Overview of Computational Toxicology Methods Applied in Drug and Green Chemical Discovery.

In the field of computational chemistry, computer models are quickly and cheaply constructed to predict toxicology hazards and results, with no need for test material or animals as these computational predictions are often based on physicochemical properties of chemical structures. Multiple methodologies are employed to support in silico assessments based on machine learning (ML) and deep learning (DL). This review introduces the development of computational toxicology, focusing on ML and DL and emphasizing their importance in the field of toxicology.

Synthesis of Quinolones and Zwitterionic Quinolonate Derivatives with Broad-Spectrum Antibiotic Activity.

Quinolones are one of the most extensively used therapeutic families of antibiotics. However, the increase in antibiotic-resistant bacteria has rendered many of the available compounds useless. After applying our prediction model of activity against to a library of 1000 quinolones, two quinolones were selected to be synthesized.

New Pharmacokinetic and Microbiological Prediction Equations to Be Used as Models for the Search of Antibacterial Drugs.

Currently, the development of resistance of bacteria is one of the most important health problems worldwide. Consequently, there is a growing urge for finding new compounds with antibacterial activity. Furthermore, it is very important to find antibacterial compounds with a good pharmacokinetic profile too, which will lead to more efficient and safer drugs.

Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design.

Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development.

Molecular Topology for the Search of New Anti-MRSA Compounds.

The variability of methicillin-resistant (MRSA), its rapid adaptive response against environmental changes, and its continued acquisition of antibiotic resistance determinants have made it commonplace in hospitals, where it causes the problem of multidrug resistance. In this study, we used molecular topology to develop several discriminant equations capable of classifying compounds according to their anti-MRSA activity. Topological indices were used as structural descriptors and their relationship with anti-MRSA activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds.

Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds Against .

Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) model to predict antibacterial activity against Escherichia coli. The model consists on a hierarchical decision tree in which a discrete index is used to divide compounds into groups according to their values for said index in order to construct probability spaces.

Molecular Topology for the Discovery of New Broad-Spectrum Antibacterial Drugs.

In this study, molecular topology was used to develop several discriminant equations capable of classifying compounds according to their antibacterial activity. Topological indices were used as structural descriptors and their relation to antibacterial activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds. Four equations were constructed, named DF1, DF2, DF3, and DF4, all with good statistical parameters such as Fisher-Snedecor's F (over 25 in all cases), Wilk's lambda (below 0.

Topological index as a factor determining the antibacterial activity of quinolones against .

Due to antibiotic resistance and the lack of investment in antimicrobial R&D, quantitative structure-activity relationship (SAR) methods appear as an ideal approach for the discovery of new antibiotics. Molecular topology and linear discriminant analysis were used to construct a model to predict activity against . This model establishes new SARs, of which, molecular size and complexity (), stand out for their discriminant power.

New solvent options for assays in the larvae model.

Experimentation in mammals is a long and expensive process in which ethical aspects must be considered, which has led the scientific community to develop alternative models such as that of . This model is a cost and time effective option to act as a filter in the drug discovery process. The main limitation of this model is the lack of variety in the solvents used to administer compounds, which limits the compounds that can be studied using this model.

Obtaining Microbiological and Pharmacokinetic Highly Predictive Equations.

In this paper, a Multilinear Regression (MLR) analysis has been carried out in order to accurately predict physicochemical properties and biological activities of a group of antibacterial quinolones by means of a set of structural descriptors called topological indices. The aim of this work is to develop prediction equations for these properties after collecting the maximum number of data from the literature on antibacterial quinolones. The five regression functions selected by presenting the best combination of various statistical parameters, subsequently validated by means of internal validation (intercorrelation, Y-randomization and leave-one-out cross-validation tests), allowed the reliable prediction of minimum inhibitory concentration 50 versus Staphylococcus aureus (MIC50Sa), Streptococcus pyogenes (MIC50Spy) and Bacteroides fragilis (MIC50Bf), Mean Residence Time (MRT) after oral administration and volume of distribution (VD).

Topological pattern for the search of new active drugs against methicillin resistant Staphylococcus aureus.

Molecular topology was used to develop a mathematical model capable of classifying compounds according to antimicrobial activity against methicillin resistant Staphylococcus aureus (MRSA). Topological indices were used as structural descriptors and their relation to antimicrobial activity was determined by using linear discriminant analysis. This topological model establishes new structure activity relationships which show that the presence of cyclopropyl, chlorine and ramification pairs at a distance of two bonds favor this activity, while the presence of tertiary amines decreases it.

Topological Model for the Search of New Antibacterial Drugs. 158 Theoretical Candidates.

In this paper, molecular topology was used to develop a mathematical model capable of classifying compounds according to their antibacterial activity. Topological indices were used as structural descriptors and their relation to antibacterial activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones, widely used nowadays because of their broad spectrum of activity, well tolerance profile and advantageous pharmacokinetic properties. The topological model of activity obtained included two discriminant functions, selected by a combination of various statistical paremeters such as Fisher-Snedecor F and Wilk's lambda, and allows the reliable prediction of antibacterial activity in any organic compound.

Application of a mathematical topological pattern of antihistaminic activity for the selection of new drug candidates and pharmacology assays.

Molecular topology was used to achieve a mathematical model capable of classifying compounds according to their antihistaminic activity and low sedative effects. By application of this model of activity to databases containing chemical reagents and drugs exhibiting other pharmacological activity, we selected 30 compounds with possible antihistaminic activity. After those with possible sedative effects were discarded, activity tests were performed with five chemical reagents and three drugs searching for in vivo antihistaminic activity.

New potential antihistaminic compounds. Virtual combinatorial chemistry, computational screening, real synthesis, and pharmacological evaluation.

To study the utility of the virtual combinatorial chemistry coupled with computational screening, a library of amine and urea derivatives was designed by virtual combinatorial synthesis and eventually computationally screened by a mathematical topological model as antihistaminic compounds. The results reveal that virtual combinatorial synthesis and virtual screening together with molecular topology are a powerful tool in the design of new drugs.