Blood Serum Proteome-Mass Spectra (SP-MS) may allow detecting Proteome- Early Drug Induced Cardiac Toxicity Relationships (called here Pro-EDICToRs). However, due to the thousands of proteins in the SP, a more realistic alternative represents the identification of general Pro-EDICToRs patterns instead of a single protein marker. In this sense, we introduced a novel Cartesian 2D spectrum graph for SP-MS. Next, we calculated the graph node-overlapping parameters (nopk) to numerically characterize SPMS by using them as inputs for a Quantitative Proteome-Toxicity Relationship (QPTR) classifier for Pro-EDICToRs with accuracy higher than 80%. This QPTR approach is the result of adapting the classic blood proteome Quantitative Property-Structure Relationship models (QSPR) used in Chemometrics to low-mass molecules study. Principal Component Analysis (PCA) on the QPTR nopk values explains with one factor (F1) the 82.7% of variance. These nopk values were used to construct for the first time a Pro-EDICToRs Complex Network having samples as nodes linked by similarity between two samples edges. We compared the topology of two sub-networks for the cardiac toxicity and control samples and found extreme relative differences for the re-linking (P) and Zagreb (M2) indices (9.5 and 54.2 % respectively) out of 11 parameters. We also compared the subnetworks with the well-known ideal random networks including Barabasi-Albert, Kleinberg Small World, Erdos-Renyi, and Epsstein Power Law models. Finally, we proposed Partial Order (PO) schemes of the 115 samples based on LDA-probabilities, F1- scores and/or network node degrees. PCA-CN and LDA-PCA based POs with Tanimoto’s coefficients equal or higher than 0.75 are promising for the study of Pro-EDICToRs. These results show that simple QPTRs models based on MS graph numerical parameters are an interesting tool for proteome research.

Calcium is an essential element in living organism and one of the most important minerals in the development and maintenance of bones and teeth. Calcium channel blockers (CCBs) are chemical substances which affect the movement of the calcium ion, Ca²⁺, through its specific channels. It is well-known as an important clinical path the usage of calcium channel blockers in order to decrease blood pressure of patients with hypertension. There were identified three types of calcium channels: voltage-sensitive, receptor-operated and stretch-operated.

In history, the first concepts of QSAR appeared when Richet, Meyer and Overton studied the relation between water/lipid solubility and toxicity or narcosis and Fisher underlined the importance of the steric configuration of a compound in enzymatic processes. Nowadays, the efforts to improve the class of CCBs are ongoing. The design of new active molecules with an improved pharmacotoxicological profile than the already discovered compounds of its class represent a very important aspect in the new drug development.

Antimicrobial peptides, also called body defense peptides, are highly conserved chemical structures. As these peptides have a wide distribution across the animal and vegetal kingdoms, this suggests their fundamental role as part of the immune system. These peptides are used against a wide range of pathogens, such as negative- and positive-gram bacteria, microbacteria, fungus, viruses, etc. Their wide action spectrum makes them important targets for pharmaceutical industry in order to obtain new structures by using drug design. Contrary to antibiotics, antimicrobial peptides do not develop resistance, have a wide action spectrum over a short period of time and have a bactericide action.

In this chapter, we present a set of pharmaceutical descriptors (isoelectric point of peptides, hydrophobicity, weighted holistic invariant molecular index, contact energy between neighboring amino acids, molecular volume and area, polar area, internal dipole moment, etc.) involved in QSAR studies of antimicrobial peptides activity (melittin, indolicidin, mastoporan, etc). Also for a number of 66 antimicrobial derivates, obtained through computational mutagenesis, we have evaluated and presented their pharmaceutical descriptors with possible contribution on antimicrobial activities.

This chapter overviews recent reports on theoretic, computer-based development of new anticonvulsant agents, including applications of Comparative Molecular Field Analysis, Chemical Graph Theory, Molecular Docking, Similarity Measures, Pharmacophore postulation and Virtual Screening.

