Leishmaniasis and trypanosomiasis occur primarily in undeveloped countries and account for millions of deaths and disability-adjusted life years. Limited therapeutic options, high toxicity of ...chemotherapeutic drugs and the emergence of drug resistance associated with these diseases demand urgent development of novel therapeutic agents for the treatment of these dreadful diseases. In the last decades, different in silico methods have been successfully implemented for supporting the lengthy and expensive drug discovery process. In the current review, we discuss recent advances pertaining to in silico analyses towards lead identification, lead modification and target identification of antileishmaniasis and anti-trypanosomiasis agents. We describe recent applications of some important in silico approaches, such as 2D-QSAR, 3D-QSAR, pharmacophore mapping, molecular docking, and so forth, with the aim of understanding the utility of these techniques for the design of novel therapeutic anti-parasitic agents. This review focuses on: (a) advanced computational drug design options; (b) diverse methodologies - e.g.: use of machine learning tools, software solutions, and web-platforms; (c) recent applications and advances in the last five years; (d) experimental validations of in silico predictions; (e) virtual screening tools; and (f) rationale or justification for the selection of these in silico methods.
Hepatitis C constitutes an unresolved global health problem. This infectious disease is caused by the hepatotropic hepatitis C virus (HCV), and it can lead to the occurrence of life-threatening ...medical conditions, such as cirrhosis and liver cancer. Nowadays, major clinical concerns have arisen because of the appearance of multidrug resistance (MDR) and the side effects especially associated with long-term treatments. In this work, we report the first multitasking model for quantitative structure-biological effect relationships (mtk-QSBER), focused on the simultaneous exploration of anti-HCV activity and in vitro safety profiles related to the absorption, distribution, metabolism, elimination, and toxicity (ADMET). The mtk-QSBER model was created from a data set formed by 40 158 cases, displaying accuracy higher than 95% in both training and prediction (test) sets. Several molecular fragments were selected, and their quantitative contributions to anti-HCV activity and ADMET profiles were calculated. By combining the analysis of the fragments with positive contributions and the physicochemical meanings of the different molecular descriptors in the mtk-QSBER, six new molecules were designed. These new molecules were predicted to exhibit potent anti-HCV activity and desirable in vitro ADMET properties. In addition, the designed molecules have good druglikeness according to the Lipinski’s rule of five and its variants.
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.
The present work aims at establishing multi-target chemometric models using the recently launched quantitative structure-activity relationship (QSAR)-Co tool for predicting the activity of inhibitor ...compounds against different isoforms of phosphoinositide 3-kinase (PI3K) under various experimental conditions. The inhibitors of class I phosphoinositide 3-kinase (PI3K) isoforms have emerged as potential therapeutic agents for the treatment of various disorders, especially cancer. The cell-based enzyme inhibition assay results of PI3K inhibitors were curated from the CHEMBL database. Factors such as the nature and mutation of cell lines that may significantly alter the assay outcomes were considered as important experimental elements for mt-QSAR model development. The models, in turn, were developed using two machine learning techniques as implemented in QSAR-Co: linear discriminant analysis (LDA) and random forest (RF). Both techniques led to models with high accuracy (ca. 90%). Several molecular fragments were extracted from the current dataset, and their quantitative contributions to the inhibitory activity against all the proteins and experimental conditions under study were calculated. This case study also demonstrates the utility of QSAR-Co tool in solving multi-factorial and complex chemometric problems. Additionally, the combination of different in silico methods employed in this work can serve as a valuable guideline to speed up early discovery of PI3K inhibitors.
Antimicrobial peptides (AMPs) represent promising alternatives to fight against bacterial pathogens. However, cellular toxicity remains one of the main concerns in the early development of ...peptide-based drugs. This work introduces the first multitasking (mtk) computational model focused on performing simultaneous predictions of antibacterial activities, and cytotoxicities of peptides. The model was created from a data set containing 3592 cases, and it displayed accuracy higher than 96% for classifying/predicting peptides in both training and prediction (test) sets. The technique known as alanine scanning was computationally applied to illustrate the calculation of the quantitative contributions of the amino acids (in their respective positions of the sequence) to the biological effects of a defined peptide. A small library formed by 10 peptides was generated, where peptides were designed by considering the interpretations of the different descriptors in the mtk-computational model. All the peptides were predicted to exhibit high antibacterial activities against multiple bacterial strains, and low cytotoxicity against various cell types. The present mtk-computational model can be considered a very useful tool to support high throughput research for the discovery of potent and safe AMPs.
Nowadays, quantitative structure⁻activity relationship (QSAR) methods have been widely performed to predict the toxicity of compounds to organisms due to their simplicity, ease of implementation, and ...low hazards. In this study, to estimate the toxicities of substituted aromatic compounds to
, the QSAR models were established by the multiple linear regression (MLR) and radial basis function neural network (RBFNN). Unlike other QSAR studies, according to the difference of functional groups (−NO₂, −X), the whole dataset was divided into three groups and further modeled separately. The statistical characteristics for the models are obtained as the following: MLR:
= 36, R² = 0.829, RMS (root mean square) = 0.192, RBFNN:
= 36, R² = 0.843, RMS = 0.167 for Group 1; MLR:
= 60, R² = 0.803, RMS = 0.222, RBFNN:
= 60, R² = 0.821, RMS = 0.193 for Group 2; MLR:
= 31 R² = 0.852, RMS = 0.192; RBFNN:
= 31, R² = 0.885, RMS = 0.163 for Group 3, respectively. The results were within the acceptable range, and the models were found to be statistically robust with high external predictivity. Moreover, the models also gave some insight on those characteristics of the structures that most affect the toxicity.
