Designing Antibacterial Compounds through a Topological Substructural Approach

Development of binary classification models for grouping hydroxylated polychlorinated biphenyls into active and inactive thyroid hormone receptor agonists

Some adverse effects of hydroxylated polychlorinated biphenyls (OH-PCBs) in humans are presumed to be initiated via thyroid hormone receptor (TR) binding. Due to the trial-and-error approach adopted for OH-PCB selection in previous studies, experiments designed to test the TR binding hypothesis mostly utilized inactive OH-PCBs, leading to considerable waste of time, effort and other material resources. In this paper, linear discriminant analysis (LDA) and binary logistic regression (LR) were used to develop classification models to group OH-PCBs into active and inactive TR agonists using radial distribution function (RDF) descriptors as predictor variables. The classifications made by both LDA and LR models on the training set compounds resulted in an accuracy of 84.3%, sensitivity of 72.2% and specificity of 90.9%. The areas under the ROC curves, constructed with the training set data, were found to be 0.872 and 0.880 for LDA and LR models, respectively. External validation of the models revealed that 76.5% of the test set compounds were correctly classified by both LDA and LR models. These findings suggest that the two models reported in this paper are good and reliable for classifying OH-PCB congeners into active and inactive TR agonists.

Identification of Novel Antibacterials Using Machine Learning Techniques

Many pharmaceutical companies are avoiding the development of novel antibacterials due to a range of rational reasons and the high risk of failure. However, there is an urgent need for novel antibiotics especially against resistant bacterial strains. Available in silico models suffer from many drawbacks and, therefore, are not applicable for scoring novel molecules with high structural diversity by their antibacterial potency. Considering this, the overall aim of this study was to develop an efficient in silico model able to find compounds that have plenty of chances to exhibit antibacterial activity. Based on a proprietary screening campaign, we have accumulated a representative dataset of more than 140,000 molecules with antibacterial activity against Escherichia coli assessed in the same assay and under the same conditions. This intriguing set has no analogue in the scientific literature. We applied six in silico techniques to mine these data. For external validation, we used 5,000 compounds with low similarity towards training samples. The antibacterial activity of the selected molecules against E. coli was assessed using a comprehensive biological study. Kohonen-based nonlinear mapping was used for the first time and provided the best predictive power (av. 75.5%). Several compounds showed an outstanding antibacterial potency and were identified as translation machinery inhibitors in vitro and in vivo. For the best compounds, MIC and CC50 values were determined to allow us to estimate a selectivity index (SI). Many active compounds have a robust IP position.

Cobaltaelectro-Catalyzed C-H Activation with Carbon Monoxide or Isocyanides

Electrochemical oxidative C-H/N-H activations with isocyanides have been realized with a versatile cobalt catalyst. The widely applicable cobalt catalysis manifold further enabled electrooxidative C-H/N-H carbonylations with carbon monoxide under ambient conditions. The C-H functionalizations were efficiently realized with ample scope and outstanding functional group tolerance in a user-friendly undivided cell setup.

Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks

Predicting the activity of new chemical compounds over pathogenic microorganisms with different metabolic reaction networks (MRN _s) is an important goal due to the different susceptibility to antibiotics. The ChEMBL database contains >160 000 outcomes of preclinical assays of antimicrobial activity for 55 931 compounds with >365 parameters of activity (MIC, IC₅₀, etc.) and >90 bacteria strains of >25 bacterial species. In addition, the Leong and Barabàsi data set includes >40 MRNs of microorganisms. However, there are no models able to predict antibacterial activity for multiple assays considering both drug and MRN structures at the same time. In this work, we combined perturbation theory, machine learning, and information fusion techniques to develop the first PTMLIF model. The best linear model found presented values of specificity = 90.31/90.40 and sensitivity = 88.14/88.07 in training/validation series. We carried out a comparison to nonlinear artificial neural network (ANN) techniques and previous models from the literature. Next, we illustrated the practical use of the model with an experimental case of study. We reported for the first time the isolation and characterization of terpenes from the plant Cissus incisa. The antibacterial activity of the terpenes was experimentally determined. The more active compounds were phytol and α-amyrin, with MIC = 100 μg/mL for Vancomycin-resistant Enterococcus faecium and Acinetobacter baumannii resistant to carbapenems. These compounds are already known from other sources. However, they have been isolated and evaluated for the first time here against several strains of multidrug-resistant bacteria including World Health Organization (WHO) priority pathogens. Last, we used the model to predict the activity of these compounds versus other microorganisms with different MRNs in order to find other potential targets.

