2D MI-DRAGON: A new predictor for protein–ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins

Development of Nitric Oxide Releasing Oxoisoaporphines with Antidepressant Activities by Simultaneously Regulating MAO-A and SERT

The occurrence of depression is closely related to the decrease in serotonin (5-HT) levels in the synaptic cleft. Designing negative regulators aiming at intervening in MAO-A and serotonin transporter (SERT) could work synergistically to elevate synaptic 5-HT levels and thus might exhibit superior antidepressant efficacy. By linking the lead compound oxoisoaporphine to various nitric oxide donors, we endeavored to design and synthesize 10 synergistic negative regulators. The overarching objective was to maintain the original inhibitory effect on MAO-A while concurrently mitigating SERT-mediated reuptake of 5-HT. Within the spectrum of inhibitory compounds, I7 showcased the most formidable neuroprotective efficacy in a cellular depression model. In vivo experiments demonstrated that I7 significantly improved depressive behavior in both zebrafish and mice. Further research indicated that the antidepressant mechanism of I7 was associated with the downregulation of both MAO-A and SERT.

Development of MAO-A and 5-HT2AR Dual Inhibitors with Improved Antidepressant Activity

Designing dual-target inhibitors targeting 5-HT_2AR and MAO-A could synergistically promote interstitial 5-HT levels, so as to exhibit a more efficient antidepressant effect. On the premise of maintaining the original pharmacophore binding, arylpiperazine scaffolds and 5-oxygen-substituted oxoisoaporphines were hybridized to afford 15 dual-target inhibitors through suitable linkers. Among all inhibitors, I₁₄ exhibited the best inhibitory activities against 5-HT_2AR and MAO-A. In vitro cell proliferation assays showed that most compounds were nontoxic to neuronal cells and normal hepatocytes. I₁₄ also significantly ameliorated the depression-like behavior of zebrafish and mice. Further study revealed that I₁₄ was able to occupy the active cavity of 5-HT_2AR and MAO-A with multiple hydrogen bonding forces and π-π stacking interaction. I₁₄ was also able to repair the damage of mice hippocampal neuronal cells and reduce the expression of 5-HT_2AR in mice brain tissue. In conclusion, I₁₄ could be a potential antidepressant candidate for further study.

Developing a Multi-target Model to Predict the Activity of Monoamine Oxidase A and B Drugs

INTRODUCTION

Monoamine oxidase inhibitors (MAOIs) are compounds largely used in the treatment of Parkinson's disease (PD), Alzheimer's disease and other neuropsychiatric disorders since they are closely related to the MAO enzymes activity. The two isoforms of the MAO enzymes, MAO-A and MAO-B, are responsible for the degradation of monoamine neurotransmitters and due to this, relevant efforts have been devoted to finding new compounds with more selectivity and less side effects. One of the most used approaches is based on the use of computational approaches since they are time and money-saving and may allow us to find a more relevant structure-activity relationship.

OBJECTIVE

In this manuscript, we will review the most relevant computational approaches aimed at the prediction and development of new MAO inhibitors. Subsequently, we will also introduce a new multitask model aimed at predicting MAO-A and MAO-B inhibitors.

METHODS

The QSAR multi-task model herein developed was based on the use of the linear discriminant analysis. This model was developed gathering 5,759 compounds from the public dataset Chembl. The molecular descriptors used was calculated using the Dragon software. Classical statistical tests were performed to check the validity and robustness of the model.

RESULTS

The herein proposed model is able to correctly classify all the 5,759 compounds. All the statistical performed tests indicated that this model is robust and reproducible.

CONCLUSION

MAOIs are compounds of large interest since they are largely used in the treatment of very serious illness. These inhibitors may lose efficacy and produce severe side effects. Due to this, the development of selective MAO-A or MAO-B inhibitors is crucial for the treatment of these diseases and their effects. The herein proposed multi-target QSAR model may be a relevant tool in the development of new and more selective MAO inhibitors.

An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features

Background

Prediction of novel Drug–Target interactions (DTIs) plays an important role in discovering new drug candidates and finding new proteins to target. In consideration of the time-consuming and expensive of experimental methods. Therefore, it is a challenging task that how to develop efficient computational approaches for the accurate predicting potential associations between drug and target.

