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The human soluble epoxide hydrolase (sEH) is a bifunctional enzyme that modulates the levels of regulatory epoxy lipids. The hydrolase activity is carried out by a catalytic triad located at the center of a wide L-shaped binding site, which contains two hydrophobic subpockets at both sides. On the basis of these structural features, it can be assumed that desolvation is a major factor in determining the maximal achievable affinity that can be attained for this pocket. Accordingly, hydrophobic descriptors may be better suited to the search of novel hits targeting this enzyme. This study examines the suitability of quantum mechanically derived hydrophobic descriptors in the discovery of novel sEH inhibitors. To this end, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophores were generated by combining electrostatic and steric or alternatively hydrophobic and hydrogen-bond parameters in conjunction with a tailored list of 76 known sEH inhibitors. The pharmacophore models were then validated by using two external sets chosen (i) to rank the potency of four distinct series of compounds and (ii) to discriminate actives from decoys, using in both cases datasets taken from the literature. Finally, a prospective study was performed including a virtual screening of two chemical libraries to identify new potential hits, which were subsequently experimentally tested for their inhibitory activity on human, rat, and mouse sEH. The use of hydrophobic-based descriptors led to the identification of six compounds as inhibitors of the human enzyme with IC50 < 20 nM, including two with IC50 values of 0.4 and 0.7 nM. The results support the use of hydrophobic descriptors as a valuable tool in the search of novel scaffolds that encode a proper hydrophilic/hydrophobic distribution complementary to the target's binding site.

Quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR) models predict biological activity and molecular property based on the numerical relationship between chemical structures and activity (property) values. Molecular representations are of importance in QSAR/QSPR analysis. Topological information of molecular structures is usually utilized (2D representations) for this purpose. However, conformational information seems important because molecules are in the three-dimensional space. As a three-dimensional molecular representation applicable to diverse compounds, similarity between a test molecule and a set of reference molecules has been previously proposed. This 3D representation was found to be effective on virtual screening for early enrichment of active compounds. In this study, we introduced the 3D representation into QSAR/QSPR modeling (regression tasks). Furthermore, we investigated relative merits of 3D representations over 2D in terms of the diversity of training data sets. For the prediction task of quantum mechanics-based properties, the 3D representations were superior to 2D. For predicting activity of small molecules against specific biological targets, no consistent trend was observed in the difference of performance using the two types of representations, irrespective of the diversity of training data sets., identifier:1573-4951

Background: Emergence of cross-resistance to current anti-malarial drugs has led to an urgent need for identification of potential compounds with novel modes of action and anti-malarial activity against the resistant strains. One of the most promising therapeutic targets of anti-malarial agents related to food vacuole of malaria parasite is haemozoin, a product formed by the parasite through haemoglobin degradation. Methods: With this in mind, this study developed two-dimensional-quantitative structure–activity relationships (QSAR) models of a series of 21 haemozoin inhibitors to explore the useful physicochemical parameters of the active compounds for estimation of anti-malarial activities. The 2D-QSAR model with good statistical quality using partial least square method was generated after removing the outliers. Results: Five two-dimensional descriptors of the training set were selected: atom count (a_ICM); adjacency and distance matrix descriptor (GCUT_SLOGP_2: the third GCUT descriptor using atomic contribution to logP); average total charge sum (h_pavgQ) in pKa prediction (pH = 7); a very low negative partial charge, including aromatic carbons which have a heteroatom-substitution in “ortho” position (PEOE_VSA-0) and molecular descriptor (rsynth: estimating the synthesizability of molecules as the fraction of heavy atoms that can be traced back to starting material fragments resulting from retrosynthetic rules), respectively. The model suggests that the anti-malarial activity of haemozoin inhibitors increases with molecules that have higher average total charge sum in pKa prediction (pH = 7). QSAR model also highlights that the descriptor using atomic contribution to logP or the distance matrix descriptor (GCUT_SLOGP_2), and structural component of the molecules, including topological descriptors does make for better anti-malarial activity. Conclusions: The model is capable of predicting the anti-malarial activities of anti-haemozoin compounds. In addition, the, Malaria Journal, 20, art. no. 264; 2021

Bromodomain testis (BRDT), a member of the bromodomain and extraterminal (BET) subfamily that includes the cancer targets BRD2, BRD3, and BRD4, is a validated contraceptive target. All BET subfamily members have two tandem bromodomains (BD1 and BD2). Knockout mice lacking BRDT-BD1 or both bromodomains are infertile. Treatment of mice with JQ1, a BET BD1/BD2 nonselective inhibitor with the highest affinity for BRD4, disrupts spermatogenesis and reduces sperm number and motility. To assess the contribution of each BRDT bromodomain, we screened our collection of DNA-encoded chemical libraries for BRDT-BD1 and BRDT-BD2 binders. High-enrichment hits were identified and resynthesized off-DNA and examined for their ability to compete with JQ1 in BRDT and BRD4 bromodomain AlphaScreen assays. These studies identified CDD-1102 as a selective BRDT-BD2 inhibitor with low nanomolar potency and >1,000-fold selectivity over BRDT-BD1. Structure-activity relationship studies of CDD-1102 produced a series of additional BRDT-BD2/BRD4-BD2 selective inhibitors, including CDD-1302, a truncated analog of CDD-1102 with similar activity, and CDD-1349, an analog with sixfold selectivity for BRDT-BD2 versus BRD4-BD2. BROMOscan bromodomain profiling confirmed the great affinity and selectivity of CDD-1102 and CDD-1302 on all BET BD2 versus BD1 with the highest affinity for BRDT-BD2. Cocrystals of BRDT-BD2 with CDD-1102 and CDD-1302 were determined at 2.27 and 1.90 Å resolution, respectively, and revealed BRDT-BD2 specific contacts that explain the high affinity and selectivity of these compounds. These BD2-specific compounds and their binding to BRDT-BD2 are unique compared with recent reports and enable further evaluation of their nonhormonal contraceptive potential in vitro and in vivo.

