Multi-target Quantitative Structure-activity Relationship Research Articles

DRDB: A Machine Learning Platform to Predict Chemical-Protein Interactions towards Diabetic Retinopathy.

Diabetic retinopathy (DR), a diabetic microangiopathy caused by diabetes, affects approximately 93 million people, worldwide. However, the drugs used to treat DR have limited efficacy and the variety of side effects. This is possibly because the complicated pathogenesis of DR is associated with multiple proteins. In this work, we attempted to identify potential drugs against DR-associated proteins and predict potential targets for drugs using in silico prediction of chemical-protein interactions (CPI) based on multitarget quantitative structure-activity relationship (mt-QSAR) method. Therefore, we developed 128 binary classifiers to predict the CPI for 15 DR targets using random forest (RF), k-nearest neighbours (KNN), support vector machine (SVM), and neural network (NN) algorithms with MACCS, extended connectivity fingerprints (ECFP6) fingerprints, and protein descriptors. In order to facilitate discovery of the novel drugs and target identification using the 128 binary classifiers, a free web server (DRDB) was developed. Compound Danshen Dripping Pills (CDDP), composed of Salvia miltiorrhiza, Panax notoginseng, and borneol, is commonly used in the treatment of cardiovascular diseases. To explore the applicability of DRDB, the potential CPIs of CDDP in treatment of DR were investigated based on DRDB. In vitro experimental validation demonstrated that cryptotanshinone and protocatechuic acid, two key components of CDDP, are capable of targeting ICAM-1 which is one of the key target of DR. We hope that this work can facilitate development of more effective clinical strategies for the treatment of DR.

First multi-target QSAR model for predicting the cytotoxicity of acrylic acid-based dental monomers

ObjectiveAcrylic acid derivatives are frequently used as dental monomers and their cytotoxicity towards various cell lines is well documented. This study aims to probe the structural and physicochemical attributes responsible for higher toxicity of dental monomers, using quantitative structure-activity relationships (QSAR) modeling approaches. MethodsA regression-based linear single-target QSAR (st-QSAR) model was developed with a comparatively small dataset containing 39 compounds, the cytotoxicity of which has been assessed over the Hela S3 cell line. By contrast, a classification-based multi-target QSAR model was developed with 138 compounds, the cytotoxicity of which has been reported against 18 different cell lines. Both models were set up following rigorous validation protocols confirming their statistical significance and robustness. ResultsThe performance of the linear mt-QSAR model, developed with various feature selection and post-selection similarity searching-based schemes, superseded that of all non-linear models produced with six machine learning methods by hyperparameter optimization. The final derived st-QSAR and mt-QSAR linear models are shown to be highly predictive, as well as revealing the crucial structural and physicochemical factors responsible for higher cytotoxicity of the dental monomers. SignificanceThis study is the first attempt on unveiling the cytotoxicity of dental monomers over several cell lines by means of a single multi-target QSAR model. Further, such a model is ready to get widespread applicability in the screening of new monomers, judging from its almost accurate predictions over diverse experimental assay conditions.

QSAR-Co-X: an open source toolkit for multitarget QSAR modelling

Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python–based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery.

AKT, is a serine/threonine protein kinase comprising three isoforms—namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT’ inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.

Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents.

Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure–activity relationship (mt-QSAR) models based on the Box–Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.

Multi-target QSAR modelling of chemo-genomic data analysis based on Extreme Learning Machine

