A New Set of Principal Properties for Heteroaromatics Obtained by GRID

Retention Index Prediction Using Quantitative Structure-Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics

Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure-retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was carried out on the 34 resulting groups to eliminate false positives. Partial least squares (PLS) regression combined with a Genetic algorithm (GA) was applied to construct the linear QSRR models based on a variety of VolSurf+ molecular descriptors. A novel dual-filtering approach, which combines Tanimoto similarity (TS) searching as the primary filter and retention index (RI) similarity clustering as the secondary filter, was utilized to select compounds in training sets to derive the QSRR models yielding R² of 0.8512 and an average root mean square error in prediction (RMSEP) of 8.45%. With a retention index filter expressed as ±2 standard deviations (SD) of the error, representative compounds were predicted with >91% accuracy, and for 53% of the groups (18/34), at least one false positive compound could be eliminated. The proposed strategy can thus narrow down the number of false positives to be assessed in nontargeted metabolomics.

Cyto- and enzyme toxicities of ionic liquids modelled on the basis of VolSurf+ descriptors and their principal properties

Five in silico principal properties (PPs) for 218 heterocyclic cations and four PPs for 38 organic and inorganic anionic counterparts of ionic liquids (ILs) were derived by the VolSurf+ approach. VolSurf+ physicochemical descriptors take into account several cationic structural features of ILs such as heterocyclic aromatic and non-aromatic cationic cores, alkyl chain length, presence of oxygen atoms in the substituents as well as the properties of a wide variety of inorganic and organic anions. Combination of these cation and anion PPs can provide descriptors for over 8000 ILs, thus allowing the development of QSPR models for IL cytotoxicity (IPC-81 rat cell line) and enzyme toxicity (acetylcholinesterase inhibition). The adoption of a Partial Least Squares approach, relating PPs and toxicities, provided affordable predictions for ILs in both learning and external validation sets, implying the possibility to extend the predictive model to a set of 520 ILs. This allows us to establish priorities in selecting ILs for experimental hazard assessment as required by the REACH regulation.

A multivariate insight into ionic liquids toxicities

A multivariate insight into the toxicities of ionic liquids provides a comprehensive picture and guidelines for the evaluation of their eco- and bio-sustainability.

In silico log P prediction for a large data set with support vector machines, radial basis neural networks and multiple linear regression

Oil/water partition coefficient (log P) is one of the key points for lead compound to be drug. In silico log P models based solely on chemical structures have become an important part of modern drug discovery. Here, we report support vector machines, radial basis function neural networks, and multiple linear regression methods to investigate the correlation between partition coefficient and physico-chemical descriptors for a large data set of compounds. The correlation coefficient r(2) between experimental and predicted log P for training and test sets by support vector machines, radial basis function neural networks, and multiple linear regression is 0.92, 0.90, and 0.88, respectively. The results show that non-linear support vector machines derives statistical models that have better prediction ability than those of radial basis function neural networks and multiple linear regression methods. This indicates that support vector machines can be used as an alternative modeling tool for quantitative structure-property/activity relationships studies.

Computational study of the binding mode of epidermal growth factor receptor kinase inhibitors

Epidermal growth factor receptor kinase is relative to the progression of various types of cancers. In order to design anticancer drug, docking and support vector machines were used to guide CoMFA and CoMSIA for constructing optimal 3D-QSAR model. Additional descriptors, log P and HOMO, combined with several fields of CoMFA and CoMSIA, were introduced to construct models for the inhibitor of epidermal growth factor receptor kinase. The results show that the inclusion of log P and HOMO energy is meaningful for 3D-QSAR model. The validation of these models was testified by some structurally diverse compounds, which were not included in the CoMFA and CoMSIA models. The docking study and molecular dynamic simulation permit us to insight into the binding mode between ligand and EGFR kinase, and provide important information for structure-based drug design. The proposed approach can also be extended to other QSAR investigations.

Computational Studies and Drug Design for HIV-1 Reverse Transcriptase Inhibitors of 3′,4′-di-O-(S)-camphanoyl-(+)-cis-Khellactone (DCK) Analogs

Molecular docking and molecular dynamics simulation were applied to study the binding mode of 3',4'-di-O-(S)-camphanoyl-(+)-cis-khellactone (DCK) analogs anti-HIV inhibitors with HIV-1 RT. The results suggest that there is a strong hydrogen bond between DCK O16 and NH of Lys101, and that DCK analogues might act similarly as other types of HIV-1 RT inhibitors. The investigation about drug resistance for DCK shows no remarkable influence on the most frequently observed mutation K103N of HIV-1 RT. Based on the proposed mechanism, some new structures were designed and predicted by a SVM model. All compounds exhibited potent inhibitory activities against HIV replication in H9 lymphocytes with EC50 values lower than 1.95 microM. The rationality of the method was validated by experimental results.

Chemical space navigation in lead discovery

The number of new chemical entities has remained rather constant (averaging 37 per year) in the past decade, despite the multiple-fold increase in the number of compounds that are being made and tested. Chemical space requires novel methods that can handle the increasing number of potentially accessible molecules. Neighborhood behavior, as an approach to similarity, and chemical property space navigation are some of the recent advances that are discussed, in the context of lead discovery and appropriate pharmacokinetic properties.