Isothiocyanates (ITCs) derived from Brassicaceae are potential food preservatives. Their antimicrobial ac-tivity is strongly influenced by their subclass and side chain. This is the first quantitative structure-activity relationships (QSAR) study of ITCs as antibacterials. Twenty-six ITCs covering 9 subclasses were tested against Escherichia coli and Bacillus cereus. Minimum inhibitory concentration (MIC) and growth inhibitory response (GIR) were determined and used to develop QSAR models. MIC of the most active ITCs was 6.3–9.4 μg/mL. The QSAR models were validated with leave-one-out cross validation. The proposed models had a good fit (R2adj 0.86–0.93) and high internal predictive power (Q2adj 0.80–0.89). Partial charge, polarity, reactivity, and shape of ITCs were key physicochemical properties underlying antibacterial activity of ITCs. Furthermore, ITC compositions and antibacterial activity of Sinapis alba, Brassica napus, B. juncea, B. oleracea, and Camelina sativa extracts were determined, after myrosinase treatment. B. oleracea ITC-rich extract showed promising activity (MIC 750–1000 μg/mL) against both bacteria. C. sativa ITC-rich extract showed promising activity (MIC 188 μg/mL) against B. cereus. The QSAR models successfully predicted activity of the extracts based on ITC compositions. The models are useful to predict antibacterial activity of new ITCs and ITC-rich mixtures. Brassicaceae ITC-rich extracts are promising natural food preservatives.

Bromodomain testis (BRDT), a member of the bromodomain and extraterminal (BET) subfamily that includes the cancer targets BRD2, BRD3, and BRD4, is a validated contraceptive target. All BET subfamily members have two tandem bromodomains (BD1 and BD2). Knockout mice lacking BRDT-BD1 or both bromodomains are infertile. Treatment of mice with JQ1, a BET BD1/BD2 nonselective inhibitor with the highest affinity for BRD4, disrupts spermatogenesis and reduces sperm number and motility. To assess the contribution of each BRDT bromodomain, we screened our collection of DNA-encoded chemical libraries for BRDT-BD1 and BRDT-BD2 binders. High-enrichment hits were identified and resynthesized off-DNA and examined for their ability to compete with JQ1 in BRDT and BRD4 bromodomain AlphaScreen assays. These studies identified CDD-1102 as a selective BRDT-BD2 inhibitor with low nanomolar potency and >1,000-fold selectivity over BRDT-BD1. Structure-activity relationship studies of CDD-1102 produced a series of additional BRDT-BD2/BRD4-BD2 selective inhibitors, including CDD-1302, a truncated analog of CDD-1102 with similar activity, and CDD-1349, an analog with sixfold selectivity for BRDT-BD2 versus BRD4-BD2. BROMOscan bromodomain profiling confirmed the great affinity and selectivity of CDD-1102 and CDD-1302 on all BET BD2 versus BD1 with the highest affinity for BRDT-BD2. Cocrystals of BRDT-BD2 with CDD-1102 and CDD-1302 were determined at 2.27 and 1.90 Å resolution, respectively, and revealed BRDT-BD2 specific contacts that explain the high affinity and selectivity of these compounds. These BD2-specific compounds and their binding to BRDT-BD2 are unique compared with recent reports and enable further evaluation of their nonhormonal contraceptive potential in vitro and in vivo.

There is a pressing need to improve the efficiency of drug development, and nowhere is that need more clear than in the case of neglected diseases like malaria. The peculiarities of pyrimidine metabolism in Plasmodium species make inhibition of dihydroorotate dehydrogenase (DHODH) an attractive target for antimalarial drug design. By applying a pair of complementary quantitative structure-activity relationships derived for inhibition of a truncated, soluble form of the enzyme from Plasmodium falciparum (s-PfDHODH) to data from a large-scale phenotypic screen against cultured parasites, we were able to identify a class of antimalarial leads that inhibit the enzyme and abolish parasite growth in blood culture. Novel analogs extending that class were designed and synthesized with a goal of improving potency as well as the general pharmacokinetic and toxicological profiles. Their synthesis also represented an opportunity to prospectively validate our in silico property predictions. The seven analogs synthesized exhibited physicochemical properties in good agreement with prediction, and five of them were more active against P. falciparum growing in blood culture than any of the compounds in the published lead series. The particular analogs prepared did not inhibit s-PfDHODH in vitro, but advanced biological assays indicated that other examples from the class did inhibit intact PfDHODH bound to the mitochondrial membrane. The new analogs, however, killed the parasites by acting through some other, unidentified mechanism 24-48 h before PfDHODH inhibition would be expected to do so.

Sukcinimidi su jedinjenja koja pokazuju višestruke farmakološke efekte uključujući i antiproliferativnu aktivnost, zahvaljujući prisustvu farmakofore sa dva hidrofobna regiona i dva regiona bogata elektronima. Savremeni dizajn lekova ima za cilj da se modifikacijama u strukturi (promena vrste, položaja i orijentacije supstituenata) i in silico računarskim metodama predvide i optimizuju farmakokinetske osobine i bezbednosni profil kandidata za lek. U ranoj fazi razvoja lekova se koriste postojeće baze podataka o molekulskim, farmakokinetskim i toksikološkim parametrima već ispitanih jedinjenja i pomoću matematičkih modela i algoritama predviđaju se osobine novih molekula, eliminišu se neodgovarajući kandidati i postiže se ušteda u vremenu i materijalnim sredstvima. Da se ispitaju fizičko-hemijske karakteristike 11 novosintetisanih metil-etil-N-aril-sukcinimida na osnovu strukture, primenom različitih softverskih paketa; da se na osnovu strukture odrede farmakokinetski i toksikološki parametri, primenom različitih softverskih paketa; da se ispita retenciono ponašanje, odnosno odrede retencione konstante za svako jedinjenje primenom visokoefikasne hromatografije na tankom sloju (HP-TLC) i ispita mogućnost primene retencionih konstanti kao mere lipofilnosti ispitivanih jedinjenja; da se ispita antiproliferativna aktivnost na odabranim kulturama ćelija karcinoma i na zdravim ćelijama fibroblasta pluća; da se analizom molekulskog dokinga ustanovi vezivanje za estrogene receptore. Ispitano je retenciono ponašanje 11 novosintetisanih derivata sukcinimida primenom visokoefikasne hromatografije na tankom sloju (HP-TLC) obrnute faze uz primenu dvokomponentne smeše vode i organskog rastvarača (metanola, acetonitrila ili acetona), sa odgovarajućim zapreminskim udelom organskog rastvarača kao mobilne faze. Iz razvijenih hromatograma su izračunate retencione konstante RM0 i S. Logaritam podeonog koeficijenta (logP) određen je in silico, korišćenjem različitih računarskih programa., Succinimides have exhibited various pharmaceutical effects including antiproliferative activity due to an important structural fragment (a pharmacophore) presented in form of two hydrophobic regions and two electron-rich centers. Current development of new drugs involves modifications in structure (type, position and orientation of substituents) and usage of in silico computational programs to predict and optimize pharmacokinetic and safety profile of drug candidates. In early phase of drug development, databases regarding the molecular, pharmacokinetic and toxicological parameters of already tested compounds are used, mathematical models and algorithms are applied for predicting the properties of new molecules and inadequate candidates are eliminated saving time and resources. Determination of physico-chemical properties of the analyzed methyl-ethyl-N-phenilsuccinimide derivatives by software packages; virtual pharmacokinetic and toxicology screening; investigation of retention behavior of the compounds by the reversed-phase HPTLC analysis and calculation of retention constants and their correlation with lipophilicity; in vitro evaluation of antiproliferative activity toward five carcinoma cell lines and normal fetal lung cell line; molecular behavior study on target estrogen receptors by molecular docking and correlation of antiproliferative activity toward ER+ breast carcinoma cell lines and in silico estrogen receptor affinity binding. Retention behavior of 11 newly synthesized succinimide derivatives was determined by reversed phase high performance thin layer chromatography (RP HPTLC) with the application of two-component mixtures water - organic solvent (methanol, acetonitrile or acetone) with adequate volume fractions of the organic modifier. After chromatographic development RM0 and S parameters were calculated. The logarithm of partition coefficient, logP for the analyzed compounds were calculated by different softwares. Physico-chemical properties, pharma