This paper presents a new Quantitative Structure-Activity Relationship (QSAR) model based on Extreme Learning Machine (ELM) to predict the biological activity of the benchmark Escape-Data sets compounds in order to provide an effective learning solution for regression analysis. The pre-processing phase of this model has been performed for the chemo-genomics datasets using the k-Nearest Neighbours (k-NN) algorithm to predict missing values of the dataset. In the second phase, the Genetic algorithm hybrid with Binary Whale Optimization algorithm (GBWOA) is adapted to determine the significance and the optimized features in feature selection phase. The min–max method is used in the third phase to transform all features to binary form in order to increases the efficiency of the proposed model by smoothing the data points and reducing fluctuation among features. ELM is used in the final phase as regression algorithm to predict chemo-genomics chemical compound. Different experiments have been performed in this paper on datasetwhich has been collected from ExCAPE chemo-genomics database project composed of 43509 compounds, 1134 targets besides biological activity and 40 chemical descriptors. The experimental results show that the proposed model is efficient in improving the level of prediction based on some statistical measurements. Also, ELM produced satisfactory results when the number of hidden nodes is greater than or equal to 1000 L. Moreover, the proposed model achieved high accuracy using R2 measure (≈0.971) which outperforms the other algorithms in literature such as (WOA, ALO, BAT and CSA) with accuracies (≈0.673, ≈0.753, ≈0.680, and ≈0.897) respectively. In addition, the docking results succeeded in validating the current QSAR model. In the current research, 41686 (95.81%) compounds are lead compound and 36965 (84.95%) compounds are a candidate for multi-target genes.

Computational Approaches Towards Kinases as Attractive Targets for Anticancer Drug Discovery and Development.

One of the major goals of computational chemists is to determine and develop the pathways for anticancer drug discovery and development. In recent past, high performance computing systems elicited the desired results with little or no side effects. The aim of the current review is to evaluate the role of computational chemistry in ascertaining kinases as attractive targets for anticancer drug discovery and development. Research related to computational studies in the field of anticancer drug development is reviewed. Extensive literature on achievements of theorists in this regard has been compiled and presented with special emphasis on kinases being the attractive anticancer drug targets. Different approaches to facilitate anticancer drug discovery include determination of actual targets, multi-targeted drug discovery, ligand-protein inverse docking, virtual screening of drug like compounds, formation of di-nuclear analogs of drugs, drug specific nano-carrier design, kinetic and trapping studies in drug design, multi-target QSAR (Quantitative Structure Activity Relationship) model, targeted co-delivery of anticancer drug and siRNA, formation of stable inclusion complex, determination of mechanism of drug resistance, and designing drug like libraries for the prediction of drug-like compounds. Protein kinases have gained enough popularity as attractive targets for anticancer drugs. These kinases are responsible for uncontrolled and deregulated differentiation, proliferation, and cell signaling of the malignant cells which result in cancer. Interest in developing drugs through computational methods is a growing trend, which saves equally the cost and time. Kinases are the most popular targets among the other for anticancer drugs which demand attention. 3D-QSAR modelling, molecular docking, and other computational approaches have not only identified the target-inhibitor binding interactions for better anticancer drug discovery but are also designing and predicting new inhibitors, which serve as lead for the synthetic preparation of drugs. In light of computational studies made so far in this field, the current review highlights the importance of kinases as attractive targets for anticancer drug discovery and development.

QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models.

Quantitative structure-activity relationships (QSAR) modeling is a well-known computational technique with wide applications in fields such as drug design, toxicity predictions, nanomaterials, etc. However, QSAR researchers still face certain problems to develop robust classification-based QSAR models, especially while handling response data pertaining to diverse experimental and/or theoretical conditions. In the present work, we have developed an open source standalone software "QSAR-Co" (available to download at https://sites.google.com/view/qsar-co ) to setup classification-based QSAR models that allow mining the response data coming from multiple conditions. The software comprises two modules: (1) the Model development module and (2) the Screen/Predict module. This user-friendly software provides several functionalities required for developing a robust multitasking or multitarget classification-based QSAR model using linear discriminant analysis or random forest techniques, with appropriate validation, following the principles set by the Organisation for Economic Co-operation and Development (OECD) for applying QSAR models in regulatory assessments.

AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease.