Interest in gas shale, a novel source rock of natural gas, has increased tremendously in recent years. Better understanding of the kerogen–rock interaction is of crucial importance for efficient gas ...extraction and, hence, asset management. In this study, we explore the possible chemical bonds between kerogen and silica, one of the most predominant mineral constituents of gas shale, by means of quantum chemistry. Energetically favorable bond formation reactions are found between alcoholic hydroxyl, carboxylate, and aldehyde groups, as well as aliphatic double bonds of kerogen and the silica surface. The performance of a reactive force field was also assessed in a representative set of chemical reactions and found to be satisfactory. The potential impact of bond formation reactions between the two phases on the actual kerogen–silica interface is discussed as a function of the kerogen type, maturity, and density. Finally, a methodology aiming to reconstruct realistic kerogen–silica interfaces is presented.
The study of quantitative structure-property relationships (QSPR) is important to study complex networks of chemical reactions in drug synthesis or metabolism or drug-target interaction networks. A ...difficult but possible goal is the prediction of drug absorption, distribution, metabolism, and excretion (ADME) process with a single QSPR model. For this QSPR modelers need to use flexible structural parameters useful for the description of many different systems at different structural scales (multi-scale parameters). Also they need to use powerful analytical methods able to link in a single multi-scale hypothesis structural parameters of different target systems (multi-target modeling) with different experimental properties of these systems (multi-output models). In this sense, the QSPR study of complex bio-molecular systems may benefit substantially from the combined application of spectral moments of graph representations of complex systems with perturbation theory methods. On one hand, spectral moments are almost universal parameters that can be calculated to many different matrices used to represent the structure of the states of different systems. On the other hand, perturbation methods can be used to add "small" variation terms to parameters of a known state of a given system in order to approach to a solution of another state of the same or similar system with unknown properties. Here we present one state-of-art review about the different applications of spectral moments to describe complex bio-molecular systems. Next, we give some general ideas and formulate plausible linear models for a general-purpose perturbation theory of QSPR problems of complex systems. Last, we develop three new QSPR-Perturbation theory models based on spectral moments for three different problems with multiple in-out boundary conditions that are relevant to biomolecular sciences. The three models developed correctly classify more than pairs 115,600; 48,000; 134,900 cases of the effects of in-out perturbations in intra-molecular carbolithiations, drug ADME process, or self-aggregation of micelle nanoparticles of drugs or surfactants. The Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp) of these models were >90% in all cases. The first model predicts variations in the yield or enantiomeric excess due to structural variations or changes in the solvent, temperature, temperature of addition, or time of reaction. The second model predicts changes in >18 parameters of biological effects for >3000 assays of ADME properties and/or interactions between 31,723 drugs and 100 targets (metabolizing enzymes, drug transporters, or organisms). The third model predicts perturbations due to changes in temperature, solvent, salt concentration, and/or structure of anions or cations in the self-aggregation of micelle nanoparticles of drugs and surfactants.
Parkinson's Disease (PD) is a long-term neurodegenerative brain disorder that mainly
affects the motor system. The causes are still unknown, and even though currently there is no cure,
several ...therapeutic options are available to manage its symptoms. The development of novel antiparkinsonian
agents and an understanding of their proper and optimal use are, indeed, highly demanding.
For the last decades, L-3,4-DihydrOxyPhenylAlanine or levodopa (L-DOPA) has been
the gold-standard therapy for the symptomatic treatment of motor dysfunctions associated to PD.
However, the development of dyskinesias and motor fluctuations (wearing-off and on-off phenomena)
associated with long-term L-DOPA replacement therapy have limited its antiparkinsonian
efficacy. The investigation for non-dopaminergic therapies has been largely explored as an attempt
to counteract the motor side effects associated with dopamine replacement therapy. Being one of
the largest cell membrane protein families, G-Protein-Coupled Receptors (GPCRs) have become a
relevant target for drug discovery focused on a wide range of therapeutic areas, including Central
Nervous System (CNS) diseases. The modulation of specific GPCRs potentially implicated in PD,
excluding dopamine receptors, may provide promising non-dopaminergic therapeutic alternatives
for symptomatic treatment of PD. In this review, we focused on the impact of specific GPCR subclasses,
including dopamine receptors, adenosine receptors, muscarinic acetylcholine receptors,
metabotropic glutamate receptors, and 5-hydroxytryptamine receptors, on the pathophysiology of
PD and the importance of structure- and ligand-based in silico approaches for the development of
small molecules to target these receptors.
Malaria is nowadays a worldwide and serious problem with a significant social, economic, and human cost, mainly in developing countries. In addition, the emergence and spread of resistance to ...existing antimalarial therapies deteriorate the global malaria situation, and lead thus to an urgent need toward the design and discovery of new antimalarial drugs. In this work, a QSAR predictive model based on GETAWAY descriptors was developed which is able to explain with, only three variables, more than 77% of the variance in antimalarial potency and displays a good internal predictive ability (of 73.3% and 72.9% from leave-one-out cross-validation and bootstrapping analyses, respectively). The performance of the proposed model was judged against other five methodologies providing evidence of the superiority of GETAWAY descriptors in predicting the antimalarial potency of the bisbenzamidine family. Moreover, a desirability analysis based on the final QSAR model showed that to be a useful way of selecting the predictive variable level necessary to obtain potent bisbenzamidines. From the proposed model it is also possible to infer that elevated high atomic masses/polarizabilities/van der Waals volumes could play a negative/positive/positive role in the molecular interactions responsible for the desired drug conformation, which is required for the optimal binding to the macromolecular target. The results obtained point out that our final QSAR model is statistically significant and robust as well as possessing a high predictive effectiveness. Thus, the model provides a feasible and practical tool for looking for new and potent antimalarial bisbenzamidines.
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