This thesis is an important milestone in understanding the transformation and degradation processes of polycyclic aromatic hydrocarbons (PAHs). PAHs are indicator organic pollutants in urban road dust and some of their transformed products are extremely hazardous. Analytical methods were developed and optimized to comprehensively measure PAHs and their transformed products. The optimized method and relevant statistical techniques were used to assess the ultraviolet photon driven processes of the pollutants. A novel risk assessment approach was developed to evaluate the carcinogenic health risk posed by the pollutants. The new knowledge gained is vital in addressing the challenges posed by these pollutants.

Sixteen significant physicochemical predictor variables for thirty PAHs and transformed PAH products (TPPs) were retrieved individually prior to collation from ChemSpider.com [1] whilst their corresponding toxicity equivalency factor (TEF) end-point was obtained from published articles by Bortey-Sam, Ikenaka [2] and Wei, Bandowe [3]. In order to achieve a 5:1 ratio of the number of observations to predictors which is vital for an effective quantitative structure-activity relationship (QSAR) modelling, factor analysis was used to reduce the data. Four fundamental predictors were obtained whilst the observations were found to cluster into two main groups of nitro-PAHs and other analytes. It is anticipated that the data presented here is highly relevant for future studies on the toxicity and health effects of the analytes in the environment. Secondly, the fate and distribution patterns of PAHs and TPPs are influenced by the parameters in the dataset. In this regard, studies on the behaviour patterns of these environmental pollutants require this information for a comprehensive evaluation and interpretation of results. Researchers across varied fields of environmental science and toxicology will find this dataset very useful. This data currently serves as supplementary information for the research article in the Journal of Hazardous Materials by Gbeddy, Egodawatta [4].

Structure-based design and synthesis of two biphenyl-N-acyl-β-d-glucopyranosylamine derivatives as well as their assessment as inhibitors of human liver glycogen phosphorylase (hlGPa, a pharmaceutical target for type 2 diabetes) is presented. X-ray crystallography revealed the importance of structural water molecules and that the inhibitory efficacy correlates with the degree of disturbance caused by the inhibitor binding to a loop crucial for the catalytic mechanism. The in silico-derived models of the binding mode generated during the design process corresponded very well with the crystallographic data.

Acridone based synthetic and natural products with inherent anticancer activity advancing the research and generating a large number of structurally diversified compounds. In this sequence we have designed, synthesized a series of tetracyclic acridones with amide framework viz., 3-(alkyloyl/ aryloyl/ heteroaryloyl/ heteroaryl)-2,3-dihydropyrazino[3,2,1-de]acridin-7(1H)-ones and screened for their in vitro anti-cancer activity. The in vitro study revealed that compounds with cyclopropyl-acetyl, benzoyl, p-hydroxybenzoyl, p-(trifluoromethyl)benzoyl, p-fluorobenzoyl, m-fluorobenzoyl, picolinoyl, 6-methylpicolinoyl and 3-nicotinoyl groups are active against HT29, MDAMB231 and HEK293T cancer cell lines. The molecular docking studies performed for them against 4N5Y, HT29 and 2VWD revealed the potential ligand–protein binding interactions among the neutral aminoacid of the enzymes and carbonyl groups of the title compounds with a binding energy ranging from − 8.1394 to − 6.9915 kcal/mol. In addition, the BSA protein binding assay performed for them has confirmed their interaction with target proteins through strong binding to BSA macromolecule. The additional studies like ADMET, QSAR, bioactivity scores, drug properties and toxicity risks ascertained them as newer drug candidates. This study had added a new collection of piperazino fused acridone derivatives to the existing array of other nitrogen heterocyclic fused acridone derivatives as anticancer agents. © 2020, The Author(s).

A series of 1-(2,3-dihydro-1H-indan-1-yl)-3-aryl urea/thiourea derivatives (4a-j) have been synthesized from the reaction of 2,3-dihydro-1H-inden-1-amine (2) with various aryl isocyanates/isothiocyanates (3a-j) by using N,N-DIPEA base (Hunig's base) catalyst in THF at reflux conditions. All of them are structurally confirmed by spectral (IR, 1H & 13C NMR and MASS) and elemental analysis and screened for their in-vitro antioxidant activity against DPPH and NO free radicals and found that compounds 4b, 4i, 4h & 4g are potential antioxidants. The obtained in vitro results were compared with the molecular docking, ADMET, QSAR and bioactivity study results performed for them and identified that the recorded in silico binding affinities were observed in good correlation with the in vitro antioxidant results. The Molecular docking analysis had unveiled the strong hydrogen bonding interactions of synthesized ligands with ARG 160 residue of protein tyrosine kinase (2HCK) enzyme and plays an effective role in its inhibition. Toxicology studies have assessed the potential risks of 4a-j and inferred that all of them were in the limits of potential drugs. The conformational analysis of 4a-j inferred that the urea/thiourea spacer linking 2,3-dihydro-1H-inden-1-amino and substituted aryl units has facilitated all these molecules to effectively bind with ARG 160 amino acid residue present on the α-helix of the protein tyrosine kinase (2HCK) enzyme specifically on chain A of hemopoetic cell kinase. Collectively this study has established a relationship between the antioxidant potentiality and ligands binding with ARG 160 amino acid residue of chain A of 2HCK enzyme to inhibit its growth as well as proliferation of reactive oxygen species in vivo. © 2019 Elsevier Inc.

