Abstract: Alzheimer's disease (AD), a degenerative disease affecting the brain, is the single most common source of dementia in adults. The cause and the progression of AD still remains a mystery among medical experts. As a result, a cure has not yet been discovered, even after decade's worth of research that started since 1906, when the disease was first identified. Despite the efforts of the scientific community, several of the biological receptors associated with AD have not been sufficiently studied to date, limiting in turn the design of new and more potent anti-AD agents. Thus, the search for new drug candidates as inhibitors of different targets associated with AD constitutes an essential part towards the discovery of new and more efficient anti-AD therapies. The present work is focused on the role of the Ligand-Based Drug Design (LBDD) methodologies which have been applied for the elucidation of new molecular entities with high inhibitory activity against targets related with AD. Particular emphasis is given also to the current state of fragment-based ligand approaches as alternatives of the Fragment-Based Drug Discovery (FBDD) methodologies. Finally, several guidelines are offered to show how the use of fragment-based descriptors can be determinant for the design of multi-target inhibitors of proteins associated with AD.
Abstract: With the significant increase of life expectancy of populations in societies today, the importance of the discovery of drugs associated with neurodegenerative diseases has emerged. Therefore, neurodegenerative diseases are an important topic in Medicinal Chemistry. Although drug discovery is considered a complex and slow process, new approaches and methods have been developed with the intention of finding new chemical entities in more efficient ways. This work provides a review of virtual methodologies applied in drug discovery and especially a new model for the prediction of MAO-A inhibitors using a multi-target QSAR methodology. This model involves a mixed approach containing simple descriptors based on atom-centered fragments and functional groups (DRAGON) and topological substructural molecular design descriptors (MODESLAB). This unified multi-species QSAR model was validated through a virtual screening of a new series of oxoisoaporphine derivatives, taking into account the information in the calculated fragmental contributions. Therefore, this method represents a useful tool for the in silico screening of MAO-A inhibitors.
Abstract: Hepatitis C constitutes an infectious disease that causes severe damages to the liver, and is caused by hepatitis C virus. There is no vaccine against this type of disease and the number of people infected continues to grow worldwide. The anti-viral therapy which is currently used is a mixture of interferon alpha-2a with ribavirin, but approximately half of the patients do not respond to therapy. Therefore, it is necessary to search for new compounds with anti-hepatitis C activity. Computer-aided drug design methodologies have been vital in the discovery of candidates to drugs. This review is dedicated to the role of computer-aided drug design methodologies for the development of new anti-hepatitis C agents. In addition, we introduce a QSAR model based on substructural approaches in order to model the anti-hepatitis C activity in vivo.
Abstract: The search for new therapies against neurodegenerative disorders (NDs) such as Alzheimer (AD) and Parkinson (PD) constitutes a very active area. Although the scientific community has realized great efforts for the study of AD and PD from the most diverse points of view, these diseases remain incurable. Consequently, the design of new and more potent compounds for proteins associated with AD and PD represents nowadays, an objective of major importance. In this sense, the protein known as monoamine oxidase B (MAO-B) constitutes one of the key targets for the search of new drug candidates which could be employed as neuroprotective agents in both anti-AD and anti-PD chemotherapies. The present work is focused on the role of the Quantitative-Structure Activity Relationship (QSAR) analysis and molecular docking (MDock) techniques which have been applied for the discovery of new and promising molecular entities with high inhibitory activity against MAO-B. We also give a brief overview about one of the most potent MAO-B inhibitor drugs: rasagiline. Finally, as contribution to the field, we constructed a QSAR model using artificial neural network (ANN) analysis for the virtual screening of potent MAO-B inhibitors. By realizing a careful inspection of the meaning of the variables in the QSAR-ANN model, new rasagiline bioisosteres were suggested as possible potent MAO-B inhibitors.
Abstract: Rational design of entry inhibitors is an active area for the discovery of new and effective anti-HIV agents. C-C Chemokine receptors represent key targets for the HIV entry process. Several of these proteins with features to be HIV co-receptors have not been sufficiently studied or used for the design of novel entry inhibitors. With the purpose to overcome this problem, we develop here a fragment-based approach for the design of multi-target inhibitors against four C-C chemokine receptors. This approach was focused on the construction of a multi-target QSAR discriminant model using a large and heterogeneous database of compounds and substructural descriptors for the classification and prediction of inhibitors for C-C chemokine receptors. The model correctly classified more than 89% of active and inactive compounds in both: training and prediction series. As principal advantage, this model permitted the automatic and fast extraction of fragments responsible for the inhibitory activity against the different C-C chemokine receptors under study and new molecular entities were suggested as possible versatile inhibitors for these proteins.