Quantitative Structure-Activity Relationships (QSAR) modeling tools play a critical role today in both drug design and environmental sciences. QSAR modeling seeks to discover and use mathematical relationships between molecular properties of the compounds (descriptors) and the often complex activity of interest. An extensive number of molecular descriptors exist which can and have been used to model a wide range of target activities. This complicates the task of selecting those that will be more suitable, especially when one tries to define an accurate, robust, predictive and (most importantly) interpretable model. Lately, recognition of the importance of the three-dimensionally (3D) structure and stereochemistry of molecules to their biological activity, and awareness of the limitations of classical approaches, led to many attempts to generate 3D descriptors either as a complement for 2D-QSAR models or for standalone 3D-QSAR models. This review describes the 3D descriptors available in the DRAGON software along with their successful applications primarily in Medicinal Chemistry, updating a previously published paper in Current Topics in Medicinal Chemistry (Helguera, A.M.; Combes, R.D.; González, M.P.; Cordeiro, M.N.D.S., 2008, 8, 1628-1655).

Drug development is a long and time-consuming process, which can take an average time of 10 years for the identification of one lead compound to be further tested in the preclinical phases. Quantitative Structure-Activity Relationships (QSAR)-based techniques are valuable tools for shortening the time of lead compound identification, but also for focusing and limiting the time-costly synthetic activities and biological/ADMET evaluations. This review reports an overview of the current research and potential applications of QSAR modelling tools in rational drug design. The chapter is set out in the same order in which QSAR models are generally built up, starting from the setup of the dataset for modelling, assembly of typical molecular descriptors and selection routes, followed on by an outline of the commonly used techniques for establishing the QSAR models and lastly, by a discussion of the most useful procedures for reliability and uncertainty assessment of the models for regulatory purposes.

To develop a model used to predict the cytotoxicity, the radical-scavenging activity of a training set of 13 molecules, 2-, or 2,6-di-tert-butyl- and 2-methoxysubstituted phenols was investigated by combining two distinct approaches: first, the inhibition rate constant (k_inh) using the induction period method for methacrylate polymerization initiated by benzoyl peroxide or 2,2´-azobisisobutyronitrile; and secondly, anti-DPPH (1,1’-diphenyl-2-picrylhydrazyl) activity by a DPPH scavenging test. Also, some descriptors, the homolytic phenolic O-H bond dissociation enthalpy (BDE), ionization potential (IP_koopman), HOMO (the highest occupied molecular orbital) and LUMO (the lowest unoccupied molecular orbital) were calculated by the DFT/B3LYP method. For both human submandibular gland carcinoma cells (HSG) and human gingival fibroblasts (HGF), a linear quantitative structure–activity relationship (QSAR) for 50% cytotoxic concentration (CC50) and k_inh, but not IP, anti-DPPH activity or octanol/water partition coefficients (log P) was observed (p<0.01). Also, a significant QSAR for the cytotoxicity towards HSG cells vs. BDE was observed (p<0.05). The acceptable QSAR obtained for the cytotoxicity vs. k_inh suggested that the cytotoxicity of complex phenols may be dependent on radical reactions. The cytotoxicity of 2- methoxyphenols toward HSG cells was related to their HOMO - LUMO gap (chemical hardness reactivity, η), possibly due to the bioreactivity of these compounds. This QSAR model showed that the cytotoxicity for a wide variety of complex and multisubstituted phenols might be mediated by a radical mechanism.