Alzheimer's disease (AD) is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs) to delay disease progression. The in silico prediction of chemical-protein interactions (CPI) can accelerate target identification and drug discovery. Previously, we developed 100 binary classifiers to predict the CPI for 25 key targets against AD using the multi-target quantitative structure-activity relationship (mt-QSAR) method. In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD. Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB) and recursive partitioning (RP) algorithms. The internal 5-fold cross-validation and external test set validation were applied to evaluate the performance of the training sets and test set, respectively. The area under the receiver operating characteristic curve (ROC) for the test sets ranged from 0.629 to 1.0, with an average of 0.903. In addition, we developed a web server named AlzhCPI to integrate the comprehensive information of approximately 204 binary classifiers, which has potential applications in network pharmacology and drug repositioning. AlzhCPI is available online at http://rcidm.org/AlzhCPI/index.html. To illustrate the applicability of AlzhCPI, the developed system was employed for the systems pharmacology-based investigation of shichangpu against AD to enhance the understanding of the mechanisms of action of shichangpu from a holistic perspective.

In Silico Studies Most Employed in the Discovery of New Antimicrobial Agents.

The present review summarizes the methods most used in drug search and design, which may help to keep pace with the growing antibiotic resistance among pathogens. The rate of reduction in the effectiveness of many antimicrobial medications, caused by this resistance, is faster than new drug development, thereby creating a worldwide public health threat. Among the scientific community, the urgency of finding new drugs is peaking interest in the use of in silico studies to explore the interaction of compounds with target receptors. With this approach, small molecules (designed or retrieved from data bases) are tested with computer-aided molecular simulation to explore their efficacy. That is, ligand-protein complexes are constructed and evaluated via virtual screening (VS), molecular dynamics (MD), and docking simulations with the data from the physical, chemical and pharmacological properties of such molecules. Additionally, the application of quantitative structure-activity relationship (QSAR), multi-target quantitative structure-activity relationship (mt- QSAR), and multi-tasking quantitative structure-biological effect (mtk-QSBER) can be enhanced by principal component analysis and systematic workflows. These types of studies aid in selecting a group of promising molecules with high potency and selectivity as well as low toxicity, thus making in vitro and in vivo (animal model) testing more efficient. Since knowledge of the receptor topography and receptor-ligand interactions has yielded promising compounds and effective drugs, there is now no doubt that the use of in silico tools can lead to more rapid validation of new potential drugs for preclinical studies and clinical trials.

Recent Advances in Multi-Task QSAR Modeling for Drug Design

Computer-aided drug design may provide a massive scope to reduce the overall duration as well as the expenditure associated with drug discovery. Although the concept of Quantitative Structure Activity Relationship (QSAR) is almost fifty years old, it is still considered as a very useful method for finding novel therapeutic agents. In the last decade, the field of QSAR has witnessed great advances in terms of techniques, ideas and approaches. 3 Multi-task QSAR is one such approach that has a huge opportunity to contribute in the discovery of novel therapeutic agents. Classical QSAR techniques focus on the classical idea that one drug is aimed to act on one target. However, due to the highly complex and multi-factorial nature of some diseases like cancer, microbial infections, neurodegenerative and metabolic disorders, the classical idea of drug development frequently produces less satisfying effects. The effect of the inhibition of one specific target is compensated by different back-up or fail-safe mechanisms. Polypharmacology focuses on the concept of simultaneous inhibition of multiple targets by single therapeutic agents. Contrary to single target drugs that bind with a specific target with high affinity, multi-target agents are low affinity binders. Therefore, management of therapy is easier for the multi-target agents. Multi-task QSAR techniques are able to incorporate the ideas of polypharmacology. While conventional QSAR techniques are based on the datasets of compounds with a single target and same or similar assay conditions, multi-task QSAR can consider data on multiple targets and various assay conditions at a time. In addition, multi-task QSAR techniques are also useful to address the problems of fewer datapoints. QSAR is a statistical technique and a higher amount of data-points generates more reliable models. However, for several diseases the availability of a data is less and developed models on the available data may not be able to provide detailed insight into structural requirements of the molecules for higher potency. As multi-task QSAR models may be developed on the data collected from different assay conditions, more information may be retrieved for the explanation of structural patterns. Therefore, multi-task QSAR has emerged as one of the most essential tools in the last few years and various groups of researchers participated in the development of multi-task QSAR models. Though conventional 2D QSAR techniques have found several alternatives such as 3D QSAR, pharmacophore mapping as well as molecular docking, 2D-QSAR is unique in handling problems of multi-task modeling. Multi-task QSAR has been utilized for the developing models for anti-cancer, anti-bacterial 17 and neuroprotective agents. In addition, reports on multi-target modeling on kinase and ubiquitineproteosome pathways are also found. Furthermore, multi-task agents are also useful for understanding structural patterns required for toxicities of nanoparticles, environmental pollutants etc. Although development of multi-task QSAR model is quite similar to conventional modeling as far as descriptor calculation and model generation are concerned, the construction and management of a dataset is quite different. In most of the cases, classification models are generated with the data by suitable machine learning tools such as Linear Discriminant Analyses (LDA), Artificial Neural Network (ANN), Support Vector Machine (SVM) etc. Generally, a specific cut-off value for each system is fixed by the user to classify the data into ‘sensitive, and ‘non-sensitive’. Sometimes special statistical treatments, like averaging the descriptor values for specific system and calculation of moving average, are required for handling the complicated data. Some attempts were taken to develop regression based multi-target models. However, there still is a debate about the actual effectiveness of multi-target QSAR modeling as the size of a data set is sometimes so increased that the model may not be properly validated by conventional approaches. Moreover, there are concerns over applicability domain of the multitask models. Nevertheless, multi-task QSAR models may provide opportunity to widen the area of QSAR studies as well as to contribute to the investigation of different emerging areas such as nanotoxicity, polypharmacology, drug repositioning, environmental safety management etc.