Abstract: A brain tumor (BT) constitutes a neoplasm located in the brain or the central spinal canal. The number of new diagnosed cases with BT increases with the pass of the time. Understanding the biology of BT is essential for the development of novel therapeutic strategies, in order to prevent or deal with this disease. An active area for the search of new anti-BT therapies is the use of Chemoinformatics and/or Bioinformatics toward the design of new and potent anti-BT agents. The principal limitation of all these approaches is that they consider small series of structurally related compounds and/or the studies are realized for only one target like protein. The present work is an effort to overcome this problem. We introduce here the first Chemoinformatics multi-target approach for the in silico design and prediction of anti-BT agents against several cell lines. Here, a fragment-based QSAR model was developed. The model correctly classified 89.63% and 90.93% of active and inactive compounds respectively, in training series. The validation of the model was carried out by using prediction series which showed 88.00% of correct classification for active and 88.59% for inactive compounds. Some fragments were extracted from the molecules and their contributions to anti-BT activity were calculated. Several fragments were identified as potential substructural features responsible of anti-BT activity and new molecular entities designed from fragments with positive contributions were suggested as possible anti-BT agents.
Abstract: Mycobacterium tuberculosis (MTB) is the principal pathogen which causes tuberculosis (TB), a disease that remains as one of the most alarming health problems worldwide. An active area for the search of new anti-TB therapies is concerned with the use of computational approaches based on Chemoinformatics and/or Bioinformatics toward the discovery of new and potent anti-TB agents. These approaches consider only small series of structurally related compounds and the studies are generally realized for only one target like a protein. This fact constitutes an important limitation. The present work is an effort to overcome this problem. We introduce here the first chemo-bioinformatic approach by developing a multi-target (mt) QSAR discriminant model, for the in silico design and virtual screening of anti-TB agents against six proteins in MTB. The mt-QSAR model was developed by employing a large and heterogeneous database of compounds and substructural descriptors. The model correctly classified more than 90% of active and inactive compounds in both, training and prediction series. Some fragments were extracted from the molecules and their contributions to anti-TB activity through inhibition of the six proteins, were calculated. Several fragments were identified as responsible for anti-TB activity and new molecular entities were designed from those fragments with positive contributions, being suggested as possible anti-TB agents.
Abstract: With the increase of the life expectancy of the population in the actual society, the importance of drug discovery focused in the neurodegenerative diseases emerges. So, neurodegenerative diseases are an important topic in Medicinal Chemistry. However the drug discovery is considered a complex and slow process, new approaches and methods for drug discovery have been developed, with the intention of finding new chemical entities in a new more efficient way. This work provides a review about virtual methodologies of drug discovery and especially a new model for the prediction of the MAO-A inhibitors by the use of a multi-target QSAR methodology. This used a mixed approach, containing simple descriptors based on atom centered-fragment and functional groups (DRAGON), and topological substructural molecular design descriptors (MODESLAB). This unified multi-species QSAR model was validated through a virtual screening of a new series of oxoisoaporphine derivatives, taking into account the information on the calculated fragmental contributions. Therefore, this method represents a useful tool for the in silico screening of MAO-A inhibitors.
Abstract: 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.
Abstract: Alzheimer disease (AD) is one of the most common and serious neurodegenerative disorders in humans. For this reason, the search for new anti-AD chemotherapies is a very active area. Only few biological receptors associated with AD have been well studied. The efficacy of the current drugs is limited by the fact that they inhibit only one target like protein. Thus, the rational design of new drug candidates as versatile inhibitors for different proteins associated with AD constitutes a major goal. With the aim to overcome this problem, we developed here the first fragment-based approach by exploring quantitativestructure- activity relationships (QSAR). The principal purpose here, was the in silico design of multitarget (mt) inhibitors against five proteins associated with AD. Our approach was focused on the construction of an mt-QSAR discriminant model using a large and heterogeneous database of compounds and substructural descriptors, which permitted the simultaneous classification and prediction of inhibitors against five proteins associated with AD. The model correctly classified more than 90% of active and inactive compounds in both, training and prediction series. As principal advantage, this mt-QSAR discriminant model was used for the automatic and fast extraction of fragments responsible for the inhibitory activity against the five proteins under study, and new molecular entities were suggested as possible versatile inhibitors for these proteins.
Abstract: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive dementia and loss of cognitive abilities. Until now, AD remains incurable. The principal biological target for AD therapy is acetylcholinesterase (AChE). Thus, the search for new drug candidates like AChE inhibitors constitutes an essential part for the discovery of more potent anti-AD agents. In general terms, rational drug design methodologies have played a decisive role. The present work is focused on the current state of the Ligand-Based Drug Design (LBDD) methods which have been applied to the elucidation of new molecular entities with high anti-AChE activity. Also, as a contribution to this field, we suggest a promising fragment-based approach for the search and prediction of new AChE inhibitors and for the fast and efficient extraction of substructural alerts which are responsible for the anti-AChE activity.