Anti-tubercular drug discovery: in silico implications and challenges

Tuberculosis (TB) has been reported as a major public health concern, especially in the developing countries. WHO report on tuberculosis 2016 shows a high mortality rate caused by TB leading to 1.8 million deaths worldwide (including deaths due to TB in HIV positive individuals), which is one of the top 10 causes of mortality in 2015. However, the main therapy used for the treatment of TB is still the Direct Observed Therapy Short-course (DOTS) that consists of four main first-line drugs. Due to the prolonged and unorganized use of these drugs, Mycobacterium tuberculosis (Mtb) has developed drug-resistance against them. To overcome this drug-resistance, efforts are continuously being made to develop new therapeutics. New drug-targets of Mtb are pursued by the researchers to develop their inhibitors. For this, new methodologies that comprise of the computational drug designing techniques are vigorously applied. A major limitation that is found with these techniques is the inability of the newly identified target-based inhibitors to inhibit the whole cell bacteria. A foremost factor for this limitation is the inability of these inhibitors to penetrate the bacterial cell wall. In this regard, various strategies to overcome this limitation have been discussed in detail in this review, along with new targets and new methodologies. A bunch of in silico tools available for the prediction of physicochemical properties that need to be explored to deal with the permeability issue of the Mtb inhibitors has also been discussed.

A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents

Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions.

An index for characterization of natural and non-natural amino acids for peptidomimetics

Bioactive peptides and peptidomimetics play a pivotal role in the regulation of many biological processes such as cellular apoptosis, host defense, and biomineralization. In this work, we develop a novel structural matrix, Index of Natural and Non-natural Amino Acids (NNAAIndex), to systematically characterize a total of 155 physiochemical properties of 22 natural and 593 non-natural amino acids, followed by clustering the structural matrix into 6 representative property patterns including geometric characteristics, H-bond, connectivity, accessible surface area, integy moments index, and volume and shape. As a proof-of-principle, the NNAAIndex, combined with partial least squares regression or linear discriminant analysis, is used to develop different QSAR models for the design of new peptidomimetics using three different peptide datasets, i.e., 48 bitter-tasting dipeptides, 58 angiotensin-converting enzyme inhibitors, and 20 inorganic-binding peptides. A comparative analysis with other QSAR techniques demonstrates that the NNAAIndex method offers a stable and predictive modeling technique for in silico large-scale design of natural and non-natural peptides with desirable bioactivities for a wide range of applications.

Poisson parameters of antimicrobial activity: a quantitative structure-activity approach

A contingency of observed antimicrobial activities measured for several compounds vs. a series of bacteria was analyzed. A factor analysis revealed the existence of a certain probability distribution function of the antimicrobial activity. A quantitative structure-activity relationship analysis for the overall antimicrobial ability was conducted using the population statistics associated with identified probability distribution function. The antimicrobial activity proved to follow the Poisson distribution if just one factor varies (such as chemical compound or bacteria). The Poisson parameter estimating antimicrobial effect, giving both mean and variance of the antimicrobial activity, was used to develop structure-activity models describing the effect of compounds on bacteria and fungi species. Two approaches were employed to obtain the models, and for every approach, a model was selected, further investigated and found to be statistically significant. The best predictive model for antimicrobial effect on bacteria and fungi species was identified using graphical representation of observed vs. calculated values as well as several predictive power parameters.

Structural alerts for predicting clastogenic activity of pro-oxidant flavonoid compounds: quantitative structure-activity relationship study

Flavonoids have been reported to exert multiple biological effects that include acting as pro-oxidants at very high doses. The authors determined a structural alert to identify the clastogenic activity of a series of flavonoids with pro-oxidant activity. The methodology was based on a quantitative structure-activity relationship (QSAR) study. Specifically, the authors developed a virtual screening method for a clastogenic model using the topological substructural molecular design (TOPS-MODE) approach. It represents a useful platform for the automatic generation of structural alerts, based on the calculation of spectral moments of molecular bond matrices appropriately weighted, taking into account the hydrophobic, electronic, and steric molecular features. Therefore, it was possible to establish the structural criteria for maximal clastogenicity of pro-oxidant flavonoids: the presence of a 3-hydroxyl group and a 4-carbonyl group in ring C, the maximal number of hydroxyl groups in ring B, the presence of methoxyl and phenyl groups, the absence of a 2,3-double bond in ring C, and the presence of 5,7 hydroxyl groups in ring A. The presented clastogenic model may be useful for screening new pro-oxidant compounds. This alert could help in the design of new and efficient flavonoids, which could be used as bioactive compounds in nutraceuticals and functional food.

