Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence.

The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation.

Rational Design of a Potential New Nematicide Targeting Chitin Deacetylase.

In a previously published study, the authors devised a molecular topology QSAR (quantitative structure-activity relationship) approach to detect novel fungicides acting as inhibitors of chitin deacetylase (CDA). Several of the chosen compounds exhibited noteworthy activity. Due to the close relationship between chitin-related proteins present in fungi and other chitin-containing plant-parasitic species, the authors decided to test these molecules against nematodes, based on their negative impact on agriculture.

A Review of Computational Approaches Targeting SARS-CoV-2 Main Protease to the Discovery of New Potential Antiviral Compounds.

The new pandemic caused by the coronavirus (SARS-CoV-2) has become the biggest challenge that the world is facing today. It has been creating a devastating global crisis, causing countless deaths and great panic. The search for an effective treatment remains a global challenge owing to controversies related to available vaccines.

Computational Identification of Chemical Compounds with Potential Activity against Leishmania amazonensis using Nonlinear Machine Learning Techniques.

Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of leishmaniasis exhibit several drawbacks including toxicity, high costs and route of administration. Consequently, the development of new treatments for leishmaniasis is a priority in the field of neglected tropical diseases.

QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations.

Background: In previous reports, Marrero-Ponce et al. proposed algebraic formalisms for characterizing topological (2D) and chiral (2.5D) molecular features through atom- and bond-based ToMoCoMD-CARDD (acronym for Topological Molecular Computational Design-Computer Aided Rational Drug Design) molecular descriptors.

Generalized Molecular Descriptors Derived From Event-Based Discrete Derivative.

In the present study, a generalized approach for molecular structure characterization is introduced, based on the relation frequency matrix (F) representation of the molecular graph and the subsequent calculation of the corresponding discrete derivative (finite difference) over a pair of elements (atoms). In earlier publications (22- 24), an unique event, named connected subgraphs, (based on the Kier-Hall's subgraphs) was systematically employed for the computation of the matrix F. The present report is a generalization of this notion, in which eleven additional events are introduced, classified in three categories, namely, topological (terminal paths, vertex path incidence, quantum subgraphs, walks of length k, Sach's subgraphs), fingerprints (MACCs, E-state and substructure fingerprints) and atomic contributions (Ghose and Crippen atom-types for hydrophobicity and refractivity) for F generation.

Multi-output Model with Box-Jenkins Operators of Quadratic Indices for Prediction of Malaria and Cancer Inhibitors Targeting Ubiquitin- Proteasome Pathway (UPP) Proteins.

The ubiquitin-proteasome pathway (UPP) is the primary degradation system of short-lived regulatory proteins. Cellular processes such as the cell cycle, signal transduction, gene expression, DNA repair and apoptosis are regulated by this UPP and dysfunctions in this system have important implications in the development of cancer, neurodegenerative, cardiac and other human pathologies. UPP seems also to be very important in the function of eukaryote cells of the human parasites like Plasmodium falciparum, the causal agent of the neglected disease Malaria.

Multi-output model with Box-Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin-proteasome pathway.

The ubiquitin-proteasome pathway (UPP) plays an important role in the degradation of cellular proteins and regulation of different cellular processes that include cell cycle control, proliferation, differentiation, and apoptosis. In this sense, the disruption of proteasome activity leads to different pathological states linked to clinical disorders such as inflammation, neurodegeneration, and cancer. The use of UPP inhibitors is one of the proposed approaches to manage these alterations.

IMMAN: free software for information theory-based chemometric analysis.

The features and theoretical background of a new and free computational program for chemometric analysis denominated IMMAN (acronym for Information theory-based CheMoMetrics ANalysis) are presented. This is multi-platform software developed in the Java programming language, designed with a remarkably user-friendly graphical interface for the computation of a collection of information-theoretic functions adapted for rank-based unsupervised and supervised feature selection tasks. A total of 20 feature selection parameters are presented, with the unsupervised and supervised frameworks represented by 10 approaches in each case.

Discrete Derivatives for Atom-Pairs as a Novel Graph-Theoretical Invariant for Generating New Molecular Descriptors: Orthogonality, Interpretation and QSARs/QSPRs on Benchmark Databases.

This report presents a new mathematical method based on the concept of the derivative of a molecular graph (G) with respect to a given event (S) to codify chemical structure information. The derivate over each pair of atoms in the molecule is defined as ∂G/∂S(vi  , vj )=(fi -2fij +fj )/fij , where fi (or fj ) and fij are the individual frequency of atom i (or j) and the reciprocal frequency of the atoms i and j, respectively. These frequencies characterize the participation intensity of atom pairs in S.

