Alejandro Speck-Planche - Academia.edu (original) (raw)

Papers by Alejandro Speck-Planche

ChemInform, Mar 6, 2014

ABSTRACT Resistance of bacteria to current antibiotics has increased worldwide, being one of the ... more ABSTRACT Resistance of bacteria to current antibiotics has increased worldwide, being one of the leading unresolved situations in public health. Due to negligence regarding the treatment of community-acquired diseases, even healthcare facilities have been highly impacted by an emerging problem: nosocomial infections. Moreover, infectious diseases, including nosocomial infections, have been found to depend on multiple pathogenic factors, confirming the need to discover of multi-target antibacterial agents. Finding of new drugs is a very complex, expensive, and time-consuming process. In this sense, Quantitative Structure-Activity Relationships (QSAR) methods have become complementary tools for medicinal chemistry, permitting the efficient screening of potential drugs, and consequently, rationalizing the organic synthesis as well as the biological evaluation of compounds. In the consolidation of QSAR methods as important components of chemoinformatics, the use of mathematical chemistry, and more specifically, the use of graph-theoretical approaches have played a vital role. Here, we focus our attention on the evolution of QSAR methods, citing the most relevant works devoted to the development of promising graph-theoretical approaches in the last 8 years, and their applications to the prediction of antibacterial activities of chemicals against pathogens causing both community-acquired and nosocomial infections.

Afinidad, 2009

In this work an ESI is valued of the plasticized liquid membranes with 1-furoyl 3,3 diethylthiour... more In this work an ESI is valued of the plasticized liquid membranes with 1-furoyl 3,3 diethylthiourea as ionophore (neutral portadore), tributyl phosphate as plasticizer and poly(vinyl chloride) as matrix on a sensitive conductive support to lead and nitrate (to this I finish once out the time gives life of the gives the ESI to lead).By the same payee is manifested two mechanisms he gives very effective answer. The ESI Pb2+ present e linear response in the concentration range of the 10-6-10 -3 mol/dm3, with slopes of 29.6 mV/decade, response time obtained was less than 20 seconds. By NO-3 of the overNersnts slopes of de -63.14 mV/decade and response time 20 seconds. Their parameters are presented calibration, as well as the electron microscopy gives Sweeping of the membranes he gives the sensitive ESI to the cation lead (II) and seen he gives this when it loses its sensibility to this ion and he/she begins to respond to its primary second for a second mechanism of the gives answer. The

Mini-reviews in Medicinal Chemistry, Dec 9, 2019

This work discusses the idea that drug discovery, instead of being performed through a series of ... more This work discusses the idea that drug discovery, instead of being performed through a series of filtering-based stages, should be viewed as a multi-scale optimization problem. Here, the most promising multi-scale models are analyzed in terms of their applications, advantages, and limitations in the search for more potent and safer chemicals against infectious diseases. Multi-scale de novo drug design is highlighted as an emerging paradigm, able to accelerate the discovery of more effective antimicrobial agents.

Revista Cubana de Química, 2005

Expert Opinion on Drug Discovery, Sep 27, 2022

Mini-reviews in Medicinal Chemistry, Sep 1, 2020

Elsevier eBooks, 2017

Abstract Human immunodeficiency virus (HIV) is responsible for causing the life-threatening condi... more Abstract Human immunodeficiency virus (HIV) is responsible for causing the life-threatening condition known as acquired immune deficiency syndrome (AIDS). Current antiretroviral regimens are usually effective in halting the progression of HIV/AIDS, but serious concerns exist regarding the emergence of multidrug resistance and the prevalence of side effects. In the present chapter, we introduce the first multitasking model for quantitative structure–biological effect relationships (mtk-QSBER), which is focused on performing simultaneous predictions of anti-HIV activities and desirable safety profiles, and the fragment-based design of virtually efficacious anti-HIV compounds. The mtk-QSBER model was constructed from a data set formed by 29,682 cases, displaying accuracy greater than 96%. Several fragments were selected, and their contributions to multiple biological effects were calculated. The joint use of the fragment contributions and the physicochemical interpretations of the molecular descriptors in the mtk-QSBER model allowed the design of six new molecules, which were predicted as potent and safe anti-HIV agents.