Endocrine active contaminants (EACs) in environmental samples can pose a range of toxicological threats to ecosystems, especially through their impacts on reproductive pathways mediated by the estrogen receptor. The physicochemical properties of known organic EACs vary greatly and typically require different sample preparation techniques to identify different classes of compounds. EAC sources are similarly diverse, including both endogenous compounds and anthropogenic chemicals found in personal care products, pharmaceuticals, and their transformation products, which are often disposed of to sewers at their end of use. Looking for EACs in sewage sludge proposes a bottom-up, or end-of-use and treatment approach to discover environmentally relevant EACs, since many EACs accumulate in sludges even after application of robust wastewater treatment processes. This study demonstrates an extraction and analytical method capable of detecting a broad spectrum of known and suspected EACs via High Resolution Liquid Chromatography Quadropole Time-of-Flight Mass Spectrometry (LC-QTOF-MS) suspect screening of fourteen California sewage sludge samples. Spike-recovery experiments were performed using twelve carefully selected surrogates to assess different extraction solvents, sample weights, extraction pH values, procedures for combining extracts with different extraction pH's, and solid phase extraction cartridges. Using LC-QTOF-MS, identifications of several other organic compounds in the samples were made, a goal unachievable with unit resolution mass spectrometry. Suspect screening of California sludge samples discovered 118 compounds including hormones, pharmaceuticals, phosphate flame retardants, recreational drugs, antimicrobials, and pesticides. Additionally, 22 of these identified compounds are predicted to interfere with estrogen receptors or other reproductive/developmental pathways based on the VEGA QSAR toxicity prediction model.

The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure-activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database

Once applied, an herbicide first makes contact with leaves and soil. It is known that photolysis can be one of the most important processes of dissipation of herbicides in the field. However, degradation does not guarantee detoxification and can give rise to byproducts that could be more toxic and/or persistent than the active substance. In this work, the photodegradation of alloxydim herbicide in soil and leaf cuticle surrogates was studied and a detailed study on the phytotoxicity of the main byproduct on sugar beet, tomato, and rotational crops was performed. Quantitative structure–activity relationship (QSAR) models were used to obtain a first approximation of the possible ecotoxicological and environmental implications of the alloxydim and its degradation product. The results show that alloxydim is rapidly degraded on carnauba and sandy loam soil surfaces, two difficult matrices to analyze and not previously studied with alloxydim. Two transformation products that formed in both matrices were identified: alloxydim Z-isomer and imine derivative (mixture of two tautomers). The phytotoxicity of alloxydim and the major byproduct shows that tomato possesses high sensitivity to the imine byproduct, while wheat crops are inhibited by the parent compound. This paper demonstrates the need to further investigate the behavior of herbicide degradation products on target and nontarget species to determine the adequate use of herbicidal products to maximize productivity in the context of sustainable agriculture.

Whereas 400 million distinct compounds are now purchasable within the span of a few weeks, the biological activities of most are unknown. To facilitate access to new chemistry for biology, we have combined the Similarity Ensemble Approach (SEA) with the maximum Tanimoto similarity to the nearest bioactive to predict activity for every commercially available molecule in ZINC. This method, which we label SEA+TC, outperforms both SEA and a naïve-Bayesian classifier via predictive performance on a 5-fold cross-validation of ChEMBL's bioactivity data set (version 21). Using this method, predictions for over 40% of compounds (>160 million) have either high significance (pSEA ≥ 40), high similarity (ECFP4MaxTc ≥ 0.4), or both, for one or more of 1382 targets well described by ligands in the literature. Using a further 1347 less-well-described targets, we predict activities for an additional 11 million compounds. To gauge whether these predictions are sensible, we investigate 75 predictions for 50 drugs lacking a binding affinity annotation in ChEMBL. The 535 million predictions for over 171 million compounds at 2629 targets are linked to purchasing information and evidence to support each prediction and are freely available via https://zinc15.docking.org and https://files.docking.org .

Human immunodeficiency virus type 1 (HIV-1) is responsible for the majority of HIV infections worldwide, and we still lack a cure for this infection. Blocking the interaction of HIV-1 and its primary receptor CD4 is one strategy for identifying new anti-HIV-1 entry inhibitors. Here we report the discovery of a novel ligand that can inhibit HIV-1 entry and infection via CD4. Biological and computational analyses of this inhibitor and its analogs, using bioactivity evaluation, Rule of Five (RO5), comparative molecular field analysis (CoMFA)/comparative molecular similarity index analysis (CoMSIA) models, and three-dimensional quantitative structure-activity relationship (3D-QSAR), singled out compound 3 as a promising lead molecule for the further development of therapeutics targeting HIV-1 entry. Our study demonstrates an effective approach for employing structure-based, rational drug design techniques to identify novel antiviral compounds with interesting biological activities.

Biomolecular force field development has been instrumental in improving the predictive power of molecular simulations over the past four decades. More recently, the era of large quantitative experimental datasets and ubiquitous high performance computing power has enabled rapid progress in the field. In this review we summarize recent developments in all-atom protein, nucleic acid, and small molecule force fields, paying specific attention to developments in parameterization methods and improvements in the representations of nonbonded interactions that are critical for solving the challenging biophysical problems of the present. We also sketch out new avenues for force field development and grand challenge applications for the near future.

We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification.

Biomolecular force field development has been instrumental in improving the predictive power of molecular simulations over the past four decades. More recently, the era of large quantitative experimental datasets and ubiquitous high performance computing power has enabled rapid progress in the field. In this review we summarize recent developments in all-atom protein, nucleic acid, and small molecule force fields, paying specific attention to developments in parameterization methods and improvements in the representations of nonbonded interactions that are critical for solving the challenging biophysical problems of the present. We also sketch out new avenues for force field development and grand challenge applications for the near future.