Multi-Target QSAR Approaches for Modeling Protein Inhibitors. Simultaneous Prediction of Activities Against Biomacromolecules Present in Gram-Negative Bacteria.

Drug discovery is aimed at finding therapeutic agents for the treatment of many diverse diseases and infections. However, this is a very slow an expensive process, and for this reason, in silico approaches are needed to rationalize the search for new molecular entities with desired biological profiles. Models focused on quantitative structure-activity relationships (QSAR) have constituted useful complementary tools in medicinal chemistry, allowing the virtual predictions of dissimilar pharmacological activities of compounds. In the last 10 years, multi-target (mt) QSAR models have been reported, representing great advances with respect to those models generated from classical approaches. Thus, mt- QSAR models can simultaneously predict activities against different biological targets (proteins, microorganisms, cell lines, etc.) by using large and heterogeneous datasets of chemicals. The present review is devoted to discuss the most promising mt-QSAR models, particularly those developed for the prediction of protein inhibitors. We also report the first multi-tasking QSAR (mtk-QSAR) model for simultaneous prediction of inhibitors against biomacromolecules (specifically proteins) present in Gram-negative bacteria. This model allowed us to consider both different proteins and multiple experimental conditions under which the inhibitory activities of the chemicals were determined. The mtk-QSAR model exhibited accuracies higher than 98% in both training and prediction sets, also displaying a very good performance in the classification of active and inactive cases that depended on the specific elements of the experimental conditions. The physicochemical interpretations of the molecular descriptors were also analyzed, providing important insights regarding the molecular patterns associated with the appearance/enhancement of the inhibitory potency.

Discovery of multitarget-directed ligands against Alzheimer's disease through systematic prediction of chemical-protein interactions.

To determine chemical-protein interactions (CPI) is costly, time-consuming, and labor-intensive. In silico prediction of CPI can facilitate the target identification and drug discovery. Although many in silico target prediction tools have been developed, few of them could predict active molecules against multitarget for a single disease. In this investigation, naive Bayesian (NB) and recursive partitioning (RP) algorithms were applied to construct classifiers for predicting the active molecules against 25 key targets toward Alzheimer's disease (AD) using the multitarget-quantitative structure-activity relationships (mt-QSAR) method. Each molecule was initially represented with two kinds of fingerprint descriptors (ECFP6 and MACCS). One hundred classifiers were constructed, and their performance was evaluated and verified with internally 5-fold cross-validation and external test set validation. The range of the area under the receiver operating characteristic curve (ROC) for the test sets was from 0.741 to 1.0, with an average of 0.965. In addition, the important fragments for multitarget against AD given by NB classifiers were also analyzed. Finally, the validated models were employed to systematically predict the potential targets for six approved anti-AD drugs and 19 known active compounds related to AD. The prediction results were confirmed by reported bioactivity data and our in vitro experimental validation, resulting in several multitarget-directed ligands (MTDLs) against AD, including seven acetylcholinesterase (AChE) inhibitors ranging from 0.442 to 72.26 μM and four histamine receptor 3 (H3R) antagonists ranging from 0.308 to 58.6 μM. To be exciting, the best MTDL DL0410 was identified as an dual cholinesterase inhibitor with IC50 values of 0.442 μM (AChE) and 3.57 μM (BuChE) as well as a H3R antagonist with an IC50 of 0.308 μM. This investigation is the first report using mt-QASR approach to predict chemical-protein interaction for a single disease and discovering highly potent MTDLs. This protocol may be useful for in silico multitarget prediction of other diseases.