Results

In the paper, we proposed a novel computational method called WELM-SURF based on drug fingerprints and protein evolutionary information for identifying DTIs. More specifically, for exploiting protein sequence feature, Position Specific Scoring Matrix (PSSM) is applied to capturing protein evolutionary information and Speed up robot features (SURF) is employed to extract sequence key feature from PSSM. For drug fingerprints, the chemical structure of molecular substructure fingerprints was used to represent drug as feature vector. Take account of the advantage that the Weighted Extreme Learning Machine (WELM) has short training time, good generalization ability, and most importantly ability to efficiently execute classification by optimizing the loss function of weight matrix. Therefore, the WELM classifier is used to carry out classification based on extracted features for predicting DTIs. The performance of the WELM-SURF model was evaluated by experimental validations on enzyme, ion channel, GPCRs and nuclear receptor datasets by using fivefold cross-validation test. The WELM-SURF obtained average accuracies of 93.54, 90.58, 85.43 and 77.45% on enzyme, ion channels, GPCRs and nuclear receptor dataset respectively. We also compared our performance with the Extreme Learning Machine (ELM), the state-of-the-art Support Vector Machine (SVM) on enzyme and ion channels dataset and other exiting methods on four datasets. By comparing with experimental results, the performance of WELM-SURF is significantly better than that of ELM, SVM and other previous methods in the domain.

Conclusion

The results demonstrated that the proposed WELM-SURF model is competent for predicting DTIs with high accuracy and robustness. It is anticipated that the WELM-SURF method is a useful computational tool to facilitate widely bioinformatics studies related to DTIs prediction.

Aporphines and Alzheimer's Disease: Towards a Medical Approach Facing the Future

Alzheimer's disease (AD) is a neurodegenerative disease that reduces progressively the part cognitive inside the Central Nervous System (CNS) and that affects the memories and emotions of the patients who endure this disease. Many drugs have been assessed in patients with different evolutionary grades of the disease, having diverse results, depending on the used compound. Some of them afford dependence and many others with side effects that affect the emotional part and the economic cost of the treatment. The natural products have diversified their therapeutic uses, and have been used in the treatment of AD in accordance with its easy medical administration and bioavailability. In this review, the use of aporphines in nature for treating Alzheimer's disease, alkaloids isolated from natural and/or synthetic sources have been used principally as cholinesterase inhibitors (acetyl- and butyrylcholinesterase) as galantamine, for instance, though its use has been questioned for being slightly effective or marginal. The use of aporphines give the possibility of generating new treatments with nitrogenous chemical structures of diverse complexity and that are focused in this review comparatively and with real therapeutic scopes.

Perturbation Theory Machine Learning Modeling of Immunotoxicity for Drugs Targeting Inflammatory Cytokines and Study of the Antimicrobial G1 Using Cytometric Bead Arrays

ChEMBL biological activities prediction for 1-5-bromofur-2-il-2-bromo-2-nitroethene (G1) is a difficult task for cytokine immunotoxicity. The current study presents experimental results for G1 interaction with mouse Th1/Th2 and pro-inflammatory cytokines using a cytometry bead array (CBA). In the in vitro test of CBA, the results show no significant differences between the mean values of the Th1/Th2 cytokines for the samples treated with G1 with respect to the negative control, but there are moderate differences for cytokine values between different periods (24/48 h). The experiments show no significant differences between the mean values of the pro-inflammatory cytokines for the samples treated with G1, regarding the negative control, except for the values of tumor necrosis factor (TNF) and Interleukin (IL6) between the group treated with G1 and the negative control at 48 h. Differences occur for these cytokines in the periods (24/48 h). The study confirmed that the antimicrobial G1 did not alter the Th1/Th2 cytokines concentration in vitro in different periods, but it can alter TNF and IL6. G1 promotes free radicals production and activates damage processes in macrophages culture. In order to predict all ChEMBL activities for drugs in other experimental conditions, a ChEMBL data set was constructed using 25 biological activities, 1366 assays, 2 assay types, 4 assay organisms, 2 organisms, and 12 cytokine targets. Molecular descriptors calculated with Rcpi and 15 machine learning methods were used to find the best model able to predict if a drug could be active or not against a specific cytokine, in specific experimental conditions. The best model is based on 120 selected molecular descriptors and a deep neural network with area under the curve of the receiver operating characteristic of 0.904 and accuracy of 0.832. This model predicted 1384 G1 biological activities against cytokines in all ChEMBL data set experimental conditions.