Elsevier eBooks, 2017

Abstract In this chapter, we propose a novel computational methodology for the virtual design and... more Abstract In this chapter, we propose a novel computational methodology for the virtual design and screening of peptides with potential anticancer activity against different cancer cell lines, and low cytotoxicity against diverse healthy mammalian cells. In this context, a multitasking (mtk) chemoinformatic model combining Broto–Moreau autocorrelations with artificial neural networks was derived from a data set containing 1933 cases of peptides. The model exhibited an accuracy greater than 92% in both training and prediction (test) sets. A simple statistical approach was applied to qualitatively correlate the changes in the physicochemical properties (molecular descriptors) of the peptides with the corresponding variations in their biological effects. To illustrate the practical use of the proposed in silico methodology, 12 peptides were designed and predicted by the mtk-chemoinformatic model. Encouraging results were obtained, indicating that these peptides can be considered for future experiments focused on the assessment of anticancer activity and cytotoxicity.

Biomedicines, 2022

Pancreatic cancer (PANC) is a dangerous type of cancer that is a major cause of mortality worldwi... more Pancreatic cancer (PANC) is a dangerous type of cancer that is a major cause of mortality worldwide and exhibits a remarkably poor prognosis. To date, discovering anti-PANC agents remains a very complex and expensive process. Computational approaches can accelerate the search for anti-PANC agents. We report for the first time two models that combined perturbation theory with machine learning via a multilayer perceptron network (PTML-MLP) to perform the virtual design and prediction of molecules that can simultaneously inhibit multiple PANC cell lines and PANC-related proteins, such as caspase-1, tumor necrosis factor-alpha (TNF-alpha), and the insulin-like growth factor 1 receptor (IGF1R). Both PTML-MLP models exhibited accuracies higher than 78%. Using the interpretation from one of the PTML-MLP models as a guideline, we extracted different molecular fragments desirable for the inhibition of the PANC cell lines and the aforementioned PANC-related proteins and then assembled some of...

Mini-Reviews in Medicinal Chemistry, 2020

Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug d... more Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets ...

Proceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th edition, 2019

Biological data on the cytotoxic and the ecotoxic effects of coated and uncoated nanoparticles we... more Biological data on the cytotoxic and the ecotoxic effects of coated and uncoated nanoparticles were retrieved from the scientific literature. The mathematical treatment of these data was based on the use of perturbation theory (PT) operators. This enabled the development of a model that combined perturbation theory concepts with artificial neural networks (PTML-ANN). New nanoparticles not reported during the generation of the PTML-ANN model were used in a virtual screening experiment. For these new nanoparticles, the predictions performed by the PTML-ANN model converged with the experimental results.

Current Topics in Medicinal Chemistry, 2021

Respiratory viruses continue to afflict mankind. Among them, pathogens such as coronaviruses [inc... more Respiratory viruses continue to afflict mankind. Among them, pathogens such as coronaviruses [including the current pandemic agent known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)] and the one causing influenza A (IAV) are highly contagious and deadly. These can evade the immune system defenses while causing a hyperinflammatory response that can damage different tissues/organs. Simultaneously targeting several immunomodulatory proteins is a plausible antiviral strategy since it could lead to the discovery of indirect-acting pan-antiviral (IAPA) agents for the treatment of diseases caused by respiratory viruses. In this context, computational approaches, which are an essential part of the modern drug discovery campaigns, could accelerate the identification of multi-target immunomodulators. This perspective discusses the usefulness of computational multi-target drug discovery for the virtual screening (drug repurposing) of IAPA agents capable of boosting the immun...

SAR and QSAR in Environmental Research, 2020

Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemothera... more Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.