Descriptors for antimicrobial peptides

INTRODUCTION

A frightening increase in the number of isolated multidrug resistant bacterial strains linked to the decline in novel antimicrobial drugs entering the market is a great cause for concern. Cationic antimicrobial peptides (AMPs) have lately been introduced as a potential new class of antimicrobial drugs, and computational methods utilizing molecular descriptors can significantly accelerate the development of new peptide drug candidates.

AREAS COVERED

This paper gives a broad overview of peptide and amino-acid scale descriptors available for AMP modeling and highlights which of these are currently being used in quantitative structure-activity relationship (QSAR) studies for AMP optimization. Additionally, some key commercial computational tools are discussed, and both successful and less successful studies are referenced, illustrating some of the challenges facing AMP scientists. Through examples of different peptide QSAR studies, this review highlights some of the missing links and illuminates some of the questions that would be interesting to challenge in a more systematic fashion.

EXPERT OPINION

Computer-aided peptide QSAR using molecular descriptors may provide the necessary edge to peptide drug discovery, enabling successful design of a new generation anti-infective drug molecules. However, if this wonderful scenario is to play out, computational chemists and peptide microbiologists would need to start playing together and not just side by side.

Scoring function for DNA-drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories

We introduce here a new class of invariants for MD trajectories based on the spectral moments pi(k)(L) of the Markov matrix associated to lattice network-like (LN) graph representations of Molecular Dynamics (MD) trajectories. The procedure embeds the MD energy profiles on a 2D Cartesian coordinates system using simple heuristic rules. At the same time, we associate the LN with a Markov matrix that describes the probabilities of passing from one state to other in the new 2D space. We construct this type of LNs for 422 MD trajectories obtained in DNA-drug docking experiments of 57 furocoumarins. The combined use of psoralens+ultraviolet light (UVA) radiation is known as PUVA therapy. PUVA is effective in the treatment of skin diseases such as psoriasis and mycosis fungoides. PUVA is also useful to treat human platelet (PTL) concentrates in order to eliminate Leishmania spp. and Trypanosoma cruzi. Both are parasites that cause Leishmaniosis (a dangerous skin and visceral disease) and Chagas disease, respectively; and may circulate in blood products collected from infected donors. We included in this study both lineal (psoralens) and angular (angelicins) furocoumarins. In the study, we grouped the LNs on two sets; set1: DNA-drug complex MD trajectories for active compounds and set2: MD trajectories of non-active compounds or no-optimal MD trajectories of active compounds. We calculated the respective pi(k)(L) values for all these LNs and used them as inputs to train a new classifier that discriminate set1 from set2 cases. In training series the model correctly classifies 79 out of 80 (specificity=98.75%) set1 and 226 out of 238 (Sensitivity=94.96%) set2 trajectories. In independent validation series the model correctly classifies 26 out of 26 (specificity=100%) set1 and 75 out of 78 (sensitivity=96.15%) set2 trajectories. We propose this new model as a scoring function to guide DNA-docking studies in the drug design of new coumarins for anticancer or antiparasitic PUVA therapy.

Multi-target spectral moment: QSAR for antiviral drugs vs. different viral species

The antiviral QSAR models have an important limitation today. They predict the biological activity of drugs against only one viral species. This is determined by the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this work, we use Markov Chain theory to calculate new multi-target spectral moments to fit a QSAR model for drugs active against 40 viral species. The model is based on 500 drugs (including active and non-active compounds) tested as antiviral agents in the recent literature; not all drugs were predicted against all viruses, but only those with experimental values. The database also contains 207 well-known compounds (not as recent as the previous ones) reported in the Merck Index with other activities that do not include antiviral action against any virus species. We used Linear Discriminant Analysis (LDA) to classify all these drugs into two classes as active or non-active against the different viral species tested, whose data we processed. The model correctly classifies 5129 out of 5594 non-active compounds (91.69%) and 412 out of 422 active compounds (97.63%). Overall training predictability was 92.34%. The validation of the model was carried out by means of external predicting series, the model classifying, thus, 2568 out of 2779 non-active compounds and 224 out of 229 active compounds. Overall training predictability was 92.82%. The present work reports the first attempts to calculate within a unified framework the probabilities of antiviral drugs against different virus species based on a spectral moment analysis.

Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks

One limitation of almost all antiviral Quantitative Structure-Activity Relationships (QSAR) models is that they predict the biological activity of drugs against only one species of virus. Consequently, the development of multi-tasking QSAR models (mt-QSAR) to predict drugs activity against different species of virus is of the major vitally important. These mt-QSARs offer also a good opportunity to construct drug-drug Complex Networks (CNs) that can be used to explore large and complex drug-viral species databases. It is known that in very large CNs we can use the Giant Component (GC) as a representative sub-set of nodes (drugs) and but the drug-drug similarity function selected may strongly determines the final network obtained. In the three previous works of the present series we reported mt-QSAR models to predict the antimicrobial activity against different fungi [Gonzalez-Diaz, H.; Prado-Prado, F. J.; Santana, L.; Uriarte, E. Bioorg.Med.Chem.2006, 14, 5973], bacteria [Prado-Prado, F. J.; Gonzalez-Diaz, H.; Santana, L.; Uriarte E. Bioorg.Med.Chem.2007, 15, 897] or parasite species [Prado-Prado, F.J.; González-Díaz, H.; Martinez de la Vega, O.; Ubeira, F.M.; Chou K.C. Bioorg.Med.Chem.2008, 16, 5871]. However, including these works, we do not found any report of mt-QSAR models for antivirals drug, or a comparative study of the different GC extracted from drug-drug CNs based on different similarity functions. In this work, we used Linear Discriminant Analysis (LDA) to fit a mt-QSAR model that classify 600 drugs as active or non-active against the 41 different tested species of virus. The model correctly classifies 143 of 169 active compounds (specificity=84.62%) and 119 of 139 non-active compounds (sensitivity=85.61%) and presents overall training accuracy of 85.1% (262 of 308 cases). Validation of the model was carried out by means of external predicting series, classifying the model 466 of 514, 90.7% of compounds. In order to illustrate the performance of the model in practice, we develop a virtual screening recognizing the model as active 92.7%, 102 of 110 antivirus compounds. These compounds were never use in training or predicting series. Next, we obtained and compared the topology of the CNs and their respective GCs based on Euclidean, Manhattan, Chebychey, Pearson and other similarity measures. The GC of the Manhattan network showed the more interesting features for drug-drug similarity search. We also give the procedure for the construction of Back-Projection Maps for the contribution of each drug sub-structure to the antiviral activity against different species.

Using multidimensional patterns of amino acid attributes for QSAR analysis of peptides

On the basis of exploratory factor analysis, six multidimensional patterns of 516 amino acid attributes, namely, factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, are proposed to represent structures of 48 bitter-tasting dipeptides and 58 angiotensin-converting enzyme inhibitors. Characteristic parameters related to bioactivities of the peptides studied are selected by genetic algorithm, and quantitative structure-activity relationship (QSAR) models are constructed by partial least square (PLS). Our results by a leave-one-out cross validation are compared with the previously known structure representation method and are shown to give slightly superior or comparative performance. Further, two data sets are divided into training sets and test sets to validate the characterization repertoire of FASGAI. Performance of the PLS models developed by training samples by a leave-one-out cross validation and external validation for test samples are satisfying. These results demonstrate that FASGAI is an effective representation technique of peptide structures, and that FASGAI vectors have many preponderant characteristics such as straightforward physicochemical information, high characterization competence and easy manipulation. They can be further applied to investigate the relationship between structures and functions of various peptides, even proteins.

Topological research on diamagnetic susceptibilities of organic compounds

A novel molecular connectivity index, (m)chi('), based on the adjacency matrix of molecular graphs and novel atomic valence connectivities, delta(i)(') for predicting the molar diamagnetic susceptibilities of organic compounds is proposed. The delta(i)(') is defined as: delta(i)(') = delta(i)(nu) x Ei=12:625, where delta(i)(nu) and E(i) are the atomic valence connectivity and the valence orbital energy of atom i, respectively. A good QSPR model for molar diamagnetic susceptibilities can be constructed from (0)chi('), (1)chi('), (2)chi(') and (4)chi(p)(') using multivariate linear regression (MLR). The correlation coefficient r, standard error, and average absolute deviation of the MLR model are 0.9918, 5.56 cgs, and 4.26 cgs, respectively, for the 721 organic compounds tested (training set). Cross-validation using the leave-one-out method demonstrates that the MLR model is highly reliable statistically. Using the MLR model, the average absolute deviations of the predicted values of molar diamagnetic susceptibility of another 360 organic compounds (test set) is 4.34 cgs. The results show that the current method is more effective than literature methods for estimating the molar diamagnetic susceptibility of an organic compound. The MLR method thus provides an acceptable model for the prediction of molar diamagnetic susceptibilities of organic compounds.