Oxoisoaporphine Alkaloids: Prospective Anti-Alzheimer's Disease, Anticancer, and Antidepressant Agents

Oxoisoaporphine alkaloids are a family of oxoisoquinoline-derived alkaloids that were first isolated from the rhizome of Menispermum dauricum DC. (Menispermaceae). It has been demonstrated that oxoisoaporphine alkaloids possess various biological properties, such as cholinesterase and β-amyloid inhibition, acting as a topoisomerase intercalator, monoamine oxidase A inhibition, and are expected to become anti-Alzheimer's disease, anticancer, and antidepressant drugs. This review provides an overview of natural sources, synthetic routes, bioactivities, structure-function relationship, and modification investigations into oxoisoaporphine alkaloids, with the aim of providing references to the structure-activity relationships for the design and development of oxoisoaporphine derivatives with higher efficacy and therapeutic potential.

Exploring the anti-proliferative activity of Pelargonium sidoides DC with in silico target identification and network pharmacology

Pelargonium sidoides DC (Geraniaceae) is a medicinal plant indigenous to Southern Africa that has been widely evaluated for its use in the treatment of upper respiratory tract infections. In recent studies, the anti-proliferative potential of P. sidoides was shown, and several phenolic compounds were identified as the bioactive compounds. Little, however, is known regarding their anti-proliferative protein targets. In this study, the anti-proliferative mechanisms of P. sidoides through in silico target identification and network pharmacology methodologies were evaluated. The protein targets of the 12 phenolic compounds were identified using the target identification server PharmMapper and the server for predicting Drug Repositioning and Adverse Reactions via the Chemical-Protein Interactome (DRAR-CPI). Protein-protein and protein-pathway interaction networks were subsequently constructed with Cytoscape 3.4.0 to evaluate potential mechanisms of action. A total of 142 potential human target proteins were identified with the in silico target identification servers, and 90 of these were found to be related to cancer. The protein interaction network was constructed from 86 proteins involved in 209 interactions with each other, and two protein clusters were observed. A pathway enrichment analysis identified over 80 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched with the protein targets and included several pathways specifically related to cancer as well as various signaling pathways that have been found to be dysregulated in cancer. These results indicate that the anti-proliferative activity of P. sidoides may be multifactorial and arises from the collective regulation of several interconnected cell signaling pathways.

In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences

Analysis of drug–target interactions (DTIs) is of great importance in developing new drug candidates for known protein targets or discovering new targets for old drugs. However, the experimental approaches for identifying DTIs are expensive, laborious and challenging. In this study, we report a novel computational method for predicting DTIs using the highly discriminative information of drug-target interactions and our newly developed discriminative vector machine (DVM) classifier. More specifically, each target protein sequence is transformed as the position-specific scoring matrix (PSSM), in which the evolutionary information is retained; then the local binary pattern (LBP) operator is used to calculate the LBP histogram descriptor. For a drug molecule, a novel fingerprint representation is utilized to describe its chemical structure information representing existence of certain functional groups or fragments. When applying the proposed method to the four datasets (Enzyme, GPCR, Ion Channel and Nuclear Receptor) for predicting DTIs, we obtained good average accuracies of 93.16%, 89.37%, 91.73% and 92.22%, respectively. Furthermore, we compared the performance of the proposed model with that of the state-of-the-art SVM model and other previous methods. The achieved results demonstrate that our method is effective and robust and can be taken as a useful tool for predicting DTIs.

SELF-BLM: Prediction of drug-target interactions via self-training SVM

Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such as the bipartite local model (BLM), show promise, they often categorize unknown interactions as negative interaction. Therefore, these methods are not ideal for finding potential drug-target interactions that have not yet been validated as positive interactions. Thus, here we propose a method that integrates machine learning techniques, such as self-training support vector machine (SVM) and BLM, to develop a self-training bipartite local model (SELF-BLM) that facilitates the identification of potential interactions. The method first categorizes unlabeled interactions and negative interactions among unknown interactions using a clustering method. Then, using the BLM method and self-training SVM, the unlabeled interactions are self-trained and final local classification models are constructed. When applied to four classes of proteins that include enzymes, G-protein coupled receptors (GPCRs), ion channels, and nuclear receptors, SELF-BLM showed the best performance for predicting not only known interactions but also potential interactions in three protein classes compare to other related studies. The implemented software and supporting data are available at https://github.com/GIST-CSBL/SELF-BLM.