ACS Omega

Respiratory viruses are infectious agents, which can cause pandemics. Although nowadays the dange... more Respiratory viruses are infectious agents, which can cause pandemics. Although nowadays the danger associated with respiratory viruses continues to be evidenced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the virus responsible for the current COVID-19 pandemic, other viruses such as SARS-CoV-1, the influenza A and B viruses (IAV and IBV, respectively), and the respiratory syncytial virus (RSV) can lead to globally spread viral diseases. Also, from a biological point of view, most of these viruses can cause an organ-damaging hyperinflammatory response known as the cytokine storm (CS). Computational approaches constitute an essential component of modern drug development campaigns, and therefore, they have the potential to accelerate the discovery of chemicals able to simultaneously inhibit multiple molecular and nonmolecular targets. We report here the first multicondition model based on quantitative structure−activity relationships and an artificial neural network (mtc-QSAR-ANN) for the virtual design and prediction of molecules with dual pan-antiviral and anti-CS profiles. Our mtc-QSAR-ANN model exhibited an accuracy higher than 80%. By interpreting the different descriptors present in the mtc-QSAR-ANN model, we could retrieve several molecular fragments whose assembly led to new molecules with drug-like properties and predicted panantiviral and anti-CS activities.

In the present work a study in quantitative structure-chromatographic retention index relationshi... more In the present work a study in quantitative structure-chromatographic retention index relationship (QSRR) in a family of imines, was realized. The model was ...

Frontiers in Chemistry, 2021

Neglected Tropical Diseases (NTDs) are a group of infectious diseases that disproportionally affe... more Neglected Tropical Diseases (NTDs) are a group of infectious diseases that disproportionally affect impoverished countries. NTDs, because of their severity, negatively impact the disability-adjusted life year (DALY), a measure of overall disease burden on a country. This metric quantifies the potential years of life lost due to premature death as well as potential years of healthy life lost due to chronic states of illness and disability. NTDs, thus, transform the health of individuals and whole societies. The world health organization estimates that NTDs affect more than one billion people in 149 countries and cost developing economies billions of dollars every year. In 2012, NTDs accounted for approximately 22 million DALYs globally. People suffering from these diseases often lose their abilities to contribute socially and economically, forgo educational opportunities, and are burdened with exorbitant expenses for treatment. NTDs are, therefore, a primary driver of the poverty cyc...

ACS Omega, 2019

Pancreatic cancers are widely recognized as a group of neoplasms with one of the poorest prognose... more Pancreatic cancers are widely recognized as a group of neoplasms with one of the poorest prognoses in oncology research. Despite the advances achieved in drug design and development, there is no effective cure for pancreatic cancers, and the current chemotherapeutic regimens increase the survival rate by only a few months. As an integral part of all modern drug discovery campaigns, computer-aided approaches can represent a promising alternative change to accelerate the early discovery of potent anti-pancreatic cancer agents. To date, however, most of the efforts made so far have focused on small series of structurally related chemicals, where the anti-pancreatic cancer activity has been measured against only one cancer cell line. In addition, no rational insight has been provided in the sense of unveiling the physicochemical aspects and the structural features that the molecules should possess to increase the anti-pancreatic cancer activity. This work reports the first multicellular target QSAR model based on ensemble learning (mct-QSAR-EL) that allows the simultaneous prediction and design of molecules with activity against different pancreatic cancer cell lines, which exhibit different degrees of sensitivity to chemical treatment. The mct-QSAR-EL model displayed sensitivities and specificities higher than 80% in both training and test sets. The physicochemical and structural interpretations of the molecular descriptors in the model permitted the selection of several fragments with potentially positive contributions to the increase of the anti-pancreatic cancer activity. These fragments were then assembled to design new molecules. The designed molecules were predicted as multicell line inhibitors by the mct-QSAR-EL model, and these results converged with the predictions performed by recently reported models. The designed molecules complied with Lipinski's rule of five and its variants.

Biomolecules, 2021

Inflammation involves a complex biological response of the body tissues to damaging stimuli. When... more Inflammation involves a complex biological response of the body tissues to damaging stimuli. When dysregulated, inflammation led by biomolecular mediators such as caspase-1 and tumor necrosis factor-alpha (TNF-alpha) can play a detrimental role in the progression of different medical conditions such as cancer, neurological disorders, autoimmune diseases, and cytokine storms caused by viral infections such as COVID-19. Computational approaches can accelerate the search for dual-target drugs able to simultaneously inhibit the aforementioned proteins, enabling the discovery of wide-spectrum anti-inflammatory agents. This work reports the first multicondition model based on quantitative structure–activity relationships and a multilayer perceptron neural network (mtc-QSAR-MLP) for the virtual screening of agency-regulated chemicals as versatile anti-inflammatory therapeutics. The mtc-QSAR-MLP model displayed accuracy higher than 88%, and was interpreted from a physicochemical and structu...