Search for new antagonist ligands for adenosine receptors from QSAR point of view. How close are we?

In view of the large libraries of nucleoside analogues that are now being handled in organic synthesis, the identification of drug biological activity is advisable prior to synthesis and this can be achieved by employing predictive biological property methods. In this sense, Quantitative Structure-Activity Relationships (QSAR) or docking approaches have emerged as promising tools. Although a large number of in silico approaches have been described in the literature for the prediction of different biological activities, the use of QSAR applications to develop adenosine receptor (AR) antagonists is not common as for the case of the antibiotics and anticancer compounds for instance. The intention of this review is to summarize the present knowledge concerning computational predictions of new molecules as adenosine receptor antagonists.

ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds

Developing a model for predicting anticancer activity of any classes of organic compounds based on molecular structure is very important goal for medicinal chemist. Different molecular descriptors can be used to solve this problem. Stochastic molecular descriptors so-called the MARCH-INSIDE approach, shown to be very successful in drug design. Nevertheless, the structural diversity of compounds is so vast that we may need non-linear models such as artificial neural networks (ANN) instead of linear ones. SmartMLP-ANN analysis used to model the anticancer activity of organic compounds has shown high average accuracy of 93.79% (train performance) and predictability of 90.88% (validation performance) for the 8:3-MLP topology with different training and predicting series. This ANN model favourably compares with respect to a previous linear discriminant analysis (LDA) model [H. González-Díaz et al., J. Mol. Model 9 (2003) 395] that showed only 80.49% of accuracy and 79.34% of predictability. The present SmartMLP approach employed shorter training times of only 10h while previous models give accuracies of 70-89% only after 25-46 h of training. In order to illustrate the practical use of the model in bioorganic medicinal chemistry, we report the in silico prediction, and in vitro evaluation of six new synthetic tegafur analogues having IC(50) values in a broad range between 37.1 and 138 microgmL(-1) for leukemia (L1210/0) and human T-lymphocyte (Molt4/C8, CEM/0) cells. Theoretical predictions coincide very well with experimental results.

2D-autocorrelation descriptors for predicting cytotoxicity of naphthoquinone ester derivatives against oral human epidermoid carcinoma

A QSAR study was developed, employing 2D-autocorrelation descriptors and a set of 37 naphthoquinone ester derivatives, in order to model the cytotoxicity of these compounds against oral human epidermoid carcinoma (KB). A comparison with other approaches such as the BCUT, Galvez topological charge indexes, Randić molecular profile, Geometrical, and RDF descriptors was carried out. Mathematical models were obtained by means of the multiple regression analysis (MRA) and the variables were selected using genetic algorithm. Based on the statistical results the 2D-autocorrelation descriptors were considered the best and were able to describe more than 84.2% of the variance in the experimental activity once we controlled for outliers.

QSAR study of anticoccidial activity for diverse chemical compounds: Prediction and experimental assay of trans-2-(2-nitrovinyl)furan

In this work we report a QSAR model that discriminates between chemically heterogeneous classes of anticoccidial and non-anticoccidial compounds. For this purpose we used the Markovian Chemicals in silico Design (MARCH-INSIDE) approach J. Mol. Mod.2002, 8, 237-245; J. Mol. Mod.2003, 9, 395-407]. Linear discriminant analysis allowed us to fit the discriminant function. This function correctly classifies 86.67% of anticoccidial compounds and 96.23% of inactive compounds in the training series. Overall classification is 94.12%. We validated the model by means of an external predicting series, with 86.96% of global predictability. Remarkably, the present model is based on topological as well as configuration-dependent molecular descriptors. Therefore, the model performs timely calculations and allows discrimination between Z/E and chiral isomers. Finally, to exemplify the use of the model in practice we report the prediction and experimental assay of trans-2-(2-nitrovinyl)furan. It is notable that lesion control was 72.86% at mg/kg of body weight with respect to 60% at 125 mg/kg for amprolium (control drug). The back-projection map for this compound predicts a high level of importance for the double bond and for the nitro group in the trans position. We conclude that the MARCH-INSIDE approach enables the accurate fast track identification of anticoccidial hits. Moreover, trans-2-(2-nitrovinyl)furan seems to be a promising drug for the treatment of coccidiosis.

