carbon dioxide absorption by amino acids of potassium taurate, potassium glycinate, potassium prolinate, and potassium lysinate at different temperatures, partial pressures, and concentrations was prepared from different sources to train and test the proposed algorithms. The models were applied to estimate carbon dioxide absorption by amino acid solutions in terms of temperature, molar concentration, equilibrium partial pressure, molecular weight, number of hydrogen bond donor, number of hydrogen bond acceptor, and number of rotatable bond. The R2 values of LSSVM optimized by HGAPSO, PSO, and GA are 0.9944, 0.9915, and 0.9891, respectively, and the various errors were determined close to zero. On the other hand, the visual comparison of models outputs and actual carbon dioxide adsorption data was used to clarify performances of the models. By comparison analysis, it was found that the LSSVM‐HGAPSO is the most accurate model for estimation of carbon dioxide loading. Also, comparison of our proposed models results with previously reported artificial neural network results indicates the impressive estimation capability of LSSVM algorithm. According to sensitivity analysis, it becomes obvious that pressure is the most effective parameter on carbon dioxide absorption.