Chemography: the art of navigating in chemical space

Combinatorial chemistry needs focused molecular diversity applied to the druglike chemical space (drugspace). A drugspace map can be obtained by systematically applying the same conventions when examining the chemical space, in a manner similar to the Mercator convention in geography: Rules are equivalent to dimensions (e.g., longitude and latitude), while structures are equivalent to objects (e.g., cities and countries). Selected rules include size, lipophilicity, polarizability, charge, flexibility, rigidity, and hydrogen bond capacity. For these, extreme values were set, e.g., maximum molecular weight 1500, calculated negative logarithm of the octanol/water partition between -10 and 20, and up to 30 nonterminal rotatable bonds. Only S, N, O, P, and halogens were considered as elements besides C and H. Selected objects include a set of "satellite" structures and a set of representative drugs ("core" structures). Satellites, intentionally placed outside drugspace, have extreme values in one or several of the desired properties, while containing druglike chemical fragments. ChemGPS (chemical global positioning system) is a tool that combines these predefined rules and objects to provide a global drugspace map. The ChemGPS drugspace map coordinates are t-scores extracted via principal component analysis (PCA) from 72 descriptors that evaluate the above-mentioned rules on a total set of 423 satellite and core structures. Global ChemGPS scores describe well the latent structures extracted with PCA for a set of 8599 monocarboxylates, a set of 45 heteroaromatic compounds, and for 87 alpha-amino acids. ChemGPS positions novel structures in drugspace via PCA-score prediction, providing a unique mapping device for the druglike chemical space. ChemGPS scores are comparable across a large number of chemicals and do not change as new structures are predicted, making this tool a well-suited reference system for comparing multiple libraries and for keeping track of previously explored regions of the chemical space.

Chemical information management in drug discovery: optimizing the computational and combinatorial chemistry interfaces

Structure-property relationships, central to many of today's drug discovery strategies, are not straightforward to deal with when trying to predict drug efficacy, that is, the combined outcome of target affinity, pharmacodynamic behavior, pharmacokinetic properties, and metabolic fate. In this article, we discuss the handling of chemical property information in reagents-for-synthesis selection, enumeration, and virtual library construction. We describe the use of diversity assessment and/or experimental design in selection of compound-libraries-to-be-synthesized. Our overall objective was to identify good-quality drug candidates through reliable structure-activity relationship data, with the minimum number of compounds synthesized and tested. Chemical filters, property filters, scoring functions, and utilization of interactive visualization tools are discussed. The concept of chemical diversity and aspects of chemical space navigation employing a proprietary tool, Chemical Global Positioning System (ChemGPS), for mapping the drug-related chemical space are examined. Guidelines and workflow recommendations for the practicing medicinal chemist are proposed.

Progress in predicting human ADME parameters in silico

Understanding the development of a scientific approach is a valuable exercise in gauging the potential directions the process could take in the future. The relatively short history of applying computational methods to absorption, distribution, metabolism and excretion (ADME) can be split into defined periods. The first began in the 1960s and continued through the 1970s with the work of Corwin Hansch et al. Their models utilized small sets of in vivo ADME data. The second era from the 1980s through 1990s witnessed the widespread incorporation of in vitro approaches as surrogates of in vivo ADME studies. These approaches fostered the initiation and increase in interpretable computational ADME models available in the literature. The third era is the present were there are many literature data sets derived from in vitro data for absorption, drug-drug interactions (DDI), drug transporters and efflux pumps [P-glycoprotein (P-gp), MRP], intrinsic clearance and brain penetration, which can theoretically be used to predict the situation in vivo in humans. Combinatorial synthesis, high throughput screening and computational approaches have emerged as a result of continual pressure on pharmaceutical companies to accelerate drug discovery while decreasing drug development costs. The goal has become to reduce the drop-out rate of drug candidates in the latter, most expensive stages of drug development. This is accomplished by increasing the failure rate of candidate compounds in the preclinical stages and increasing the speed of nomination of likely clinical candidates. The industry now understands the reasons for clinical failure other than efficacy are mainly related to pharmacokinetics and toxicity. The late 1990s saw significant company investment in ADME and drug safety departments to assess properties such as metabolic stability, cytochrome P-450 inhibition, absorption and genotoxicity earlier in the drug discovery paradigm. The next logical step in this process is the evaluation of higher throughput data to determine if computational (in silico) models can be constructed and validated from it. Such models would allow an exponential increase in the number of compounds screened virtually for ADME parameters. A number of researchers have started to utilize in silico, in vitro and in vivo approaches in parallel to address intestinal permeability and cytochrome P-450-mediated DDI. This review will assess how computational approaches for ADME parameters have evolved and how they are likely to progress.

Principal components describing biological activities and molecular diversity of heterocyclic aromatic ring fragments

Ten physicochemical variables have been calculated for each of 100 different aromatic rings. These variables were selected because of their potential involvement in the molecular recognition of drug-receptor binding interactions, and they include size, lipophilicity, dipole magnitude and orientation, HOMO and LUMO energies, and electronic point charges. A total of 59 different aromatic ring systems were studied including monocyclics and [5.5]-, [6.5]- and [6.6]-fused bicyclics. A principal components analysis of b1ese results generated four principal components which account for 84% of the total variance in the data. These principal components provide a quantitative measure of molecular diversity, and their relevance for structure-activity relationships is discussed. The principal components correlate with the in vitro biological activity of heterocyclic aromatic fragments within a series of previously reported HIV-1 reverse transcriptase inhibitors.