Pred-binding: large-scale protein-ligand binding affinity prediction

Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently, experimental determination of compound-protein interactions remains challenging because of funding investment and difficulties of purifying proteins. In this study, we proposed two in silico models based on support vector machine (SVM) and random forest (RF), using 1589 molecular descriptors and 1080 protein descriptors in 9948 ligand-protein pairs to predict DTIs that were quantified by Ki values. The cross-validation coefficient of determination of 0.6079 for SVM and 0.6267 for RF were obtained, respectively. In addition, the two-dimensional (2D) autocorrelation, topological charge indices and three-dimensional (3D)-MoRSE descriptors of compounds, the autocorrelation descriptors and the amphiphilic pseudo-amino acid composition of protein are found most important for Ki predictions. These models provide a new opportunity for the prediction of ligand-receptor interactions that will facilitate the target discovery and toxicity evaluation in drug development.

Drug-symptom networking: Linking drug-likeness screening to drug discovery

Understanding the relationships between drugs and symptoms has broad medical consequences, yet a comprehensive description of the drug-symptom associations is currently lacking. Here, 1441 FDA-approved drugs were collected, and PCA was used to extract 122 descriptors which explained 91% of the variance. Then, a k-means++ method was employed to partition the drug dataset into 3 clusters, and 3 corresponding SVDD models (drug-likeness screening models) were constructed with an overall accuracy of up to 95.6%. Furthermore, 6878 herbal molecules from the TcmSP™ database were screened by the above 3 SVDD model to obtain 5309 candidate drug molecules with highly accept classification of 77.19%. To assess the accuracy of the SVDD models, 8559 herbal molecule-symptom co-occurrences were mined from Pubmed abstracts, involving 697 herbal molecules and 314 symptoms. Most of the 697 herbal molecules could be found in the accepted SVDD data (5309 molecules), showing the potential of the SVDD for the screening of drug candidates. Moreover, a herbal molecule-herbal molecule network and a herbal molecule-symptom were constructed. Overall, the results provided a new drug-likeness screening approach independent to abnormal training data, and the comprehensive collection of herbal molecule-symptom associations formed a new data resource for systematic characterization of the symptom-oriented medicines.

Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features

Drugtarget interactions (DTIs) are central to current drug discovery processes. Efforts have been devoted to the development of methodology for predicting DTIs and drugtarget interaction networks. Most existing methods mainly focus on the application of information about drug or protein structure features. In the present work, we proposed a computational method for DTI prediction by combining the information from chemical, biological and network properties. The method was developed based on a learning algorithm-random forest (RF) combined with integrated features for predicting DTIs. Four classes of drugtarget interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models. The RF models gave prediction accuracy of 93.52 %, 94.84 %, 89.68 % and 84.72 % for four pharmaceutically useful datasets, respectively. The prediction ability of our approach is comparative to or even better than that of other DTI prediction methods. These comparative results demonstrated the relevance of the network topology as source of information for predicting DTIs. Further analysis confirmed that among our top ranked predictions of DTIs, several DTIs are supported by databases, while the others represent novel potential DTIs. We believe that our proposed approach can help to limit the search space of DTIs and provide a new way towards repositioning old drugs and identifying targets.

