Multitarget Approach to Drug Candidates against Alzheimer's Disease Related to AChE, SERT, BACE1 and GSK3β Protein Targets.

Alzheimer's disease is a neurodegenerative condition for which currently there are no drugs that can cure its devastating impact on human brain function. Although there are therapeutics that are being used in contemporary medicine for treatment against Alzheimer's disease, new and more effective drugs are in great demand. In this work, we proposed three potential drug candidates which may act as multifunctional compounds simultaneously toward AChE, SERT, BACE1 and GSK3β protein targets.

Identification of Natural Compounds against Neurodegenerative Diseases Using In Silico Techniques.

The aim of this study was to identify new potentially active compounds for three protein targets, tropomyosin receptor kinase A (TrkA), -methyl-d-aspartate (NMDA) receptor, and leucine-rich repeat kinase 2 (LRRK2), that are related to various neurodegenerative diseases such as Alzheimer's, Parkinson's, and neuropathic pain. We used a combination of machine learning methods including artificial neural networks and advanced multilinear techniques to develop quantitative structure⁻activity relationship (QSAR) models for all target proteins. The models were applied to screen more than 13,000 natural compounds from a public database to identify active molecules.

Indole-like Trk receptor antagonists.

The virtual screening for new scaffolds for TrkA receptor antagonists resulted in potential low molecular weight drug candidates for the treatment of neuropathic pain and cancer. In particular, the compound (Z)-3-((5-methoxy-1H-indol-3-yl)methylene)-2-oxindole and its derivatives were assessed for their inhibitory activity against Trk receptors. The IC50 values were computationally predicted in combination of molecular and fragment-based QSAR.

In silico machine learning methods in drug development.

Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic targets. These methods are particularly useful for screening compound libraries of diverse chemical structures, "noisy" and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods.

Rational design of a series of novel amphipathic cell-penetrating peptides.

A series of novel, amphipathic cell-penetrating peptides was developed based on a combination of the model amphipathic peptide sequence and modifications based on the strategies developed for PepFect and NickFect peptides. The aim was to study the role of amphipathicity for peptide uptake and to investigate if the modifications developed for PepFect peptides could be used to improve the uptake of another class of cell-penetrating peptides. The peptides were synthesized by solid phase peptide synthesis and characterized by circular dichroism spectroscopy.

Fragment-based development of HCV protease inhibitors for the treatment of hepatitis C.

A novel computational technology based on fragmentation of the chemical compounds has been used for the fast and efficient prediction of activities of prospective protease inhibitors of the hepatitis C virus. This study spans over a discovery cycle from the theoretical prediction of new HCV NS3 protease inhibitors to the first cytotoxicity experimental tests of the best candidates. The measured cytotoxicity of the compounds indicated that at least two candidates would be suitable further development of drugs.

Novel carboxamides as potential mosquito repellents.

A model was developed using 167 carboxamide derivatives, from the United States Department of Agriculture archival database, that were tested as arthropod repellents over the past 60 yr. An artificial neural network employing CODESSA PRO descriptors was used to construct a quantitative structure-activity relationship model for prediction of novel mosquito repellents. By correlating the structure of these carboxamides with complete protection time, a measure of repellency based on duration, 34 carboxamides were predicted as candidate mosquito repellents.

A general treatment of solubility 4. Description and analysis of a PCA model for ostwald solubility coefficients.

Principal component analysis (PCA) of a large data matrix (153 solvents x 396 solutes) for Ostwald solubility coefficients (log L) resulted in a two-component model covering 98.6% of the variability. Analysis of the principal components exposed the structural characteristics of solutes and solvents that codify interactions which determine the behavior of a chemical in the surrounding media.

Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks.

An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration.

Legitimate utilization of large descriptor pools for QSPR/QSAR models.

The use of large descriptor pools in multilinear QSAR/QSPR approaches has recently been increasingly criticized for their sensitivity to "chance correlations". Statistical experiments substituting "real descriptor" pools by random numbers were stated to demonstrate such sensitivity. While contributing positively to the improvement of the QSAR/QSPR methodology, these approaches claim complete interchangeability between the molecular descriptors used in QSAR/QSPR models and random numbers.

QSAR modeling of the antifungal activity against Candida albicans for a diverse set of organic compounds.

The molecular structures of 83 diverse organic compounds are correlated by a quantitative structure-activity relationship (QSAR) to their minimum inhibitor concentrations (MIC expressed as log(1/MIC)), involving 6 descriptors with R(2)=0.788, F=47.140, s(2)=0.

Novel computational models for predicting dopamine interactions.

Dopamine is a crucial neurotransmitter responsible for functioning and maintenance of the nervous system. Dopamine has also been implicated in a number of diseases including schizophrenia, Parkinson's disease and drug addiction. Dopamine agonists are used in early Parkinson's disease treatment.

QSPR modeling of UV absorption intensities.

Literature UV absorption intensities at 260 nm and 25 degrees C in water of a diverse set of 805 organic compounds when analyzed by CODESSA Pro software using an initial pool of 800 + descriptors provide a significant QSPR correlation (R (2) = 0.692). Concurrently, a neural networks approach was used to develop a corresponding nonlinear model.