Unified Multi-target Approach for the Rational in silico Design of Anti-bladder Cancer Agents

Bladder cancer (BLC) is a very dangerous and common disease which is characterized by an uncontrolled growth of the urinary bladder cells. In the field of chemotherapy, many compounds have been synthesized and evaluated as anti-BLC agents. The future design of more potent anti-BLC drugs depends on a rigorous and rational discovery, where the computer-aided design (CADD) methodologies should play a very important role. However, until now, there is no CADD methodology able to predict anti-BLC activity of compounds versus different BLC cell lines. We report in this work the first unified approach by exploring Quantitative- Structure Activity Relationship (QSAR) studies using a large and heterogeneous database of compounds. Here, we constructed two multi-target (mt) QSAR models for the classification of compounds as anti-BLC agents against four BLC cell lines. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while 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. We also extracted different substructural patterns which could be responsible for the activity/inactivity of molecules against BLC and we suggested new molecular entities as possible potent and versatile anti-BLC agents.

3D MI-DRAGON: New Model for the Reconstruction of US FDA Drug- Target Network and Theoretical-Experimental Studies of Inhibitors of Rasagiline Derivatives for AChE

The number of neurodegenerative diseases has been increasing in recent years. Many of the drug candidates to be used in the treatment of neurodegenerative diseases present specific 3D structural features. An important protein in this sense is the acetylcholinesterase (AChE), which is the target of many Alzheimer's dementia drugs. Consequently, the prediction of Drug-Protein Interactions (DPIs/nDPIs) between new drug candidates and specific 3D structure and targets is of major importance. To this end, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out a rational DPIs prediction. Unfortunately, many previous QSAR models developed to predict DPIs take into consideration only 2D structural information and codify the activity against only one target. To solve this problem we can develop some 3D multi-target QSAR (3D mt-QSAR) models. In this study, using the 3D MI-DRAGON technique, we have introduced a new predictor for DPIs based on two different well-known software. We have used the MARCH-INSIDE (MI) and DRAGON software to calculate 3D structural parameters for drugs and targets respectively. Both classes of 3D parameters were used as input to train Artificial Neuronal Network (ANN) algorithms using as benchmark dataset the complex network (CN) made up of all DPIs between US FDA approved drugs and their targets. The entire dataset was downloaded from the DrugBank database. The best 3D mt-QSAR predictor found was an ANN of Multi-Layer Perceptron-type (MLP) with profile MLP 37:37-24-1:1. This MLP classifies correctly 274 out of 321 DPIs (Sensitivity = 85.35%) and 1041 out of 1190 nDPIs (Specificity = 87.48%), corresponding to training Accuracy = 87.03%. We have validated the model with external predicting series with Sensitivity = 84.16% (542/644 DPIs; Specificity = 87.51% (2039/2330 nDPIs) and Accuracy = 86.78%. The new CNs of DPIs reconstructed from US FDA can be used to explore large DPI databases in order to discover both new drugs and/or targets. We have carried out some theoretical-experimental studies to illustrate the practical use of 3D MI-DRAGON. First, we have reported the prediction and pharmacological assay of 22 different rasagiline derivatives with possible AChE inhibitory activity. In this work, we have reviewed different computational studies on Drug- Protein models. First, we have reviewed 10 studies on DP computational models. Next, we have reviewed 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking with different compounds to find Drug-Protein QSAR models. Last, we have developped a 3D multi-target QSAR (3D mt-QSAR) models for the prediction of the activity of new compounds against different targets or the discovery of new targets.