Quantitative structure activity relation (QSAR) towards COX-2 inhibitory activity were developed for three oxygen containing heterocycles namely, 3,4,6- triphenylpyran-2-ones, 2,3-diarylpyran-4-ones and 5-aryl-2,2-dialkyl-4-phenyl-3(2H) furanones. Regression and artificial back propagation neural network models were tested for fitting the data. For the individual data sets octanol-water partition coefficient, geometric and connectivity indices were highly correlated with activity. For the combined data set the extent of branching, and molecular shape factor had a positive correlation and molecular connectivity index had a negative correlation with COX-2 inhibitory activity. A 4-2-1 back propagation neural network model fitted the combined data set well (R²= 0.77, R²_adj =0.73, q² = 0.63, and F=243). The predictive capability of the neural network model was gauged by using part of the furanone data for learning and the rest for validation and systematically reducing the number of processing elements in the hidden layer.

QSAR paradigm depends on the application of the similarity principle that states that structurally similar compounds have similar physicochemical and biological properties. Similarity assessment is, therefore, critical to define pertinence of application of a QSAR model, designing adequate datasets for modeling purposes and in virtual screening of new potential active compounds at the beginning of a drug research and development campaign, when few substrates of a certain molecular target are known. This chapter will review recent studies related to similarity coefficients and different fingerprinting systems and feature weighting schemes used in similarity assessment. Applications of these advances in chemoinformatics are also briefly discussed, including similarity-based virtual screening, assessment of chemical libraries diversity and applicability domain estimation based on similarity measures.

Tyrosinase is an oxidoreductase enzyme (EC 1.14.18.1) involved in the two main steps of the biochemical melanin pathway. In humans it is also related to the process of freeradical scavenging avoiding UV-radiations side-effects. However, abnormal overproduction of melanin lead to hyperpigmentation, that includes, melanoma, lentigenes, age spots and other skin disorders. Therefore, the research of novel chemical with inhibitory activity against the enzyme remains as a challenge to scientific community. In this chapter we survey the results achieved in the elucidation of new tyrosinase inhibitors by using Quantitative Structure-Activity Relationships (QSAR) and TOMOCOMD-CARDD (TOpological MOlecular COMputational Design-Computer-Aided Rational Drug Design) approach. Later, the use of different chemometric, machine learning and artificial intelligence techniques for modeling the tyrosinase inhibitory activity is showed. Finally, it has been shown that the algorithm proposed in this chapter was being used to the ligand-based virtual screening of several in-house databases, and many classes of compounds from both natural and synthetic sources. These compounds were found to have potent inhibitory profiles against the enzyme compared to the current reference depigmenting agents, kojic acid and L-mimosine.

The number of studies in rational peptide design has increased tremendously over the last past decade and this tendency is likely to be mirrored in drug discovery and pharmaceutical biotechnology. Although peptides can be rapidly metabolized by proteolytic enzymes and have poor oral bioavailability, the low toxicity and great efficacy compared to existing drugs make them attractive in treatment of infectious and autoimmune diseases and in the development of new anti-infective drugs.

With the development of high-throughput screening assays, it is possible to get access to a large set of qualitative and quantitative activity values that can be correlated to the primary amino acid sequence and even the structure of peptides using computer-aided design such as quantitative structure-activity relationships (QSARs) and machine learning methods.

In this chapter, we will focus on the computer-aided peptide design that has been successfully used for the prediction of the major histocompatibility complex (MHC)- peptide binding and for the optimization of antimicrobial peptides (AMPs).

The Quantitative Structure Activity Relationship (QSAR) is a technique which quantifies the relationship between a physicochemical property of a drug and its biological activity. The present chapter aims at presenting some basic considerations concerning QSAR. These considerations include a historic background, some selected developments and some important descriptors including; electronic parameters, hydrophobic (Lipophilic) parameters and steric parameters. Finally, the application of QSAR on some selected biological structures is presented. Among these are 3'-azido-2',3'-dideoxythymidine (AZT); fulleropyrrolidine-1-carbodithioic acid 2; 3 and 4-substituted-benzyl esters; hydroxychalca- acetic acid-(4-pyrrolidin-1-yl-phenyl) ester and hydroxy-chalcoacetic acid-[2-(2- hydroxy-acetylchalcanyl)-4-pyrrolidin-1-yl-phenyl] ester.