Synthetic oxoisoaporphine alkaloids: in vitro, in vivo and in silico assessment of antileishmanial activities

Leishmaniasis is a growing health problem worldwide. As there are certain drawbacks with the drugs currently used to treat human leishmaniasis and resistance to these drugs is emerging, there is a need to develop novel antileishmanial compounds, among which isoquinoline alkaloids are promising candidates. In this study, 18 novel oxoisoaporphine derivatives were synthesized and their possible antileishmanial activity was evaluated. The in vitro activity of these derivatives against Leishmania amazonensis axenic amastigotes was first evaluated, and the selected compounds were then tested in an inhibition assay with promastigotes of L. infantum, L. braziliensis, L. amazonensis and L. guyanensis, and with intracellular amastigotes of L. infantum and L. amazonensis. Finally, the most active compounds, OXO 1 (2,3-dihydro-7H-dibenzo[de,h]quinolin-7-one) and OXO 13 (2,3,8,9,10,11-hexahydro-7H-dibenzo[de,h]quinolin-7-one), were tested in BALB/c mice infected with L. infantum. Treatment of mice at a dose of 10 mg/kg with OXO 1 yielded significant reductions (p<0.05) in parasite burden in liver and spleen (99% and 78%, respectively) whereas with OXO 13 were not significant. Although previous reports suggest that this family of molecules displays inhibitory activity against monoamine oxidase A and acetylcholinesterase, these enzymes were not confirmed as targets for antileishmanial activity on the basis of the present results. However, after development of a new bioinformatics model to analyze the Leishmania proteome, we were able to identify other putative targets for these molecules. The most promising candidates were four proteins: two putative pteridine reductase 2 (1MXF and 1MXH), one N-myristoyltransferase (2WUU) and one type I topoisomerase (2B9S).

S. Cordeiro MN, Yañez M, González-Díaz H, García-Mera X. Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates

The disappointing results obtained in recent clinical trials renew the interest in experimental/computational techniques for the discovery of neuroprotective drugs. In this context, multitarget or multiplexing QSAR models (mt-QSAR/mx-QSAR) may help to predict neurotoxicity/neuroprotective effects of drugs in multiple assays, on drug targets, and in model organisms. In this work, we study a data set downloaded from CHEMBL; each data point (>8000) contains the values of one out of 37 possible measures of activity, 493 assays, 169 molecular or cellular targets, and 11 different organisms (including human) for a given compound. In this work, we introduce the first mx-QSAR model for neurotoxicity/neuroprotective effects of drugs based on the MARCH-INSIDE (MI) method. First, we used MI to calculate the stochastic spectral moments (structural descriptors) of all compounds. Next, we found a model that classified correctly 2955 out of 3548 total cases in the training and validation series with Accuracy, Sensitivity, and Specificity values>80%. The model also showed excellent results in Computational-Chemistry simulations of High-Throughput Screening (CCHTS) experiments, with accuracy=90.6% for 4671 positive cases. Next, we reported the synthesis, characterization, and experimental assays of new rasagiline derivatives. We carried out three different experimental tests: assay (1) in the absence of neurotoxic agents, assay (2) in the presence of glutamate, and assay (3) in the presence of H2O2. Compounds 11 with 27.4%, 8 with 11.6%, and 9 with 15.4% showed the highest neuroprotective effects in assays (1), (2), and (3), respectively. After that, we used the mx-QSAR model to carry out a CCHTS of the new compounds in >400 unique pharmacological tests not carried out experimentally. Consequently, this model may become a promising auxiliary tool for the discovery of new drugs for the treatment of neurodegenerative diseases.

Predicting network of drug-enzyme interaction based on machine learning method

It is important to correctly and efficiently map drugs and enzymes to their possible interaction network in modern drug research. In this work, a novel approach was introduced to encode drug and enzyme molecules with physicochemical molecular descriptors and pseudo amino acid composition, respectively. Based on this encoding method, Random Forest was adopted to build the drug-enzyme interaction network. After selecting the optimal features that are able to represent the main factors of drug-enzyme interaction in our prediction, a total of 129 features were attained which can be clustered into nine categories: Elemental Analysis, Geometry, Chemistry, Amino Acid Composition, Secondary Structure, Polarity, Molecular Volume, Codon Diversity and Electrostatic Charge. It is further found that Geometry features were the most important of all the features. As a result, our predicting model achieved an MCC of 0.915 and a sensitivity of 87.9% at the specificity level of 99.8% for 10-fold cross-validation test, and achieved an MCC of 0.895 and a sensitivity of 95.7% at the specificity level of 95.4% for independent set test. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

Large-scale prediction of human kinase-inhibitor interactions using protein sequences and molecular topological structures