ChemInform, Mar 6, 2014

ABSTRACT Resistance of bacteria to current antibiotics has increased worldwide, being one of the ... more ABSTRACT Resistance of bacteria to current antibiotics has increased worldwide, being one of the leading unresolved situations in public health. Due to negligence regarding the treatment of community-acquired diseases, even healthcare facilities have been highly impacted by an emerging problem: nosocomial infections. Moreover, infectious diseases, including nosocomial infections, have been found to depend on multiple pathogenic factors, confirming the need to discover of multi-target antibacterial agents. Finding of new drugs is a very complex, expensive, and time-consuming process. In this sense, Quantitative Structure-Activity Relationships (QSAR) methods have become complementary tools for medicinal chemistry, permitting the efficient screening of potential drugs, and consequently, rationalizing the organic synthesis as well as the biological evaluation of compounds. In the consolidation of QSAR methods as important components of chemoinformatics, the use of mathematical chemistry, and more specifically, the use of graph-theoretical approaches have played a vital role. Here, we focus our attention on the evolution of QSAR methods, citing the most relevant works devoted to the development of promising graph-theoretical approaches in the last 8 years, and their applications to the prediction of antibacterial activities of chemicals against pathogens causing both community-acquired and nosocomial infections.

Afinidad, 2009

In this work an ESI is valued of the plasticized liquid membranes with 1-furoyl 3,3 diethylthiour... more In this work an ESI is valued of the plasticized liquid membranes with 1-furoyl 3,3 diethylthiourea as ionophore (neutral portadore), tributyl phosphate as plasticizer and poly(vinyl chloride) as matrix on a sensitive conductive support to lead and nitrate (to this I finish once out the time gives life of the gives the ESI to lead).By the same payee is manifested two mechanisms he gives very effective answer. The ESI Pb2+ present e linear response in the concentration range of the 10-6-10 -3 mol/dm3, with slopes of 29.6 mV/decade, response time obtained was less than 20 seconds. By NO-3 of the overNersnts slopes of de -63.14 mV/decade and response time 20 seconds. Their parameters are presented calibration, as well as the electron microscopy gives Sweeping of the membranes he gives the sensitive ESI to the cation lead (II) and seen he gives this when it loses its sensibility to this ion and he/she begins to respond to its primary second for a second mechanism of the gives answer. The

Mini-reviews in Medicinal Chemistry, Dec 9, 2019

This work discusses the idea that drug discovery, instead of being performed through a series of ... more This work discusses the idea that drug discovery, instead of being performed through a series of filtering-based stages, should be viewed as a multi-scale optimization problem. Here, the most promising multi-scale models are analyzed in terms of their applications, advantages, and limitations in the search for more potent and safer chemicals against infectious diseases. Multi-scale de novo drug design is highlighted as an emerging paradigm, able to accelerate the discovery of more effective antimicrobial agents.

Revista Cubana de Química, 2005

Expert Opinion on Drug Discovery, Sep 27, 2022

Mini-reviews in Medicinal Chemistry, Sep 1, 2020

Elsevier eBooks, 2017

Abstract Human immunodeficiency virus (HIV) is responsible for causing the life-threatening condi... more Abstract Human immunodeficiency virus (HIV) is responsible for causing the life-threatening condition known as acquired immune deficiency syndrome (AIDS). Current antiretroviral regimens are usually effective in halting the progression of HIV/AIDS, but serious concerns exist regarding the emergence of multidrug resistance and the prevalence of side effects. In the present chapter, we introduce the first multitasking model for quantitative structure–biological effect relationships (mtk-QSBER), which is focused on performing simultaneous predictions of anti-HIV activities and desirable safety profiles, and the fragment-based design of virtually efficacious anti-HIV compounds. The mtk-QSBER model was constructed from a data set formed by 29,682 cases, displaying accuracy greater than 96%. Several fragments were selected, and their contributions to multiple biological effects were calculated. The joint use of the fragment contributions and the physicochemical interpretations of the molecular descriptors in the mtk-QSBER model allowed the design of six new molecules, which were predicted as potent and safe anti-HIV agents.