Computational chemistry comparison of stable/nonstable protein mutants classification models based on 3D and topological indices

In principle, there are different protein structural parameters that can be used in computational chemistry studies to classify protein mutants according to thermal stability including: sequence, connectivity, and 3D descriptors. Connectivity parameters (called topological indices, TIs) are simpler than 3D parameters being then less computationally expensive. However, TIs ignore important aspects of protein structure and hence are expected to be inaccurate. In any case, a comparison of 3D and TIs has not been reported with respect to the power of discrimination of proteins according to stability. In this study, we compare both classes of indices in this sense by the first time. The best model found, based on 3D spectral moments correctly classified 507 out of 525 (96.6%) proteins while TIs model correctly classified 404 out of 525 (77.0%) proteins. We have shown that, in fact, 3D descriptor models gave more accurate results than TIs but interestingly, TIs give acceptable results in a timely way in spite of their simplicity.

On an aspect of calculated molecular descriptors in QSAR studies of quinolone antibacterials

The re-emergence of tuberculosis infections, which are resistant to conventional drug therapy, has steadily risen in the last decade and as a result of that, fluoroquinolone drugs are being used as the second line of action. But there is hardly any study to examine specific structure activity relationships of quinolone antibacterials against mycobacteria. In this paper, an attempt has been made to establish a quantitative structure activity relationship modeling for a series of quinolone compounds against Mycobacterium fortuitum and Mycobacterium smegmatis. Due to lack of sufficient physicochemical data for the anti-mycobacterial compounds, it becomes very difficult to develop predictive methods based on experimental data. The present paper is an effort for the development of QSARs from the standpoint of physicochemical, constitutional, geometrical, electrostatic and topological indices. Molecular descriptors have been calculated solely from the chemical structure of N-1, C-7 and 8 substituted quinolone compounds and ridge regression models have been developed which can explain a better structure-activity relationship. Consideration of an intermolecular similarity analysis approach that led to a successful computer program development in PERL language has been used for comparing the influence of various molecular descriptors in different data subsets. The comparison of relative effectiveness of the calculated descriptors in our ridge regression model gives rise to some interesting results.

3D QSAR on a library of heterocyclic diamidine derivatives with antiparasitic activity

African trypanosomes, Trypanosoma brucei rhodesiense (TBR) and Trypanosoma brucei gambiense (TBG), affect hundreds of thousands of lives in tropical regions of the world. The toxicity of the diamidine pentamidine, an effective drug against TBG, necessitates the design of better drugs. An orally effective prodrug of the diamidine, furamidine (DB75), presently scheduled for phase III clinical trials, has excellent activity against TBG with toxicity lower than that of pentamidine. As part of an effort to develop additional and improved diamidines against African trypanosomes, CoMFA and CoMSIA 3D QSAR analyses have been conducted with furamidine and a set of 25 other structurally related compounds. Two different alignment strategies, based on a putative kinetoplast DNA minor groove target, were used. Due to conserved electrostatic properties across the compounds, models that used only steric and electronic properties did not perform well in predicting biological results. An extended CoMSIA model with additional descriptors for hydrophobic, donor, and acceptor properties had good predictive ability with a q2=0.699, r2=0.974, SEE, standard error of estimate=0.1, and F=120.04. The results have been used as a guide to design compounds that, potentially, have better activity against African trypanosomes.

Can ‘Bacterial-Metabolite-Likeness' Model Improve Odds of ‘in Silico' Antibiotic Discovery?

'Inductive' QSAR descriptors have been used to develop the series of QSAR models enabling 'in silico' distinguishing between antimicrobial compounds, conventional drugs, and druglike substances. The constructed neural network-based models operating by 30 'inductive' parameters have been validated on an extensive set of 2686 chemical structures and resulted in up to 97% accurate separation of the three types of molecular activities. The demonstrated ability of 'inductive' parameters to adequately capture molecular features determining 'antibiotic-like' and 'druglike' potentials have been further utilized to construct a model of 'Bacterial-Metabolite-Likeness' (BML). The same 'inductive' descriptors have been used to train a neural network that could very accurately recognize substances involved into bacterial metabolism (that have been experimentally identified). When the developed model has been applied to the mixed set of antimicrobials, drugs, and druglike chemicals (not used for training the BML model), it exhibited a 2-5-fold recognition preference toward antimicrobial compounds compared to general drugs and an 18- to 45-fold preference when compared to a druglike substance (depending on the model stringency). These results illustrate immanent similarity between conventional antimicrobials and native bacterial metabolites and suggest that the developed BML model can be an effective classification tool for 'in silico' antibiotic studies.

