Molecular electrostatic potential and pattern recognition models to design potentially active pentamidine derivatives against Trypanosoma brucei rhodesiense

Molecular electrostatic potential (MEP) and pattern recognition (PR) were used to draw potentially active pentamidine derivatives against Trypanosome brucei rhodesiense (T. b. rhodesiense). PR models: Principal Component Analysis, PCA model; Hierarchical Cluster Analysis, HCA model; K-Nearest Neighbor, KNN model; Soft Independent Modeling of Class Analogy, SIMCA model; and Stepwise Discriminant Analysis, SDA model, were built by reducing the dimensionality of a data matrix to twenty-eight pentamidine derivatives and allowed the compounds to be classified into two classes: more active and less active, according to their degrees of activity against T. b. rhodesiense. The study outlined that the properties HOMO (highest occupied molecular orbital) energy, VOL (molecular volume), and ASA_P (water accessible surface area of all polar (½qi½³0. 2) atoms) are the most relevant for the construction of the models. The key structural features required for biological activity investigated through MEP were used as guidelines in the design of thirteen new compounds, which were evaluated by PR models as more active or less active against T. b. rhodesiense. The application of PR models indicated nine promising compounds (29, 30, 31, 32, 33, 36, 37, 39, and 40) for synthesis and biological assays.

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COMPUTATIONAL MODELING OF ANTIMALARIAL 10-SUBSTITUTED DEOXOARTEMISININS

Journal of Theoretical and Computational Chemistry ◽

10.1142/s0219633612500162 ◽

2012 ◽

Vol 11 (02) ◽

pp. 241-263 ◽

Cited By ~ 5

Author(s):

MARIA DA GLÓRIA G. CRISTINO ◽

CARLA CAROLINA F. DE MENESES ◽

MALÚCIA MARQUES SOEIRO ◽

JOÃO ELIAS V. FERREIRA ◽

ANTONIO FLORÊNCIO DE FIGUEIREDO ◽

...

Keyword(s):

Molecular Docking ◽

Electrostatic Potential ◽

Molecular Electrostatic Potential ◽

Nearest Neighbor ◽

Antimalarial Activity ◽

Principal Component ◽

Structural Features ◽

Orbital Energy ◽

Stepwise Discriminant Analysis ◽

K Nearest Neighbor

Nineteen 10-substitued deoxoartemisinin derivatives and artemisinin with activity against D-6 strains of malarial falciparum designated as Sierra Leone are studied. We use molecular electrostatic potential maps in an attempt to identify key structural features of the artemisinins that are necessary for their activities and molecular docking to investigate the interaction with the molecular receptor (heme). Chemometric modeling: Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), K-Nearest Neighbor (KNN), Soft Independent Modeling of Class Analogy (SIMCA) and Stepwise Discriminant Analysis (SDA) are employed to reduce dimensionality and investigate which subset of descriptors are responsible for the classification between more active (MA) and less active (LA) artemisinins. The PCA, HCA, KNN, SIMCA and SDA studies showed that the descriptors LUMO (Lowest Unoccupied Molecular Orbital) energy, DFeO1 (Distance between the O 1 atom from ligand and iron atom from heme), X1A (Average Connectivity Index Chi-1) and Mor15u (Molecular Representation of Structure Based on Electron Diffraction) code of signal 15, unweighted, are responsible for separating the artemisinins according to their degree of antimalarial activity. The prediction study was done with a new set of eight artemisinins by using the chemometric methods and five of them were predicted as active against D-6 strains of falciparum malaria. In order to verify if the key structural features that are necessary for their antimalarial activities were investigated for the interaction with the heme, we also carried out calculations of the molecular electrostatic potential (MEP) and molecular docking. MEP maps and molecular docking were analyzed for more active compounds of the prediction set.