Rational drug design for anti-cancer chemotherapy: Multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents

The discovery of new and more potent anti-cancer agents constitutes one of the most active fields of research in chemotherapy. Colorectal cancer (CRC) is one of the most studied cancers because of its high prevalence and number of deaths. In the current pharmaceutical design of more efficient anti-CRC drugs, the use of methodologies based on Chemoinformatics has played a decisive role, including Quantitative-Structure–Activity Relationship (QSAR) techniques. However, until now, there is no methodology able to predict anti-CRC activity of compounds against more than one CRC cell line, which should constitute the principal goal. In an attempt to overcome this problem we develop here the first multi-target (mt) approach for the virtual screening and rational in silico discovery of anti-CRC agents against ten cell lines. Here, two mt-QSAR classification models were constructed using a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while 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 from the molecules and their contributions to anti-CRC activity were calculated using mt-QSAR-LDA model. Several fragments were identified as potential substructural features responsible for the anti-CRC activity and new molecules designed from those fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-CRC agents.

A Review of QSAR studies to Discover New Drug-like Compounds Actives Against Leishmaniasis and Trypanosomiasis

The neglected tropical diseases (NTDs) affect more than one billion people (one-sixth of the world's population) and occur primarily in undeveloped countries in sub-Saharan Africa, Asia, and Latin America. Available drugs for these diseases are decades old and present an important number of limitations, especially high toxicity and, more recently, the emergence of drug resistance. In the last decade several Quantitative Structure-Activity Relationship (QSAR) studies have been developed in order to identify new organic compounds with activity against the parasites responsible for these diseases, which are reviewed in this paper. The topics summarized in this work are: 1) QSAR studies to identify new organic compounds actives against Chaga's disease; 2) Development of QSAR studies to discover new antileishmanial drusg; 3) Computational studies to identify new drug-like compounds against human African trypanosomiasis. Each topic include the general characteristics, epidemiology and chemotherapy of the disease as well as the main QSAR approaches to discovery/identification of new actives compounds for the corresponding neglected disease. The last section is devoted to a new approach know as multi-target QSAR models developed for antiparasitic drugs specifically those actives against trypanosomatid parasites. At present, as a result of these QSAR studies several promising compounds, active against these parasites, are been indentify. However, more efforts will be required in the future to develop more selective (specific) useful drugs.

Prediction of chemical–protein interactions: multitarget-QSAR versus computational chemogenomic methods

Elucidation of chemical-protein interactions (CPI) is the basis of target identification and drug discovery. It is time-consuming and costly to determine CPI experimentally, and computational methods will facilitate the determination of CPI. In this study, two methods, multitarget quantitative structure-activity relationship (mt-QSAR) and computational chemogenomics, were developed for CPI prediction. Two comprehensive data sets were collected from the ChEMBL database for method assessment. One data set consisted of 81 689 CPI pairs among 50 924 compounds and 136 G-protein coupled receptors (GPCRs), while the other one contained 43 965 CPI pairs among 23 376 compounds and 176 kinases. The range of the area under the receiver operating characteristic curve (AUC) for the test sets was 0.95 to 1.0 and 0.82 to 1.0 for 100 GPCR mt-QSAR models and 100 kinase mt-QSAR models, respectively. The AUC of 5-fold cross validation were about 0.92 for both 176 kinases and 136 GPCRs using the chemogenomic method. However, the performance of the chemogenomic method was worse than that of mt-QSAR for the external validation set. Further analysis revealed that there was a high false positive rate for the external validation set when using the chemogenomic method. In addition, we developed a web server named CPI-Predictor, , which is available for free. The methods and tool have potential applications in network pharmacology and drug repositioning.