The kinase family is one of the largest target families in the human genome. The family's key function in signal transduction for all organisms makes it a very attractive target class for the therapeutic interventions in many diseases states such as cancer, diabetes, inflammation and arthritis. A first step toward accelerating kinase drug discovery process is to fast identify whether a chemical and a kinase interact or not. Experimentally, these interactions can be identified by in vitro binding assay - an expensive and laborious procedure that is not applicable on a large scale. Therefore, there is an urgent need to develop statistically efficient approaches for identifying kinase-inhibitor interactions. For the first time, the quantitative binding affinities of kinase-inhibitor pairs are differentiated as a measurement to define if an inhibitor interacts with a kinase, and then a chemogenomics framework using an unbiased set of general integrated features (drug descriptors and protein descriptors) and random forest (RF) is employed to construct a predictive model which can accurately classify kinase-inhibitor pairs. Our results show that RF with integrated features gave prediction accuracy of 93.76%, sensitivity of 92.26%, and specificity of 95.27%, respectively. The results are superior to those by only considering two separated spaces (chemical space and protein space), demonstrating that these integrated features contribute cooperatively. Based on the constructed model, we provided a high confidence list of drug-target associations for subsequent experimental investigation guidance at a low false discovery rate.

Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it

Despite the sequencing of the human genome, the rate of innovative and successful drug discovery in the pharmaceutical industry has continued to decrease. Leaving aside regulatory matters, the fundamental and interlinked intellectual issues proposed to be largely responsible for this are: (a) the move from 'function-first' to 'target-first' methods of screening and drug discovery; (b) the belief that successful drugs should and do interact solely with single, individual targets, despite natural evolution's selection for biochemical networks that are robust to individual parameter changes; (c) an over-reliance on the rule-of-5 to constrain biophysical and chemical properties of drug libraries; (d) the general abandoning of natural products that do not obey the rule-of-5; (e) an incorrect belief that drugs diffuse passively into (and presumably out of) cells across the bilayers portions of membranes, according to their lipophilicity; (f) a widespread failure to recognize the overwhelmingly important role of proteinaceous transporters, as well as their expression profiles, in determining drug distribution in and between different tissues and individual patients; and (g) the general failure to use engineering principles to model biology in parallel with performing 'wet' experiments, such that 'what if?' experiments can be performed in silico to assess the likely success of any strategy. These facts/ideas are illustrated with a reasonably extensive literature review. Success in turning round drug discovery consequently requires: (a) decent systems biology models of human biochemical networks; (b) the use of these (iteratively with experiments) to model how drugs need to interact with multiple targets to have substantive effects on the phenotype; (c) the adoption of polypharmacology and/or cocktails of drugs as a desirable goal in itself; (d) the incorporation of drug transporters into systems biology models, en route to full and multiscale systems biology models that incorporate drug absorption, distribution, metabolism and excretion; (e) a return to 'function-first' or phenotypic screening; and (f) novel methods for inferring modes of action by measuring the properties on system variables at all levels of the 'omes. Such a strategy offers the opportunity of achieving a state where we can hope to predict biological processes and the effect of pharmaceutical agents upon them. Consequently, this should both lower attrition rates and raise the rates of discovery of effective drugs substantially.

ChemoPy: freely available python package for computational biology and chemoinformatics

MOTIVATION

Molecular representation for small molecules has been routinely used in QSAR/SAR, virtual screening, database search, ranking, drug ADME/T prediction and other drug discovery processes. To facilitate extensive studies of drug molecules, we developed a freely available, open-source python package called chemoinformatics in python (ChemoPy) for calculating the commonly used structural and physicochemical features. It computes 16 drug feature groups composed of 19 descriptors that include 1135 descriptor values. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pairs fingerprints, topological torsion fingerprints and Morgan/circular fingerprints. By applying a semi-empirical quantum chemistry program MOPAC, ChemoPy can also compute a large number of 3D molecular descriptors conveniently.