Human immunodeficiency virus type 1 (HIV-1) is responsible for the majority of HIV infections worldwide, and we still lack a cure for this infection. Blocking the interaction of HIV-1 and its primary receptor CD4 is one strategy for identifying new anti-HIV-1 entry inhibitors. Here we report the discovery of a novel ligand that can inhibit HIV-1 entry and infection via CD4. Biological and computational analyses of this inhibitor and its analogs, using bioactivity evaluation, Rule of Five (RO5), comparative molecular field analysis (CoMFA)/comparative molecular similarity index analysis (CoMSIA) models, and three-dimensional quantitative structure-activity relationship (3D-QSAR), singled out compound 3 as a promising lead molecule for the further development of therapeutics targeting HIV-1 entry. Our study demonstrates an effective approach for employing structure-based, rational drug design techniques to identify novel antiviral compounds with interesting biological activities.

Whereas 400 million distinct compounds are now purchasable within the span of a few weeks, the biological activities of most are unknown. To facilitate access to new chemistry for biology, we have combined the Similarity Ensemble Approach (SEA) with the maximum Tanimoto similarity to the nearest bioactive to predict activity for every commercially available molecule in ZINC. This method, which we label SEA+TC, outperforms both SEA and a naïve-Bayesian classifier via predictive performance on a 5-fold cross-validation of ChEMBL's bioactivity data set (version 21). Using this method, predictions for over 40% of compounds (>160 million) have either high significance (pSEA ≥ 40), high similarity (ECFP4MaxTc ≥ 0.4), or both, for one or more of 1382 targets well described by ligands in the literature. Using a further 1347 less-well-described targets, we predict activities for an additional 11 million compounds. To gauge whether these predictions are sensible, we investigate 75 predictions for 50 drugs lacking a binding affinity annotation in ChEMBL. The 535 million predictions for over 171 million compounds at 2629 targets are linked to purchasing information and evidence to support each prediction and are freely available via https://zinc15.docking.org and https://files.docking.org .

We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification.

Polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) are omnipresent and persistent environmental pollutants. In particular, 29 congeners are of special concern, and these are usually referred to as dioxin-like compounds (DLCs). In the European Union, the risks associated with DLCs in food products are estimated by a weighted sum of the DLCs’ concentrations. These weights, also called toxic equivalency factors (TEFs), compare the DLCs’ potencies to the most toxic congener, 2,3,7,8-tetrachloro-dibenzo-p-dioxin (2378- TCDD). The toxicological effects of PCDD/Fs and PCBs are diverse, ranging from chloracne and immunological effects in humans to severe weight loss, thymic atrophy, hepatotoxicity, immunotoxicity, endocrine disruption, and carcinogenesis in rodents. Here, the molecular structures of DLCs were used as the basis to study the congeneric differences in in vitro data from both human and rodent cell responses related to the aryl hydrocarbon receptor (AhR). Based on molecular orbital densities and partial charges, we developed new ways to describe DLCs, which proved to be useful in quantitative structure-activity relationship modeling. This thesis also provides a new approach, the calculation of the consensus toxicity factor (CTF), to condense information from a battery of screening tests. The current TEFs used to estimate the risk of DLCs in food are primarily based on in vivo information from rat and mouse experiments. Our CTFs, based on human cell responses, show clear differences compared to the current TEFs. For instance, the CTF of 23478-PeCDF is as high as the CTF for 2378-TCDD, and the CTF of PCB 126 is 30 times lower than the corresponding TEF. Both of these DLCs are common congeners in fish in the Baltic Sea. Due to the severe effects of DLCs and their impact on environmental and human health, it is crucial to determine if other compounds have similar effects. To find such compounds, we developed a virtual scre

Inhibition of plasmin has been found to effectively reduce fibrinolysis and to avoid hemorrhage. This can be achieved by addressing its kringle 1 domain with the known drug and lysine analogue tranexamic acid. Guided by shape similarities toward a previously discovered lead compound, 5-(4-piperidyl)isoxazol-3-ol, a set of 16 structurally similar compounds was assembled and investigated. Successfully, in vitro measurements revealed one compound, 5-(4-piperidyl)isothiazol-3-ol, superior in potency compared to the initial lead. Furthermore, a strikingly high correlation (R(2) = 0.93) between anti-fibrinolytic activity and kringle 1 binding affinity provided strong support for the hypothesized inhibition mechanism, as well as revealing opportunities to fine-tune biological effects through minor structural modifications. Several different ligand-based (Freeform, shape, and electrostatic-based similarities) and structure-based methods (e.g., Posit, MM/GBSA, FEP+) were used to retrospectively predict the binding affinities. A combined method, molecular alignment using Posit and scoring with Tcombo, lead to the highest coefficient of determination (R(2) = 0.6).

Polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) are omnipresent and persistent environmental pollutants. In particular, 29 congeners are of special concern, and these are usually referred to as dioxin-like compounds (DLCs). In the European Union, the risks associated with DLCs in food products are estimated by a weighted sum of the DLCs’ concentrations. These weights, also called toxic equivalency factors (TEFs), compare the DLCs’ potencies to the most toxic congener, 2,3,7,8-tetrachloro-dibenzo-p-dioxin (2378- TCDD). The toxicological effects of PCDD/Fs and PCBs are diverse, ranging from chloracne and immunological effects in humans to severe weight loss, thymic atrophy, hepatotoxicity, immunotoxicity, endocrine disruption, and carcinogenesis in rodents. Here, the molecular structures of DLCs were used as the basis to study the congeneric differences in in vitro data from both human and rodent cell responses related to the aryl hydrocarbon receptor (AhR). Based on molecular orbital densities and partial charges, we developed new ways to describe DLCs, which proved to be useful in quantitative structure-activity relationship modeling. This thesis also provides a new approach, the calculation of the consensus toxicity factor (CTF), to condense information from a battery of screening tests. The current TEFs used to estimate the risk of DLCs in food are primarily based on in vivo information from rat and mouse experiments. Our CTFs, based on human cell responses, show clear differences compared to the current TEFs. For instance, the CTF of 23478-PeCDF is as high as the CTF for 2378-TCDD, and the CTF of PCB 126 is 30 times lower than the corresponding TEF. Both of these DLCs are common congeners in fish in the Baltic Sea. Due to the severe effects of DLCs and their impact on environmental and human health, it is crucial to determine if other compounds have similar effects. To find such compounds, we developed a virtual scre

Polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) are omnipresent and persistent environmental pollutants. In particular, 29 congeners are of special concern, and these are usually referred to as dioxin-like compounds (DLCs). In the European Union, the risks associated with DLCs in food products are estimated by a weighted sum of the DLCs’ concentrations. These weights, also called toxic equivalency factors (TEFs), compare the DLCs’ potencies to the most toxic congener, 2,3,7,8-tetrachloro-dibenzo-p-dioxin (2378- TCDD). The toxicological effects of PCDD/Fs and PCBs are diverse, ranging from chloracne and immunological effects in humans to severe weight loss, thymic atrophy, hepatotoxicity, immunotoxicity, endocrine disruption, and carcinogenesis in rodents. Here, the molecular structures of DLCs were used as the basis to study the congeneric differences in in vitro data from both human and rodent cell responses related to the aryl hydrocarbon receptor (AhR). Based on molecular orbital densities and partial charges, we developed new ways to describe DLCs, which proved to be useful in quantitative structure-activity relationship modeling. This thesis also provides a new approach, the calculation of the consensus toxicity factor (CTF), to condense information from a battery of screening tests. The current TEFs used to estimate the risk of DLCs in food are primarily based on in vivo information from rat and mouse experiments. Our CTFs, based on human cell responses, show clear differences compared to the current TEFs. For instance, the CTF of 23478-PeCDF is as high as the CTF for 2378-TCDD, and the CTF of PCB 126 is 30 times lower than the corresponding TEF. Both of these DLCs are common congeners in fish in the Baltic Sea. Due to the severe effects of DLCs and their impact on environmental and human health, it is crucial to determine if other compounds have similar effects. To find such compounds, we developed a virtual scre

Polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) are omnipresent and persistent environmental pollutants. In particular, 29 congeners are of special concern, and these are usually referred to as dioxin-like compounds (DLCs). In the European Union, the risks associated with DLCs in food products are estimated by a weighted sum of the DLCs’ concentrations. These weights, also called toxic equivalency factors (TEFs), compare the DLCs’ potencies to the most toxic congener, 2,3,7,8-tetrachloro-dibenzo-p-dioxin (2378- TCDD). The toxicological effects of PCDD/Fs and PCBs are diverse, ranging from chloracne and immunological effects in humans to severe weight loss, thymic atrophy, hepatotoxicity, immunotoxicity, endocrine disruption, and carcinogenesis in rodents. Here, the molecular structures of DLCs were used as the basis to study the congeneric differences in in vitro data from both human and rodent cell responses related to the aryl hydrocarbon receptor (AhR). Based on molecular orbital densities and partial charges, we developed new ways to describe DLCs, which proved to be useful in quantitative structure-activity relationship modeling. This thesis also provides a new approach, the calculation of the consensus toxicity factor (CTF), to condense information from a battery of screening tests. The current TEFs used to estimate the risk of DLCs in food are primarily based on in vivo information from rat and mouse experiments. Our CTFs, based on human cell responses, show clear differences compared to the current TEFs. For instance, the CTF of 23478-PeCDF is as high as the CTF for 2378-TCDD, and the CTF of PCB 126 is 30 times lower than the corresponding TEF. Both of these DLCs are common congeners in fish in the Baltic Sea. Due to the severe effects of DLCs and their impact on environmental and human health, it is crucial to determine if other compounds have similar effects. To find such compounds, we developed a virtual scre

Polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) are omnipresent and persistent environmental pollutants. In particular, 29 congeners are of special concern, and these are usually referred to as dioxin-like compounds (DLCs). In the European Union, the risks associated with DLCs in food products are estimated by a weighted sum of the DLCs’ concentrations. These weights, also called toxic equivalency factors (TEFs), compare the DLCs’ potencies to the most toxic congener, 2,3,7,8-tetrachloro-dibenzo-p-dioxin (2378- TCDD). The toxicological effects of PCDD/Fs and PCBs are diverse, ranging from chloracne and immunological effects in humans to severe weight loss, thymic atrophy, hepatotoxicity, immunotoxicity, endocrine disruption, and carcinogenesis in rodents. Here, the molecular structures of DLCs were used as the basis to study the congeneric differences in in vitro data from both human and rodent cell responses related to the aryl hydrocarbon receptor (AhR). Based on molecular orbital densities and partial charges, we developed new ways to describe DLCs, which proved to be useful in quantitative structure-activity relationship modeling. This thesis also provides a new approach, the calculation of the consensus toxicity factor (CTF), to condense information from a battery of screening tests. The current TEFs used to estimate the risk of DLCs in food are primarily based on in vivo information from rat and mouse experiments. Our CTFs, based on human cell responses, show clear differences compared to the current TEFs. For instance, the CTF of 23478-PeCDF is as high as the CTF for 2378-TCDD, and the CTF of PCB 126 is 30 times lower than the corresponding TEF. Both of these DLCs are common congeners in fish in the Baltic Sea. Due to the severe effects of DLCs and their impact on environmental and human health, it is crucial to determine if other compounds have similar effects. To find such compounds, we developed a virtual scre

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (1) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (2) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.