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

There are many pairs of possible Drug–Proteins Interactions that may take place or not (DPIs/nDPIs) between drugs with high affinity/non-affinity for different proteins. This fact makes expensive in terms of time and resources, for instance, the determination of all possible ligands–protein interactions for a single drug. In this sense, we can use Quantitative Structure–Activity Relationships (QSAR) models to carry out rational DPIs prediction. Unfortunately, almost all QSAR models predict activity against only one target. To solve this problem we can develop multi-target QSAR (mt-QSAR) models. In this work, we introduce the technique 2D MI-DRAGON a new predictor for DPIs based on two different well-known software. We use the software MARCH-INSIDE (MI) to calculate 3D structural parameters for targets and the software DRAGON was used to calculated 2D molecular descriptors all drugs showing known DPIs present in the Drug Bank (US FDA benchmark dataset). Both classes of parameters were used as input of different Artificial Neural Network (ANN) algorithms to seek an accurate non-linear mt-QSAR predictor. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 21:21-31-1:1. This MLP classifies correctly 303 out of 339 DPIs (Sensitivity = 89.38%) and 480 out of 510 nDPIs (Specificity = 94.12%), corresponding to training Accuracy = 92.23%. The validation of the model was carried out by means of external predicting series with Sensitivity = 92.18% (625/678 DPIs; Specificity = 90.12% (730/780 nDPIs) and Accuracy = 91.06%. 2D MI-DRAGON offers a good opportunity for fast-track calculation of all possible DPIs of one drug enabling us to re-construct large drug-target or DPIs Complex Networks (CNs). For instance, we reconstructed the CN of the US FDA benchmark dataset with 855 nodes 519 drugs + 336 targets). We predicted CN with similar topology (observed and predicted values of average distance are equal to 6.7 vs. 6.6). These CNs can be used to explore large DPIs databases in order to discover both new drugs and/or targets. Finally, we illustrated in one theoretic-experimental study the practical use of 2D MI-DRAGON. We reported the prediction, synthesis, and pharmacological assay of 10 different oxoisoaporphines with MAO-A inhibitory activity. The more active compound OXO5 presented IC 50 = 0.00083 μM, notably better than the control drug Clorgyline.

Multi-target QSAR modelling in the analysis and design of HIV-HCV co-inhibitors: an in-silico study

BackgroundHIV and HCV infections have become the leading global public-health threats. Even more remarkable, HIV-HCV co-infection is rapidly emerging as a major cause of morbidity and mortality throughout the world, due to the common rapid mutation characteristics of the two viruses as well as their similar complex influence to immunology system. Although considerable progresses have been made on the study of the infection of HIV and HCV respectively, few researches have been conducted on the investigation of the molecular mechanism of their co-infection and designing of the multi-target co-inhibitors for the two viruses simultaneously.ResultsIn our study, a multi-target Quantitative Structure-Activity Relationship (QSAR) study of the inhibitors for HIV-HCV co-infection were addressed with an in-silico machine learning technique, i.e. multi-task learning, to help to guide the co-inhibitor design. Firstly, an integrated dataset with 3 HIV inhibitor subsets targeted on protease, integrase and reverse transcriptase respectively, together with another 6 subsets of 2 HCV inhibitors targeted on NS3 serine protease and NS5B polymerase respectively were compiled. Secondly, an efficient multi-target QSAR modelling of HIV-HCV co-inhibitors was performed by applying an accelerated gradient method based multi-task learning on the whole 9 datasets. Furthermore, by solving the L-1-infinity regularized optimization, the Drug-like index features for compound description were ranked according to their joint importance in multi-target QSAR modelling of HIV and HCV. Finally, a drug structure-activity simulation for investigating the relationships between compound structures and binding affinities was presented based on our multiple target analysis, which is then providing several novel clues for the design of multi-target HIV-HCV co-inhibitors with increasing likelihood of successful therapies on HIV, HCV and HIV-HCV co-infection.ConclusionsThe framework presented in our study provided an efficient way to identify and design inhibitors that simultaneously and selectively bind to multiple targets from multiple viruses with high affinity, and will definitely shed new lights on the future work of inhibitor synthesis for multi-target HIV, HCV, and HIV-HCV co-infection treatments.