Abstract: The discovery of new and more efficient anti-cancer chemotherapies is a field of research in expansion and growth. Breast cancer (BC) is one of the most studied cancers because it is the principal cause of cancer deaths in women. In the active area for the search of more potent anti-BC drugs, the use of approaches based on Chemoinformatics has played a very important role. However, until now there is no methodology able to predict anti-BC activity of compounds against more than one BC cell line, which should constitute a greater interest. In this study we introduce the first chemoinformatic multi-target (mt) approach for the in silico design and virtual screening of anti-BC agents against 13 cell lines. Here, an mt-QSAR discriminant model was developed using a large and heterogeneous database of compounds. The model correctly classified 88.47% and 92.75% of active and inactive compounds respectively, in training set. The validation of the model was carried out by using a prediction set which showed 89.79% of correct classification for active and 92.49% for inactive compounds. Some fragments were extracted from the molecules and their contributions to anti-BC activity were calculated. Several fragments were identified as potential substructural features responsible for anti-BC activity and new molecules designed from those fragments with positive contributions were suggested as possible potent and versatile anti-BC agents.
Abstract: Agriculture is needed to deal with crop losses caused by biotic stresses like pests. The use of pesticides has played a vital role, contributing to improve crop production and harvest productivity, providing a better crop quality and supply, and consequently contributing with the improvement of the human health. An important group of these pesticides is fungicides. However, the use of these agrochemical fungicides is an important source of contamination, damaging the ecosystems. Several studies have been realized for the assessment of the toxicity in agrochemical fungicides, but the principal limitation is the use of structurally related compounds against usually one indicator species. In order to overcome this problem, we explore the quantitative structure-toxicity relationships (QSTR) in agrochemical fungicides. Here, we developed the first multi-species (ms) chemoinformatic approach for the prediction multiple ecotoxicological profiles of fungicides against 20 indicators species and their classifications in toxic or nontoxic. The ms-QSTR discriminant model was based on substructural descriptors and a heterogeneous database of compounds. The percentages of correct classification were higher than 90% for both, training and prediction series. Also, substructural alerts responsible for the toxicity/no toxicity in fungicides respect all ecotoxicological profiles, were extracted and analyzed.
Abstract: 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.
Abstract: The increasing resistance of several phytopathogenic fungal species to existing agrochemical fungicides has alarmed the worldwide scientific community. In an attempt to overcome this problem, a discriminant model based on substructural descriptors was developed from a heterogeneous database of compounds for the design of, search for and prediction of agrochemical fungicides.
Abstract: Prostate cancer (PCa) is the second-leading cause of cancer deaths among men in the around the world. Understanding the biology of PCa is essential to the development of novel therapeutic strategies, in order to prevent this disease. However, after PCa make metastases, chemotherapy plays an extremely important role. With the pass of the time, PCa cell lines become resistant to the current anti-PCa drugs. For this reason, there is a necessity to develop new anti-PCa agents with the ability to be active against several PCa cell lines. The present work is an effort to overcome this problem. We introduce here the first multi-target approach for the design and prediction of anti-PCa agents against several cell lines. Here, a fragment-based QSAR model was developed. The model had a sensitivity of 88.36% and specificity 89.81% in training series. Also, the model showed 94.06% and 92.92% for sensitivity and specificity, respectively. Some fragments were extracted from the molecules and their contributions to anti-PCa activity were calculated. Several fragments were identified as potential substructural features responsible of anti-PCa activity and new molecular entities designed from fragments with positive contributions were suggested as possible anti-PCa agents.
Abstract: The increasing resistance of several phytopathogenic fungal species to the existing agrochemical fungicides has alarmed to the worldwide scientific community. There is no available methodology to predict in an efficient way if a new fungicide will have resistance risk due to fungal species which cause considerable crop losses. In an attempt to overcome this problem, a multi-resistance risk QSAR model, based on substructural descriptors was developed from a heterogeneous database of compounds. The purpose of this model is the classification, design, and prediction of agrochemical fungicides according to resistance risk categories. The QSAR model classified correctly 85.11% of the fungicides and the 85.07% of the inactive compounds in the training series, for an accuracy of 85.08%. In the prediction series, the percentages of correct classification were 85.71 and 86.55% for fungicides and inactive compounds, respectively, with an accuracy of 86.39%. Some fragments were extracted and their quantitative contributions to the fungicidal activity were calculated taking into consideration the different resistance risk categories for agrochemical fungicides. In the same way, some fragments present in molecules with fungicidal activity and with negative contributions were analyzed like structural alerts responsible of resistance risk.