A topological function based on spectral moments for predicting affinity toward A3 adenosine receptors

The spectral moment descriptors have been applied to the study of affinity for A(3) adenosine receptors of 32 adenosine analogues. A model, able to describe more than 95% of the variance in the experimental activity, was developed with the use of the above-mentioned approach. The fragment contributions to the activity carried out show that the sulfonamido moiety at the N(6) position and hydrogen bonding play an important role in the interaction with the receptor.

Non-stochastic and stochastic linear indices of the molecular pseudograph’s atom-adjacency matrix: a novel approach for computational in silico screening and “rational” selection of new lead antibacterial agents

A novel approach (TOMOCOMD-CARDD) to computer-aided "rational" drug design is illustrated. This approach is based on the calculation of the non-stochastic and stochastic linear indices of the molecular pseudograph's atom-adjacency matrix representing molecular structures. These TOMOCOMD-CARDD descriptors are introduced for the computational (virtual) screening and "rational" selection of new lead antibacterial agents using linear discrimination analysis. The two structure-based antibacterial-activity classification models, including non-stochastic and stochastic indices, classify correctly 91.61% and 90.75%, respectively, of 1525 chemicals in training sets. These models show high Matthews correlation coefficients (MCC=0.84 and 0.82). An external validation process was carried out to assess the robustness and predictive power of the model obtained. These QSAR models permit the correct classification of 91.49% and 89.31% of 505 compounds in an external test set, yielding MCCs of 0.84 and 0.79, respectively. The TOMOCOMD-CARDD approach compares satisfactorily with respect to nine of the most useful models for antimicrobial selection reported to date. Finally, an in silico screening of 87 new chemicals reported in the anti-infective field with antibacterial activities is developed showing the ability of the TOMOCOMD-CARDD models to identify new lead antibacterial compounds.

Proteins QSAR with Markov average electrostatic potentials

Classic physicochemical and topological indices have been largely used in small molecules QSAR but less in proteins QSAR. In this study, a Markov model is used to calculate, for the first time, average electrostatic potentials xik for an indirect interaction between aminoacids placed at topologic distances k within a given protein backbone. The short-term average stochastic potential xi1 for 53 Arc repressor mutants was used to model the effect of Alanine scanning on thermal stability. The Arc repressor is a model protein of relevance for biochemical studies on bioorganics and medicinal chemistry. A linear discriminant analysis model developed correctly classified 43 out of 53, 81.1% of proteins according to their thermal stability. More specifically, the model classified 20/28, 71.4% of proteins with near wild-type stability and 23/25, 92.0% of proteins with reduced stability. Moreover, predictability in cross-validation procedures was of 81.0%. Expansion of the electrostatic potential in the series xi0, xi1, xi2, and xi3, justified the use of the abrupt truncation approach, being the overall accuracy >70.0% for xi0 but equal for xi1, xi2, and xi3. The xi1 model compared favorably with respect to others based on D-Fire potential, surface area, volume, partition coefficient, and molar refractivity, with less than 77.0% of accuracy [Ramos de Armas, R.; González-Díaz, H.; Molina, R.; Uriarte, E. Protein Struct. Func. Bioinf.2004, 56, 715]. The xi1 model also has more tractable interpretation than others based on Markovian negentropies and stochastic moments. Finally, the model is notably simpler than the two models based on quadratic and linear indices. Both models, reported by Marrero-Ponce et al., use four-to-five time more descriptors. Introduction of average stochastic potentials may be useful for QSAR applications; having xik amenable physical interpretation and being very effective.

Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity

The TOpological MOlecular COMputer Design (TOMOCOMD-CARDD) approach has been introduced for the classification and design of antimicrobial agents using computer-aided molecular design. For this propose, atom, atom-type, and total quadratic indices have been generalized to codify chemical structure information. In this sense, stochastic quadratic indices have been introduced for the description of the molecular structure. These stochastic fingerprints are based on a simple model for the intramolecular movement of all valence-bond electrons. In this work, a complete data set containing 1006 antimicrobial agents is collected and presented. Two structure-based antibacterial activity classification models have been generated. The models (including nonstochastic and stochastic indices) classify correctly more than 90% of 1525 compounds in training sets. These models permit the correct classification of 92.28% and 89.31% of 505 compounds in an external test sets. The TOMOCOMD-CARDD approach, also, satisfactorily compares with respect to nine of the most useful models for antimicrobial selection reported to date. Finally, a virtual screening of 87 new compounds reported in the antiinfective field with antibacterial activities is developed showing the ability of the TOMOCOMD-CARDD models to identify new leads as antibacterial.