Trends in information theory-based chemical structure codification.

This report offers a chronological review of the most relevant applications of information theory in the codification of chemical structure information, through the so-called information indices. Basically, these are derived from the analysis of the statistical patterns of molecular structure representations, which include primitive global chemical formulae, chemical graphs, or matrix representations. Finally, new approaches that attempt to go "back to the roots" of information theory, in order to integrate other information-theoretic measures in chemical structure coding are discussed.

Antiprotozoan lead discovery by aligning dry and wet screening: prediction, synthesis, and biological assay of novel quinoxalinones.

Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities.

Shannon's, mutual, conditional and joint entropy information indices: generalization of global indices defined from local vertex invariants.

A new mathematical approach is proposed in the definition of molecular descriptors (MDs) based on the application of information theory concepts. This approach stems from a new matrix representation of a molecular graph (G) which is derived from the generalization of an incidence matrix whose row entries correspond to connected subgraphs of a given G, and the calculation of the Shannon's entropy, the negentropy and the standardized information content, plus for the first time, the mutual, conditional and joint entropy-based MDs associated with G. We also define strategies that generalize the definition of global or local invariants from atomic contributions (local vertex invariants, LOVIs), introducing related metrics (norms), means and statistical invariants.

Ligand-based discovery of novel trypanosomicidal drug-like compounds: in silico identification and experimental support.

Two-dimensional bond-based linear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A database with 143 anti-trypanosomal and 297 compounds having other clinical uses, are utilized to develop the theoretical models. The best discriminant models computed using bond-based linear indices provides accuracies greater than 90 for both training and test sets.

TOMOCOMD-CAMPS and protein bilinear indices--novel bio-macromolecular descriptors for protein research: I. Predicting protein stability effects of a complete set of alanine substitutions in the Arc repressor.

Descriptors calculated from a specific representation scheme encode only one part of the chemical information. For this reason, there is a need to construct novel graphical representations of proteins and novel protein descriptors that can provide new information about the structure of proteins. Here, a new set of protein descriptors based on computation of bilinear maps is presented.

Bond-based linear indices of the non-stochastic and stochastic edge-adjacency matrix. 1. Theory and modeling of ChemPhys properties of organic molecules.

Novel bond-level molecular descriptors are proposed, based on linear maps similar to the ones defined in algebra theory. The kth edge-adjacency matrix (E(k)) denotes the matrix of bond linear indices (non-stochastic) with regard to canonical basis set. The kth stochastic edge-adjacency matrix, ES(k), is here proposed as a new molecular representation easily calculated from E(k).

Nucleotide's bilinear indices: novel bio-macromolecular descriptors for bioinformatics studies of nucleic acids. I. Prediction of paromomycin's affinity constant with HIV-1 Psi-RNA packaging region.

A new set of nucleotide-based bio-macromolecular descriptors are presented. This novel approach to bio-macromolecular design from a linear algebra point of view is relevant to nucleic acids quantitative structure-activity relationship (QSAR) studies. These bio-macromolecular indices are based on the calculus of bilinear maps on Re(n)[b(mk)(x (m),y (m)):Re(n) x Re(n)-->Re] in canonical basis.

Atom- and bond-based 2D TOMOCOMD-CARDD approach and ligand-based virtual screening for the drug discovery of new tyrosinase inhibitors.

Two-dimensional atom- and bond-based TOMOCOMD-CARDD descriptors and linear discriminant analysis (LDA) are used in this report to perform a quantitative structure-activity relationship (QSAR) study of tyrosinase-inhibitory activity. A database of inhibitors of the enzyme is collected for this study, within 246 highly dissimilar molecules presenting antityrosinase activity. In total, 7 discriminant functions are obtained by using the whole set of atom- and bond-based 2D indices.

Artificial neural networks and linear discriminant analysis: a valuable combination in the selection of new antibacterial compounds.

A set of topological descriptors has been used to discriminate between antibacterial and nonantibacterial drugs. Topological descriptors are simple integers calculated from the molecular structure represented in SMILES format. The methods used for antibacterial activity discrimination were linear discriminant analysis (LDA) and artificial neural networks of a multilayer perceptron (MLP) type.

Discrimination and selection of new potential antibacterial compounds using simple topological descriptors.

The aim of the work was to discriminate between antibacterial and non-antibacterial drugs by topological methods and to select new potential antibacterial agents from among new structures. The method used for antibacterial activity selection was a linear discriminant analysis (LDA). It is possible to obtain a QSAR interpretation of the information contained in the discriminant function.