Elsevier eBooks, 2017

Abstract In this chapter, we propose a novel computational methodology for the virtual design and... more Abstract In this chapter, we propose a novel computational methodology for the virtual design and screening of peptides with potential anticancer activity against different cancer cell lines, and low cytotoxicity against diverse healthy mammalian cells. In this context, a multitasking (mtk) chemoinformatic model combining Broto–Moreau autocorrelations with artificial neural networks was derived from a data set containing 1933 cases of peptides. The model exhibited an accuracy greater than 92% in both training and prediction (test) sets. A simple statistical approach was applied to qualitatively correlate the changes in the physicochemical properties (molecular descriptors) of the peptides with the corresponding variations in their biological effects. To illustrate the practical use of the proposed in silico methodology, 12 peptides were designed and predicted by the mtk-chemoinformatic model. Encouraging results were obtained, indicating that these peptides can be considered for future experiments focused on the assessment of anticancer activity and cytotoxicity.

Biomedicines, 2022

Pancreatic cancer (PANC) is a dangerous type of cancer that is a major cause of mortality worldwi... more Pancreatic cancer (PANC) is a dangerous type of cancer that is a major cause of mortality worldwide and exhibits a remarkably poor prognosis. To date, discovering anti-PANC agents remains a very complex and expensive process. Computational approaches can accelerate the search for anti-PANC agents. We report for the first time two models that combined perturbation theory with machine learning via a multilayer perceptron network (PTML-MLP) to perform the virtual design and prediction of molecules that can simultaneously inhibit multiple PANC cell lines and PANC-related proteins, such as caspase-1, tumor necrosis factor-alpha (TNF-alpha), and the insulin-like growth factor 1 receptor (IGF1R). Both PTML-MLP models exhibited accuracies higher than 78%. Using the interpretation from one of the PTML-MLP models as a guideline, we extracted different molecular fragments desirable for the inhibition of the PANC cell lines and the aforementioned PANC-related proteins and then assembled some of...

Mini-Reviews in Medicinal Chemistry, 2020

Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug d... more Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets ...

Proceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th edition, 2019

Biological data on the cytotoxic and the ecotoxic effects of coated and uncoated nanoparticles we... more Biological data on the cytotoxic and the ecotoxic effects of coated and uncoated nanoparticles were retrieved from the scientific literature. The mathematical treatment of these data was based on the use of perturbation theory (PT) operators. This enabled the development of a model that combined perturbation theory concepts with artificial neural networks (PTML-ANN). New nanoparticles not reported during the generation of the PTML-ANN model were used in a virtual screening experiment. For these new nanoparticles, the predictions performed by the PTML-ANN model converged with the experimental results.

Current Topics in Medicinal Chemistry, 2021

Respiratory viruses continue to afflict mankind. Among them, pathogens such as coronaviruses [inc... more Respiratory viruses continue to afflict mankind. Among them, pathogens such as coronaviruses [including the current pandemic agent known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)] and the one causing influenza A (IAV) are highly contagious and deadly. These can evade the immune system defenses while causing a hyperinflammatory response that can damage different tissues/organs. Simultaneously targeting several immunomodulatory proteins is a plausible antiviral strategy since it could lead to the discovery of indirect-acting pan-antiviral (IAPA) agents for the treatment of diseases caused by respiratory viruses. In this context, computational approaches, which are an essential part of the modern drug discovery campaigns, could accelerate the identification of multi-target immunomodulators. This perspective discusses the usefulness of computational multi-target drug discovery for the virtual screening (drug repurposing) of IAPA agents capable of boosting the immun...

SAR and QSAR in Environmental Research, 2020

Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemothera... more Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.

ACS Omega

Respiratory viruses are infectious agents, which can cause pandemics. Although nowadays the dange... more Respiratory viruses are infectious agents, which can cause pandemics. Although nowadays the danger associated with respiratory viruses continues to be evidenced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the virus responsible for the current COVID-19 pandemic, other viruses such as SARS-CoV-1, the influenza A and B viruses (IAV and IBV, respectively), and the respiratory syncytial virus (RSV) can lead to globally spread viral diseases. Also, from a biological point of view, most of these viruses can cause an organ-damaging hyperinflammatory response known as the cytokine storm (CS). Computational approaches constitute an essential component of modern drug development campaigns, and therefore, they have the potential to accelerate the discovery of chemicals able to simultaneously inhibit multiple molecular and nonmolecular targets. We report here the first multicondition model based on quantitative structure−activity relationships and an artificial neural network (mtc-QSAR-ANN) for the virtual design and prediction of molecules with dual pan-antiviral and anti-CS profiles. Our mtc-QSAR-ANN model exhibited an accuracy higher than 80%. By interpreting the different descriptors present in the mtc-QSAR-ANN model, we could retrieve several molecular fragments whose assembly led to new molecules with drug-like properties and predicted panantiviral and anti-CS activities.