QSAR for anti-RNA-virus activity, synthesis, and assay of anti-RSV carbonucleosides given a unified representation of spectral moments, quadratic, and topologic indices

The unified representation of spectral moments, classic topologic indices, quadratic indices, and stochastic molecular descriptors show that all these molecular descriptors lie within the same family. Consequently, the same prior probability for a successful quantitative-structure-activity-relationship (QSAR) may be expected irrespective of which indices are selected. Herein, we used stochastic spectral moments as molecular descriptors to seek a QSAR using a database of 221 bioactive compounds previously tested against diverse RNA-viruses and 402 nonactive ones. The QSAR model thus obtained correctly classifies 90.9% of compounds in training. The model also correctly classifies a total of 87.9% of 207 compounds on additional external predicting series, 73 of them having anti-RNA-virus activity and 134 nonactive ones. In addition, all compounds were regrouped into five different subsets for leave-group-out studies: (1) anti-influenza, (2) anti-picornavirus, (3) anti-paramyxovirus, (4) anti-RSV/anti-influenza, and (5) broad range anti-RNA-virus activity. The model has retained overall accuracies of about 90% on these studies validating model robustness. Finally, we exemplify the practical use of the model with the discovery of compounds 124 and 128. These compounds presented MIC50 values=3.2 and 8 microg/mL against respiratory syncytial virus (RSV) respectively. Both compounds also have low cytotoxicity expressed by their Minimal Cytotoxic Concentrations >400 microg/mL for HeLa cells. The present approach represents an effort toward a formalization and application of molecular indices in bioorganic and medicinal chemistry.

Markovian chemicals “in silico” design (MARCH-INSIDE), a promising approach for computer-aided molecular design III: 2.5D indices for the discovery of antibacterials

The present work continues our series on the use of MARCH-INSIDE molecular descriptors (parts I and II: J Mol Mod 8:237-245, [2002] and 9:395-407, [2003]). These descriptors encode information pertaining to the distribution of electrons in the molecule based on a simple stochastic approach to the idea of electronegativity equalization (Sanderson's principle). Here, 3D-MARCH-INSIDE molecular descriptors for 667 organic compounds are used as input for a linear discriminant analysis. This 2.5D-QSAR model discriminates between antibacterial compounds and non-antibacterial ones with 92.9% accuracy in training sets. On the other hand, the model classifies 94.0% of the compounds in test set correctly. Additionally, the present QSAR performs similar-to-better than other methods reported elsewhere. Finally, the discovery of a novel compound illustrates the use of the method. This compound, 2-bromo-3-(furan-2-yl)-3-oxo-propionamide has MIC50 of 6.25 and 12.50 microg/mL against Pseudomonas aeruginosa ATCC 27853 and Escherichia coli ATCC 27853, respectively while ampicillin, amoxicillin, clindamycin, and metronidazole have, for instance, MIC50 values higher than 250 mug/mL against E. coli. Consequently, the present method may becomes a useful tool for the in silico discovery of antibacterials.

Predicting stability of Arc repressor mutants with protein stochastic moments

As more and more protein structures are determined and applied to drug manufacture, there is increasing interest in studying their stability. In this study, the stochastic moments ((SR)pi(k)) of 53 Arc repressor mutants were introduced as molecular descriptors modeling protein stability. The Linear Discriminant Analysis model developed correctly classified 43 out of 53, 81.13% of proteins according to their thermal stability. More specifically, the model classified 20/28 (71.4%) proteins with near wild-type stability and 23/25 (92%) proteins with reduced stability. Moreover, validation of the model was carried out by re-substitution procedures (81.0%). In addition, the stochastic moments based model compared favorably with respect to others based on physicochemical and geometric parameters such as D-Fire potential, surface area, volume, partition coefficient, and molar refractivity, which presented less than 77% of accuracy. This result illustrates the possibilities of the stochastic moments' method for the study of bioorganic and medicinal chemistry relevant proteins.

Application of 'inductive' QSAR descriptors for quantification of antibacterial activity of cationic polypeptides

On the basis of the inductive QSAR descriptors we have created a neural network-based solution enabling quantification of antibacterial activity in the series of 101 synthetic cationic polypeptides (CAMEL-s). The developed QSAR model allowed 80% correct categorical classification of antibacterial potencies of the CAMEL-s both in the training and the validation sets. The accuracy of the activity predictions demonstrates that a narrow set of 3D sensitive 'inductive' descriptors can adequately describe the aspects of intra- and intermolecular interactions that are relevant for antibacterial activity of the cationic polypeptides. The developed approach can be further expanded for the larger sets of biologically active peptides and can serve as a useful quantitative tool for rational antibiotic design and discovery.