Abstract: A sarcoma is a type of cancer which is originated from the connective tissue cells. With the time, several sarcomas have become resistant to the current anti-tumoral drugs. Many works have been reported in order to explain some mechanisms of resistance in different types of sarcomas and around 2000 compounds have been tested as anti-sarcoma agents against several sarcoma cell lines. However, there is no an available methodology for the rational design of compounds with anti-sarcoma activity. The present work develops a unified fragment-based approach by employing a multi-target QSAR model for the efficient search and design of new anti-sarcoma agents against 12 sarcoma cell lines. The model was obtained with the use of a heterogeneous database of compounds and it was based on substructural descriptors. The percentages of correct classification of active and inactive compounds were higher than 85% in both cases. Also, the present approach provided the rapid extraction of substructural alerts responsible of anti-sarcoma profile by calculating the quantitative contributions of fragments to anti-sarcoma activity. To our knowledge, this is the first attempt to calculate the probabilities of anti-sarcoma activity of compounds against several sarcoma cell lines simultaneously, using a unified fragment-based QSAR model.
Abstract: Human immunodeficiency virus (HIV) is the responsible causal agent of acquired immunodeficiency syndrome (AIDS), a condition in humans in which the immune system begins to fail, allowing the entry of opportunistic infections. HIV infection in humans is considered pandemic by the World Health Organization (WHO). HIV needs to use a protein as a co-receptor to enter its target cells. Several chemokine receptors can in principle act as viral co-receptors, but the chemokine (C-C motif) receptor 5 (CCR5) is likely the most physiologically important co-receptor during natural infection. For this reason the development of new CCR5 inhibitors like anti-HIV agents, constitutes a challenge for the scientific community. The present review will focus on the current state of the design of novel anti-HIV drugs, and how the existing computer aided-drug design methodologies, have been effective in the search of new anti-HIV agents. In addition, a QSAR model based on substructural descirptors is presented as a rapid, rational and promising alternative for the discovery of anti-HIV agents through the inhibition of the CCR5.
Abstract: Pesticides are chemicals with a great impact in the economy of any country. They are employed for the eradication of pests. Insects constitute one of these pests which are extremely difficult to control. With the passage of the time, insects have become resistant to pesticides, causing huge crop losses and diseases in humans. For this reason, there is an increasing need for the design of more potent insecticides. The present review is focused on the current state of the application of computational approaches as essential tools for the design of novel insecticidal agents. Also, a model based on a substructural approach is presented as a rational, efficient and promising alternative for the discovery of new insecticides.
Abstract: Chagas disease is nowadays the most serious parasitic health problem. This disease is caused by Trypanosoma cruzi. The great number of deaths and the insufficient effectiveness of drugs against this parasite have alarmed the scientific community worldwide. In an attempt to overcome this problem, a model for the design and prediction of new antitrypanosomal agents was obtained. This used a mixed approach, containing simple descriptors based on fragments and topological substructural molecular design descriptors. A data set was made up of 188 compounds, 99 of them characterized an antitrypanosomal activity and 88 compounds that belong to other pharmaceutical categories. The model showed sensitivity, specificity and accuracy values above 85%. Quantitative fragmental contributions were also calculated. Then, and to confirm the quality of the model, 15 structures of molecules tested as antitrypanosomal compounds (that we did not include in this study) were predicted, taking into account the information on the abovementioned calculated fragmental contributions. The model showed an accuracy of 100% which means that the "in silico" methodology developed by our team is promising for the rational design of new antitrypanosomal drugs.
Abstract: The increasing resistance of Mycobacterium tuberculosis to the existing drugs has alarmed the worldwide scientific community. In an attempt to overcome this problem computer-aided drug design has provide an extraordinary support to the different strategies in drug discovery. There are around 250 biological receptors like enzymes that can be used in principle, for the design of antituberculosis compounds that act by a specific mechanism of action. Also, there more than 5000 compound available in the literature, and that constitute important information in order to search new molecular patterns for the design of new antituberculosis agents. The purpose of this paper is to explored the current state of drug discovery of antituberculosis agents and how the different strategies supported by computer-aided drug design methods has influenced in a determinant way in the design of new molecular entities that can result the future antituberculosis drugs.
Abstract: The increasing resistance of Mycobacterium tuberculosis to the existing drugs has alarmed the worldwide scientific community. In an attempt to overcome this problem, two models for the design and prediction of new antituberculosis agents were obtained. The first used a mixed approach, containing descriptors based on fragments and the topological substructural molecular design approach (TOPS-MODE) descriptors. The other model used a combination of two-dimensional (2D) and three-dimensional (3D) descriptors. A data set of 167 compounds with great structural variability, 72 of them antituberculosis agents and 95 compounds belonging to other pharmaceutical categories, was analyzed. The first model showed sensitivity, specificity, and accuracy values above 80% and the second one showed values higher than 75% for these statistical indices. Subsequently, 12 structures of imidazoles not included in this study were designed, taking into account the two models. In both cases accuracy was 100%, showing that the methodology in silico developed by us is promising for the rational design of antituberculosis drugs.
