Testing selected optimal descriptors with artificial neural networks

2013

Eleven properties have been modeled with the objective of checking the importance for model purposes of mixed descriptors made of empirical parameters, molecular connectivity indices and random numbers. The mixed descriptors with random indices have a descriptive character which is satisfactorily confirmed by the leave-one-out method of statistical analysis. The introduction of a partition of the set of compounds into training and evaluation sets decreases drastically the probability to find a mixed descriptor with random indices with good model quality. Two properties, the magnetic susceptibility and the elutropic values, insist on having optimal descriptors with random indices. The overal…

Superposing significant interaction rules (SSIR) method: a simple procedure for rapid ranking of congeneric compounds

2020

The Superposing Significant Interaction Rules (SSIR) method is revised and implemented. The method is a simple combinatorial procedure, which deals with in situ generated rules among a dichotomized congeneric molecular family, selecting the most probabilistically relevant ones. The mere counting of the number of relevant rules attached to new compounds generates a molecular ranking useful for database filtering, refinement and prediction. The algorithm only needs for a symbolic molecular representation and this allows for mining the database in a confidential manner. Third parties will not know the real compounds that are on the way to be worked out. The procedure is tested for a complete s…

Recent Advances in Computational Approaches for Designing Potential Anti-Alzheimer’s Agents

2017

Virtual Combinatorial Syntheses and Computational Screening of New Potential Anti-Herpes Compounds

1999

The activity of new anti-HSV-1 chemical structures, designed by virtual combinatorial chemical synthesis and selected by a computational screening, is determined by an in vitro assay. A virtual library of phenol esters and anilides was formed from two databases of building blocks: one with carbonyl fragments and the other containing both substituted phenoxy and phenylamino fragments. The library of virtually assembled compounds was computationally screened, and those compounds which were selected by our mathematical model as active ones were finally synthesized and tested. Our antiviral activity model is a "tandem" of four linear functions of topological graph-theoretical descriptors. A giv…

Checking the Efficacy of Two Basic Descriptors With a Set of Properties of Alkanes

2019

Several experimental properties of alkanes are described by means of multilinear models at the cross-validation level. The models have been obtained considering two main sets of descriptors: mathematically-based and experimental ones. The best models are obtained normally involving one of the two sets. The main goal of this work is to show how the theoretical descriptors are able to perform a competitive role against the experimental ones. This constitutes an important topic in the quantitative structure-property relationships field because the use of mathematical and in silico descriptors is validated as a proper tool for model building. Activity distributions of the properties and indices…

Search of Chemical Scaffolds for Novel Antituberculosis Agents

2005

3 A method to identify chemical scaffolds potentially active against Mycobacterium tuberculosis is presented. The molecular features of a set of structurally heterogeneous antituberculosis drugs were coded by means of structural invariants. Three tech- niques were used to obtain equations able to model the antituberculosis activity: linear discriminant analysis, multilinear re- gression, and shrinkage estimation-ridge regression. The model obtained was statistically validated through leave-n-out test, and an external set and was applied to a database for the search of new active agents. The selected compounds were assayed in vitro, and among those identified as active stand reserpine, N,N,N…

Molecular Rearrangement of an Aza-Scorpiand Macrocycle Induced by pH: A Computational Study †

2016

Rearrangements and their control are a hot topic in supramolecular chemistry due to the possibilities that these phenomena open in the design of synthetic receptors and molecular machines. Macrocycle aza-scorpiands constitute an interesting system that can reorganize their spatial structure depending on pH variations or the presence of metal cations. In this study, the relative stabilities of these conformations were predicted computationally by semi-empirical and density functional theory approximations, and the reorganization from closed to open conformations was simulated by using the Monte Carlo multiple minimum method Financial support by the Spanish Ministerio de Economía y Competitiv…

Computational Evaluation and In Vitro Validation of New Epidermal Growth Factor Receptor Inhibitors

2020

Background:The Epidermal Growth Factor Receptor (EGFR) is a transmembrane protein that acts as a receptor of extracellular protein ligands of the epidermal growth factor (EGF/ErbB) family. It has been shown that EGFR is overexpressed by many tumours and correlates with poor prognosis. Therefore, EGFR can be considered as a very interesting therapeutic target for the treatment of a large variety of cancers such as lung, ovarian, endometrial, gastric, bladder and breast cancers, cervical adenocarcinoma, malignant melanoma and glioblastoma.Methods:We have followed a structure-based virtual screening (SBVS) procedure with a library composed of several commercial collections of chemicals (615,46…

Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation

2018

Chemoinformatics methodologies such as QSAR/QSPR have been used for decades in drug discovery projects, especially for the finding of new compounds with therapeutic properties and the optimization of ADME properties on chemical series. The application of computational techniques in predictive toxicology is much more recent, and they are experiencing an increasingly interest because of the new legal requirements imposed by national and international regulations. In the pharmaceutical field, the US Food and Drug Administration (FDA) support the use of predictive models for regulatory decision-making when assessing the genotoxic and carcinogenic potential of drug impurities. In Europe, the REA…

<strong>Synthesis and Platinum (II) Complexes of Different Polyazacyclophane Receptors</strong>

2015

During the last years, research on coordination chemistry of platinum has aroused great interest due to their potential biological applications. Herein, we report the interaction of PtCI42- with different polyazacyclophanes containing a pyridine unit as aromatic spacer. Formation of complexes has been studied by 1H and 195Pt NMR spectroscopy. Analysis of the recorded spectra of D2O solutions containing L and PtCl42- in a 1:1 molar ratio at acidic pH shows the evolution with time of the 1H and 195Pt signals. Different crystal structures have been solved by X-ray diffraction analysis. At acidic pHs, the metal ion is coordinated by the central amino group of the macrocyclic cavity and three ch…

Ranking Series of Cancer-Related Gene Expression Data by Means of the Superposing Significant Interaction Rules Method

2020

The Superposing Significant Interaction Rules (SSIR) method is a combinatorial procedure that deals with symbolic descriptors of samples. It is able to rank the series of samples when those items are classified into two classes. The method selects preferential descriptors and, with them, generates rules that make up the rank by means of a simple voting procedure. Here, two application examples are provided. In both cases, binary or multilevel strings encoding gene expressions are considered as descriptors. It is shown how the SSIR procedure is useful for ranking the series of patient transcription data to diagnose two types of cancer (leukemia and prostate cancer) obtaining Area Under Recei…

QSPR with descriptors based on averages of vertex invariants. An artificial neural network study

2014

New type of indices, the mean molecular connectivity indices (MMCI), based on nine different concepts of mean are proposed to model, together with molecular connectivity indices (MCI), experimental parameters and random variables, eleven properties of organic solvents. Two model methodologies are used to test the different descriptors: the multilinear least-squares (MLS) methodology and the Artificial Neural Network (ANN) methodology. The top three quantitative structure–property relationships (QSPR) for each property are chosen with the MLS method. The indices of these three QSPRs were used to train the ANNs that selected the best training sets of indices to estimate the evaluation sets of…