AVAILABILITY

The python package, ChemoPy, is freely available via http://code.google.com/p/pychem/downloads/list, and it runs on Linux and MS-Windows.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Host-guest interaction between new nitrooxoisoaporphine and β-cyclodextrins: synthesis, electrochemical, electron spin resonance and molecular modeling studies

A new nitrooxoisoaporphine derivative was synthetized and characterized by cyclic voltammetry and electron spin resonance. Its aqueous solubility was improved by complexes formation with β-cyclodextrin, heptakis(2,6-di-O-methyl)-β-cyclodextrin and (2-hydroxypropyl)-β-cyclodextrin. In order to assess the inclusion degree reached by nitrooxoisoaporphine in cyclodextris cavity, the stability constants of formation of the complexes were determined by phase-solubility measurements obtaining in all cases a type-A(L) diagram. Moreover, electrochemical studies were carried out, where the observed change in the EPC value indicated a lower feasibility of the nitro group reduction. Additionally, a detailed spatial configuration is proposed for inclusion of derivate within the cyclodextrins cavity by 2D NMR techniques. Finally, these results are further interpreted by means of molecular modeling studies. Thus, theoretical results are in complete agreement with the experimental data.

The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so

A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).

Multi-targeted therapy for leprosy: insilico strategy to overcome multi drug resistance and to improve therapeutic efficacy

Leprosy remains a major public health problem, since single and multi-drug resistance has been reported worldwide over the last two decades. In the present study, we report the novel multi-targeted therapy for leprosy to overcome multi drug resistance and to improve therapeutic efficacy. If multiple enzymes of an essential metabolic pathway of a bacterium were targeted, then the therapy would become more effective and can prevent the occurrence of drug resistance. The MurC, MurD, MurE and MurF enzymes of peptidoglycan biosynthetic pathway were selected for multi targeted therapy. The conserved or class specific active site residues important for function or stability were predicted using evolutionary trace analysis and site directed mutagenesis studies. Ten such residues which were present in at least any three of the four Mur enzymes (MurC, MurD, MurE and MurF) were identified. Among the ten residues G125, K126, T127 and G293 (numbered based on their position in MurC) were found to be conserved in all the four Mur enzymes of the entire bacterial kingdom. In addition K143, T144, T166, G168, H234 and Y329 (numbered based on their position in MurE) were significant in binding substrates and/co-factors needed for the functional events in any three of the Mur enzymes. These are the probable residues for designing newer anti-leprosy drugs in an attempt to reduce drug resistance.

A ligand-based approach for the in silico discovery of multi-target inhibitors for proteins associated with HIV infection

Acquired immunodeficiency syndrome (AIDS) is a dangerous disease, which damages the immune system cells to the point that the immune system can no longer fight against other infections that it would usually be able to prevent. The causal agent is the human immunodeficiency virus (HIV), and for this reason, the search for more effective chemotherapies against HIV is a challenge for the scientific community. Chemoinformatics and Quantitative Structure-Activity Relationship (QSAR) studies have played an essential role in the design of potent inhibitors for proteins associated with the HIV infection. However, all previous studies took into consideration the discovery of future drug candidates using homogeneous series of compounds against only one protein. This fact limits the use of more efficient anti-HIV chemotherapies. In this work, we develop the first ligand-based approach for the in silico design of multi-target (mt) inhibitors for seven key proteins associated with the HIV infection. Two mt-QSAR models were constructed from a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors. The second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted and their contributions to anti-HIV activity through inhibition of the different proteins were calculated using the mt-QSAR-LDA model. New molecules designed from fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-HIV agents.

Molecular topology as a novel approach for drug discovery

INTRODUCTION

Molecular topology (MT) has emerged in recent years as a powerful approach for the in silico generation of new drugs. One key part of MT is that, in the process of drug design/discovery, there is no need for an explicit knowledge of a drug's mechanism of action unlike other drug discovery methods.

AREAS COVERED

In this review, the authors introduce the topic by explaining briefly the most common methodology used today in drug design/discovery and address the most important concepts of MT and the methodology followed (QSAR equations, LDA, etc.). Furthermore, the significant results achieved, from this approach, are outlined and discussed.

EXPERT OPINION

The results outlined herein can be explained by considering that MT represents a new paradigm in the field of drug design. This means that it is not only an alternative method to the conventional methods, but it is also independent, that is, it represents a pathway to connect directly molecular structure with the experimental properties of the compounds (particularly drugs). Moreover, the process can be realized also in the reverse pathway, that is, designing new molecules from their topological pattern, what opens almost limitless expectations in new drugs development, given that the virtual universe of molecules is much greater than that of the existing ones.