In the present work a study in quantitative structure-chromatographic retention index relationshi... more In the present work a study in quantitative structure-chromatographic retention index relationship (QSRR) in a family of imines, was realized. The model was ...

Frontiers in Chemistry, 2021

Neglected Tropical Diseases (NTDs) are a group of infectious diseases that disproportionally affe... more Neglected Tropical Diseases (NTDs) are a group of infectious diseases that disproportionally affect impoverished countries. NTDs, because of their severity, negatively impact the disability-adjusted life year (DALY), a measure of overall disease burden on a country. This metric quantifies the potential years of life lost due to premature death as well as potential years of healthy life lost due to chronic states of illness and disability. NTDs, thus, transform the health of individuals and whole societies. The world health organization estimates that NTDs affect more than one billion people in 149 countries and cost developing economies billions of dollars every year. In 2012, NTDs accounted for approximately 22 million DALYs globally. People suffering from these diseases often lose their abilities to contribute socially and economically, forgo educational opportunities, and are burdened with exorbitant expenses for treatment. NTDs are, therefore, a primary driver of the poverty cyc...

ACS Omega, 2019

Pancreatic cancers are widely recognized as a group of neoplasms with one of the poorest prognose... more Pancreatic cancers are widely recognized as a group of neoplasms with one of the poorest prognoses in oncology research. Despite the advances achieved in drug design and development, there is no effective cure for pancreatic cancers, and the current chemotherapeutic regimens increase the survival rate by only a few months. As an integral part of all modern drug discovery campaigns, computer-aided approaches can represent a promising alternative change to accelerate the early discovery of potent anti-pancreatic cancer agents. To date, however, most of the efforts made so far have focused on small series of structurally related chemicals, where the anti-pancreatic cancer activity has been measured against only one cancer cell line. In addition, no rational insight has been provided in the sense of unveiling the physicochemical aspects and the structural features that the molecules should possess to increase the anti-pancreatic cancer activity. This work reports the first multicellular target QSAR model based on ensemble learning (mct-QSAR-EL) that allows the simultaneous prediction and design of molecules with activity against different pancreatic cancer cell lines, which exhibit different degrees of sensitivity to chemical treatment. The mct-QSAR-EL model displayed sensitivities and specificities higher than 80% in both training and test sets. The physicochemical and structural interpretations of the molecular descriptors in the model permitted the selection of several fragments with potentially positive contributions to the increase of the anti-pancreatic cancer activity. These fragments were then assembled to design new molecules. The designed molecules were predicted as multicell line inhibitors by the mct-QSAR-EL model, and these results converged with the predictions performed by recently reported models. The designed molecules complied with Lipinski's rule of five and its variants.

Biomolecules, 2021

Inflammation involves a complex biological response of the body tissues to damaging stimuli. When... more Inflammation involves a complex biological response of the body tissues to damaging stimuli. When dysregulated, inflammation led by biomolecular mediators such as caspase-1 and tumor necrosis factor-alpha (TNF-alpha) can play a detrimental role in the progression of different medical conditions such as cancer, neurological disorders, autoimmune diseases, and cytokine storms caused by viral infections such as COVID-19. Computational approaches can accelerate the search for dual-target drugs able to simultaneously inhibit the aforementioned proteins, enabling the discovery of wide-spectrum anti-inflammatory agents. This work reports the first multicondition model based on quantitative structure–activity relationships and a multilayer perceptron neural network (mtc-QSAR-MLP) for the virtual screening of agency-regulated chemicals as versatile anti-inflammatory therapeutics. The mtc-QSAR-MLP model displayed accuracy higher than 88%, and was interpreted from a physicochemical and structu...