QSPR modeling of flash points: an update.

Quantitative structure-property relationship (QSPR) models for the flash points of 758 organic compounds are developed using geometrical, topological, quantum mechanical and electronic descriptors calculated by CODESSA PRO software. Multilinear regression models link the structures to their reported flash point values. We also report a nonlinear model based on an artificial neural network.

Neural networks convergence using physicochemical data.

An investigation of the neural network convergence and prediction based on three optimization algorithms, namely, Levenberg-Marquardt, conjugate gradient, and delta rule, is described. Several simulated neural networks built using the above three algorithms indicated that the Levenberg-Marquardt optimizer implemented as a back-propagation neural network converged faster than the other two algorithms and provides in most of the cases better prediction. These conclusions are based on eight physicochemical data sets, each with a significant number of compounds comparable to that usually used in the QSAR/QSPR modeling.

QSAR study of antiplatelet agents.

A QSAR methodology that involves multilinear (Hansch-type) and nonlinear (ANN backpropagation) approaches was developed to correlate the antiplatelet activity of 60 benzoxazinone derivatives against factor Xa. The statistical characteristics provided by multilinear model (R2 = 0.821) indicated satisfactory stability and predictive ability, while the ANN predictive ability is somewhat superior (R2 = 0.

QSAR modeling of anti-invasive activity of organic compounds using structural descriptors.

The anti-invasive activity of 139 compounds was correlated by an artificial neural network approach with descriptors calculated solely from the molecular structures using CODESSA Pro. The best multilinear regression method implemented in CODESSA Pro was used for a pre-selection of descriptors. The resulting nonlinear (artificial neural network) QSAR model predicted the exact class for 66 (71%) of the training set of 93 compounds and 32 (70%) of validation set of 46 compounds.

Skin permeation rate as a function of chemical structure.

Multilinear and nonlinear QSAR models were built for the skin permeation rate (Log K(p)) of a set of 143 diverse compounds. Satisfactory models were obtained by three approaches applied: (i) CODESSA PRO, (ii) Neural Network modeling using large pools of theoretical molecular descriptors, and (iii) ISIDA modeling based on fragment descriptors. The predictive abilities of the models were assessed by internal and external validations.

Correlation of blood-brain penetration using structural descriptors.

Experimental blood-brain partition coefficients (logBB) for a diverse set of 113 drug molecules are correlated with computed structural descriptors using CODESSA-PRO and ISIDA programs to give statistically significant QSAR models based respectively, on molecular and on fragment descriptors. The linear correlation CODESSA-PRO five-descriptor model has correlation coefficient R2=0.781 and standard deviation s2=0.

QSAR modeling of the inhibition of glycogen synthase kinase-3.

Quantitative structure-activity relationship (QSAR) models of the biological activity (pIC50) of 277 inhibitors of Glycogen Synthase Kinase-3 (GSK-3) are developed using geometrical, topological, quantum mechanical, and electronic descriptors calculated by CODESSA PRO. The linear (multilinear regression) and nonlinear (artificial neural network) models obtained link the structures to their reported activity pIC50. The results are discussed in the light of the main factors that influence the inhibitory activity of the GSK-3 enzyme.

QSAR study of mosquito repellents using Codessa Pro.

Protection times provided by 31 synthetic repellents against Aedes aegypti mosquitoes were correlated with the chemical structures of these repellents using Codessa Pro software. Two statistically significant quantitative models with R2 values of ca. 0.

Antimalarial activity: a QSAR modeling using CODESSA PRO software.

A quantitative structure-activity relationship (QSAR) modeling of the antimalarial activity of two diverse sets of compounds for each of two strains D6 and NF54 of Plasmodium falciparum is presented. The molecular structural features of compounds are presented by molecular descriptors (geometrical, topological, quantum mechanical, and electronic) calculated using the CODESSA PRO software. Satisfactory multilinear regression models were obtained for data sets of the D6 and NF54 strains, with R2 = 0.

Reparameterized Austin Model 1 for quantitative structure-property relationships in liquid media.

A reparameterization of the quantum-chemical AM1 (Austin Model 1) model has been carried out using a nonlinear optimization based on a modification of the Levenberg-Marquardt technique. The optimum numerical values for the one-electron resonance integral parameters (beta (s) and beta (p)) and core-core repulsion atomic parameters alpha were obtained for the elements H, C, N, O, Cl and Br using the statistical fit of a two-parameter QSPR equation for the boiling points of organic compounds. A substantially improved two-parameter correlation (R2=0.

QSAR studies on 1-phenylbenzimidazoles as inhibitors of the platelet-derived growth factor.

This work is devoted to the development of quantitative structure-activity relationship (QSAR) models of the biological activity of 123 1-phenylbenzimidazoles as inhibitors of the PDGF receptor. The molecular features are represented by chemical descriptors that have been calculated on geometrical, topological, quantum mechanical, and electronic basis by using CODESSA PRO. The obtained models, linear (multilinear regression) and nonlinear (artificial neural network), are aimed to link the structures to their reported activity log 1/IC50.