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A data-driven methodology for the classification of different liquids in artificial taste recognition applications with a pulse voltammetric electronic tongue

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719881601 ◽

2019 ◽

Vol 15 (10) ◽

pp. 155014771988160 ◽

Cited By ~ 6

Author(s):

Jersson X Leon-Medina ◽

Leydi J Cardenas-Flechas ◽

Diego A Tibaduiza

Keyword(s):

Machine Learning ◽

Pattern Recognition ◽

Data Analysis ◽

Nearest Neighbor ◽

Electronic Tongue ◽

Sensor Arrays ◽

Principal Component ◽

Machine Learning Techniques ◽

Learning Approaches ◽

K Nearest Neighbor

Electronic tongue-type sensor arrays are devices used to determine the quality of substances and seek to imitate the main components of the human sense of taste. For this purpose, an electronic tongue-based system makes use of sensors, data acquisition systems, and a pattern recognition system. Particularly, in the latter, machine learning techniques are useful in data analysis and have been used to solve classification and regression problems. However, one of the problems in the use of this kind of device is associated with the development of reliable pattern recognition algorithms and robust data analysis. In this sense, this work introduces a taste recognition methodology, which is composed of several steps including unfolding data, data normalization, principal component analysis for compressing the data, and classification through different machine learning models. The proposed methodology is tested using data from an electronic tongue with 13 different liquid substances; this electronic tongue uses multifrequency large amplitude pulse signal voltammetry. Results show that the methodology is able to perform the classification accurately and the best results are obtained when it includes the use of K-nearest neighbor machine in terms of accuracy compared with other kinds of machine learning approaches. Besides, the comparison to evaluate the methodology is made with different classification performance measures that show the behavior of the process in a single number.

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NMR pattern recognition of peracetylated mono- and oligosaccharide structures. Classification of residues using principal-component analysis, K-nearest neighbor analysis, and SIMCA class modeling

Journal of Magnetic Resonance (1969) ◽

10.1016/0022-2364(89)90237-0 ◽

1989 ◽

Vol 85 (3) ◽

pp. 457-469

Author(s):

Warren J Goux

Keyword(s):

Principal Component Analysis ◽

Pattern Recognition ◽

Nearest Neighbor ◽

Principal Component ◽

Component Analysis ◽

K Nearest Neighbor ◽

Nearest Neighbor Analysis ◽

Oligosaccharide Structures

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Similarity of molecular electrostatic potential distributions in a series of HMG-CoA reductase inhibitors. Preliminary results

Journal de Chimie Physique ◽

10.1051/jcp/1991882639 ◽

1991 ◽

Vol 88 ◽

pp. 2639-2644 ◽

Cited By ~ 1

Author(s):

U Cosentino ◽

G Moro ◽

D Pitea

Keyword(s):

Electrostatic Potential ◽

Molecular Electrostatic Potential ◽

Hmg Coa Reductase ◽

Preliminary Results ◽

Reductase Inhibitors ◽

Hmg Coa Reductase Inhibitors ◽

Coa Reductase

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Principal component analysis of substituent constants

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc19900055 ◽

1990 ◽

Vol 55 (1) ◽

pp. 55-62 ◽

Cited By ~ 1

Author(s):

Drahomír Hnyk

Keyword(s):

Principal Component Analysis ◽

Inductive Effect ◽

Resonance Effect ◽

Principal Component ◽

Component Analysis ◽

Total Variance ◽

Data Matrix ◽

Cumulative Proportion ◽

Two Factors ◽

Substituent Constants

The principal component analysis has been applied to a data matrix formed by 7 usual substituent constants for 38 substituents. Three factors are able to explain 99.4% cumulative proportion of total variance. Several rotations have been carried out for the first two factors in order to obtain their physical meaning. The first factor is related to the resonance effect, whereas the second one expresses the inductive effect, and both together describe 97.5% cumulative proportion of total variance. Their mutual orthogonality does not directly follow from the rotations carried out. With the help of these factors the substituents are divided into four main classes, and some of them assume a special position.

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Theoretical study of hydrogen bonding interactions in substituted nitroxide radicals

New Journal of Chemistry ◽

10.1039/d0nj05362g ◽

2021 ◽

Author(s):

Thufail M. Ismail ◽

Neetha Mohan ◽

P. K. Sajith

Keyword(s):

Hydrogen Bonding ◽

Interaction Energy ◽

Electrostatic Potential ◽

Molecular Electrostatic Potential ◽

Theoretical Study ◽

Nitroxide Radicals ◽

Hydrogen Bonding Interactions ◽

Bonded Complexes ◽

Hydrogen Bonded

Interaction energy (Eint) of hydrogen bonded complexes of nitroxide radicals can be assessed in terms of the deepest minimum of molecular electrostatic potential (Vmin).

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