Publisher: FedOA Press (Federico II Open Access University Press).  Pages: 143.  Language: Italian. NBN: http://nbn.depositolegale.it/urn:nbn:it:unina-22038 Abstract: A Gender Budget is a strategic tool that provides an assessment of the political-economic and financial decisions of public administration from a gender-sensitive perspective. Its main goal is to support a more efficient, transparent, and equitable, management of resources. The first Gender Budget of the Università degli Studi di Napoli “Federico II” (UNINA) was realized within the framework of the European-Union funded GENOVATE research project: Transforming Organizational Culture for Gender Equality in Research and Innovation. It consists in a thorough context analysis of the University enrolment and career policy outcomes, encompassing students, academic, technical and administrative staff, which results in a detailed map of the still relevant gender gap dimension in the institution. Raw data and their focused analysis show how cultural and structural barriers contribute to women dropping out of university at the very beginning of scientific careers (i.e. the so-called ‘leaky pipeline’ phenomenon). Due to horizontal and vertical segregation mechanisms, some disciplinary fields and academic positions still remain dominated by one gender. It is the authors’ hope that Gender Budgeting may become a well-established practice at UNINA, and may support transformative actions and inspire gender-mainstreaming institutional policies. GENOVATE is a project funded by the European Commission within the Seventh Framework Programme (FP7), under the Science in Society grant programme (SiS 2012 2.1.1-1). This volume was co-funded by the European Union., Editore: FedOA Press (Federico II Open Access University Press).  Pagine: 143.  Lingua: Italiano. NBN: http://nbn.depositolegale.it/urn:nbn:it:unina-22038 Abstract: Il Bilancio di Genere è un documento che analizza e valuta le scelte politiche e gli impegni economico-finanziari di una amministrazione in un’ottica di genere, con l’obiettivo di perseguire una gestione delle risorse efficiente, trasparente, equa e consapevole. La produzione del primo documento di Bilancio di Genere dell’Ateneo Federico II è stata realizzata nell’ambito del progetto europeo GENOVATE: Transforming Organizational Culture for Gender Equality in Research and Innovation. Il documento contiene un’ampia analisi di contesto che attraversa la tripla anima dell’Ateneo: studenti, personale docente e personale tecnico amministrativo, restituendo una fotografia della composizione degli organici in cui il divario di genere non appare ancora superato. I dati raccolti ed elaborati evidenziano e confermano l’esistenza di ostacoli culturali e strutturali che determinano, da un lato, l’abbandono della carriera scientifica da parte delle donne, dall’altro il persistere di meccanismi di segregazione orizzontale e verticale, con alcune aree e posizioni riservate esclusivamente a un genere. L’auspicio è che il Bilancio di Genere possa diventare prassi consolidata dell’Ateneo e assolvere alla propria funzione di strumento trasformativo nell’ottica del gender mainstreaming. GENOVATE è un progetto finanziato dalla Commissione Europea nell'ambito del Settimo Programma Quadro (FP7), bando Science in Society (SiS 2012 2.1.1-1). Il volume è stato realizzato con il cofinanziamento dell'Unione Europea.

Alzheimer's disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential Ifor cognition arid memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained frorn ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive rnodels were constructed from 100 different data splits using random forest. Generated models afforded R-2, Q(cv)(2) and Q(Ext)(2) values in ranges of 0.66-0.93, 0.55-0.79 and 0.56-0.81 for the training set, 10-fold cross-validated set and lexternal set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R-2, Q(CV)(2) and Q(Ext)(2) values of 0.92 +/- 0.01, 0.78 +/- 0.06 and 018 +/- 0.05, respectively. Furthermore, IT-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover Kennard Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed

Background: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. oBjectives: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. Methods: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. results: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. conclusion: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end

Alzheimer's disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential Ifor cognition arid memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained frorn ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive rnodels were constructed from 100 different data splits using random forest. Generated models afforded R-2, Q(cv)(2) and Q(Ext)(2) values in ranges of 0.66-0.93, 0.55-0.79 and 0.56-0.81 for the training set, 10-fold cross-validated set and lexternal set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R-2, Q(CV)(2) and Q(Ext)(2) values of 0.92 +/- 0.01, 0.78 +/- 0.06 and 018 +/- 0.05, respectively. Furthermore, IT-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover Kennard Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed

Alzheimer's disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential Ifor cognition arid memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained frorn ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive rnodels were constructed from 100 different data splits using random forest. Generated models afforded R-2, Q(cv)(2) and Q(Ext)(2) values in ranges of 0.66-0.93, 0.55-0.79 and 0.56-0.81 for the training set, 10-fold cross-validated set and lexternal set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R-2, Q(CV)(2) and Q(Ext)(2) values of 0.92 +/- 0.01, 0.78 +/- 0.06 and 018 +/- 0.05, respectively. Furthermore, IT-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover Kennard Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed

Alzheimer's disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential Ifor cognition arid memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained frorn ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive rnodels were constructed from 100 different data splits using random forest. Generated models afforded R-2, Q(cv)(2) and Q(Ext)(2) values in ranges of 0.66-0.93, 0.55-0.79 and 0.56-0.81 for the training set, 10-fold cross-validated set and lexternal set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R-2, Q(CV)(2) and Q(Ext)(2) values of 0.92 +/- 0.01, 0.78 +/- 0.06 and 018 +/- 0.05, respectively. Furthermore, IT-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover Kennard Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed

Alzheimer's disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential Ifor cognition arid memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained frorn ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive rnodels were constructed from 100 different data splits using random forest. Generated models afforded R-2, Q(cv)(2) and Q(Ext)(2) values in ranges of 0.66-0.93, 0.55-0.79 and 0.56-0.81 for the training set, 10-fold cross-validated set and lexternal set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R-2, Q(CV)(2) and Q(Ext)(2) values of 0.92 +/- 0.01, 0.78 +/- 0.06 and 018 +/- 0.05, respectively. Furthermore, IT-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover Kennard Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed

Publisher: FedOA Press (Federico II Open Access University Press).  Pages: 143.  Language: Italian. NBN: http://nbn.depositolegale.it/urn:nbn:it:unina-22038 Abstract: A Gender Budget is a strategic tool that provides an assessment of the political-economic and financial decisions of public administration from a gender-sensitive perspective. Its main goal is to support a more efficient, transparent, and equitable, management of resources. The first Gender Budget of the Università degli Studi di Napoli “Federico II” (UNINA) was realized within the framework of the European-Union funded GENOVATE research project: Transforming Organizational Culture for Gender Equality in Research and Innovation. It consists in a thorough context analysis of the University enrolment and career policy outcomes, encompassing students, academic, technical and administrative staff, which results in a detailed map of the still relevant gender gap dimension in the institution. Raw data and their focused analysis show how cultural and structural barriers contribute to women dropping out of university at the very beginning of scientific careers (i.e. the so-called ‘leaky pipeline’ phenomenon). Due to horizontal and vertical segregation mechanisms, some disciplinary fields and academic positions still remain dominated by one gender. It is the authors’ hope that Gender Budgeting may become a well-established practice at UNINA, and may support transformative actions and inspire gender-mainstreaming institutional policies. GENOVATE is a project funded by the European Commission within the Seventh Framework Programme (FP7), under the Science in Society grant programme (SiS 2012 2.1.1-1). This volume was co-funded by the European Union., Editore: FedOA Press (Federico II Open Access University Press).  Pagine: 143.  Lingua: Italiano. NBN: http://nbn.depositolegale.it/urn:nbn:it:unina-22038 Abstract: Il Bilancio di Genere è un documento che analizza e valuta le scelte politiche e gli impegni economico-finanziari di una amministrazione in un’ottica di genere, con l’obiettivo di perseguire una gestione delle risorse efficiente, trasparente, equa e consapevole. La produzione del primo documento di Bilancio di Genere dell’Ateneo Federico II è stata realizzata nell’ambito del progetto europeo GENOVATE: Transforming Organizational Culture for Gender Equality in Research and Innovation. Il documento contiene un’ampia analisi di contesto che attraversa la tripla anima dell’Ateneo: studenti, personale docente e personale tecnico amministrativo, restituendo una fotografia della composizione degli organici in cui il divario di genere non appare ancora superato. I dati raccolti ed elaborati evidenziano e confermano l’esistenza di ostacoli culturali e strutturali che determinano, da un lato, l’abbandono della carriera scientifica da parte delle donne, dall’altro il persistere di meccanismi di segregazione orizzontale e verticale, con alcune aree e posizioni riservate esclusivamente a un genere. L’auspicio è che il Bilancio di Genere possa diventare prassi consolidata dell’Ateneo e assolvere alla propria funzione di strumento trasformativo nell’ottica del gender mainstreaming. GENOVATE è un progetto finanziato dalla Commissione Europea nell'ambito del Settimo Programma Quadro (FP7), bando Science in Society (SiS 2012 2.1.1-1). Il volume è stato realizzato con il cofinanziamento dell'Unione Europea.

[Abstract] Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.

In drug discovery, it is generally accepted that neighboring molecules in a given descriptor's space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as "activity cliffs". In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming cocrystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods.

The acid dissociation constant is an important molecular property, and it can be successfully predicted by Quantitative Structure-Property Relationship (QSPR) models, even for in silico designed molecules. We analyzed how the methodology of in silico 3D structure preparation influences the quality of QSPR models. Specifically, we evaluated and compared QSPR models based on six different 3D structure sources (DTP NCI, Pubchem, Balloon, Frog2, OpenBabel, and RDKit) combined with four different types of optimization. These analyses were performed for three classes of molecules (phenols, carboxylic acids, anilines), and the QSPR model descriptors were quantum mechanical (QM) and empirical partial atomic charges. Specifically, we developed 516 QSPR models and afterward systematically analyzed the influence of the 3D structure source and other factors on their quality. Our results confirmed that QSPR models based on partial atomic charges are able to predict pKa with high accuracy. We also confirmed that ab initio and semiempirical QM charges provide very accurate QSPR models and using empirical charges based on electronegativity equalization is also acceptable, as well as advantageous, because their calculation is very fast. On the other hand, Gasteiger-Marsili empirical charges are not applicable for pKa prediction. We later found that QSPR models for some classes of molecules (carboxylic acids) are less accurate. In this context, we compared the influence of different 3D structure sources. We found that an appropriate selection of 3D structure source and optimization method is essential for the successful QSPR modeling of pKa. Specifically, the 3D structures from the DTP NCI and Pubchem databases performed the best, as they provided very accurate QSPR models for all the tested molecular classes and charge calculation approaches, and they do not require optimization. Also, Frog2 performed very well. Other 3D structure sources can also be used but are not so robust, and

Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

N-(2-Oxo-3-oxetanyl)carbamic acid esters have recently been reported to be noncompetitive inhibitors of the N-acylethanolamine acid amidase (NAAA) potentially useful for the treatment of pain and inflammation. In the present study, we further explored the structure-activity relationships of the carbamic acid ester side chain of 2-methyl-4-oxo-3-oxetanylcarbamic acid ester derivatives. Additional favorable features in the design of potent NAAA inhibitors have been found together with the identification of a single digit nanomolar inhibitor. In addition, we devised a 3D QSAR using the atomic property field method. The model turned out to be able to account for the structural variability and was prospectively validated by designing, synthesizing, and testing novel inhibitors. The fairly good agreement between predictions and experimental potency values points to this 3D QSAR model as the first example of quantitative structure-activity relationships in the field of NAAA inhibitors.

Comparative toxicity potentials (CTPs) quantify the potential ecotoxicological impacts of chemicals per unit of emission. They are the product of a substance's environmental fate, exposure, and hazardous concentration. When empirical data are lacking, substance properties can be predicted. The goal of the present study was to assess the influence of predictive uncertainty in substance property predictions on the CTPs of triazoles. Physicochemical and toxic properties were predicted with quantitative structure-activity relationships (QSARs), and uncertainty in the predictions was quantified with use of the data underlying the QSARs. Degradation half-lives were based on a probability distribution representing experimental half-lives of triazoles. Uncertainty related to the species' sample size that was present in the prediction of the hazardous aquatic concentration was also included. All parameter uncertainties were treated as probability distributions, and propagated by Monte Carlo simulations. The 90% confidence interval of the CTPs typically spanned nearly 4 orders of magnitude. The CTP uncertainty was mainly determined by uncertainty in soil sorption and soil degradation rates, together with the small number of species sampled. In contrast, uncertainty in species-specific toxicity predictions contributed relatively little. The findings imply that the reliability of CTP predictions for the chemicals studied can be improved particularly by including experimental data for soil sorption and soil degradation, and by developing toxicity QSARs for more species. (c) 2013 SETAC

N-(2-Oxo-3-oxetanyl)carbamic acid esters have recently been reported to be noncompetitive inhibitors of the N-acylethanolamine acid amidase (NAAA) potentially useful for the treatment of pain and inflammation. In the present study, we further explored the structure-activity relationships of the carbamic acid ester side chain of 2-methyl-4-oxo-3-oxetanylcarbamic acid ester derivatives. Additional favorable features in the design of potent NAAA inhibitors have been found together with the identification of a single digit nanomolar inhibitor. In addition, we devised a 3D QSAR using the atomic property field method. The model turned out to be able to account for the structural variability and was prospectively validated by designing, synthesizing, and testing novel inhibitors. The fairly good agreement between predictions and experimental potency values points to this 3D QSAR model as the first example of quantitative structure-activity relationships in the field of NAAA inhibitors.