NOVEL ALGORITHMS AND TOOLS FOR LIGAND-BASED DRUG DESIGN

Computer-aided drug design (CADD) has become an indispensible component in modern drug discovery projects. The prediction of physicochemical properties and pharmacological properties of candidate compounds effectively increases the probability for drug candidates to pass latter phases of clinic trials. Ligand-based virtual screening exhibits advantages over structure-based drug design, in terms of its wide applicability and high computational efficiency. The established chemical repositories and reported bioassays form a gigantic knowledgebase to derive quantitative structure-activity relationship (QSAR) and structure-property relationship (QSPR). In addition, the rapid advance of machine learning techniques suggests new solutions for data-mining huge compound databases. In this thesis, a novel ligand classification algorithm, Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), was reported for the prediction of diverse categorical pharmacological properties. LiCABEDS was successfully applied to model 5-HT1A ligand functionality, ligand selectivity of cannabinoid receptor subtypes, and blood-brain-barrier (BBB) passage. LiCABEDS was implemented and integrated with graphical user interface, data import/export, automated model training/ prediction, and project management. Besides, a non-linear ligand classifier was proposed, using a novel Topomer kernel function in support vector machine. With the emphasis on green high-performance computing, graphics processing units are alternative platforms for computationally expensive tasks. A novel GPU algorithm was designed and implemented in order to accelerate the calculation of chemical similarities with dense-format molecular fingerprints. Finally, a compound acquisition algorithm was reported to construct structurally diverse screening library in order to enhance hit rates in high-throughput screening

QSAR study and molecular design of open-chain enaminones as anticonvulsant agents

Present work employs the QSAR formalism to predict the ED50 anticonvulsant activity of ringed-enaminones, in order to apply these relationships for the prediction of unknown open-chain compounds containing the same types of functional groups in their molecular structure. Two different modeling approaches are applied with the purpose of comparing the consistency of our results: (a) the search of molecular descriptors via multivariable linear regressions; and (b) the calculation of flexible descriptors with the CORAL (CORrelation And Logic) program. Among the results found, we propose some potent candidate open-chain enaminones having ED50 values lower than 10 mg·kg-1 for corresponding pharmacological studies. These compounds are classified as Class 1 and Class 2 according to the Anticonvulsant Selection Project.Instituto de Investigaciones Fisicoquímicas Teóricas y AplicadasFacultad de Ciencias Exacta

Predicting Complexation Thermodynamic Parameters of β-Cyclodextrin with Chiral Guests by Using Swarm Intelligence and Support Vector Machines

The Particle Swarm Optimization (PSO) and Support Vector Machines (SVMs) approaches are used for predicting the thermodynamic parameters for the 1:1 inclusion complexation of chiral guests with β-cyclodextrin. A PSO is adopted for descriptor selection in the quantitative structure-property relationships (QSPR) of a dataset of 74 chiral guests due to its simplicity, speed, and consistency. The modified PSO is then combined with SVMs for its good approximating properties, to generate a QSPR model with the selected features. Linear, polynomial, and Gaussian radial basis functions are used as kernels in SVMs. All models have demonstrated an impressive performance with R2 higher than 0.8

QSAR analysis on tacrine-related acetylcholinesterase inhibitors

The evaluation of the clinical effects of Tacrine has shown efficacy in delaying the deterioration of the symptoms of Alzheimer's disease, while confirming the adverse events consisting mainly in the elevated liver transaminase levels. The study of tacrine analogs presents a continuous interest, and for this reason we establish Quantitative Structure-Activity Relationships on their Acetylcholinesterase inhibitory activity. Ten groups of new developed Tacrine-related inhibitors are explored, which have been experimentally measured in different biochemical conditions and AChE sources. The number of included descriptors in the structure-activity relationship is characterized by 'Rule of Thumb'. The 1502 applied molecular descriptors could provide the best linear models for the selected Alzheimer's data base and the best QSAR model is reported for the considered data sets. The QSAR models developed in this work have a satisfactory predictive ability, and are obtained by selecting the most representative molecular descriptors of the chemical structure, represented through more than a thousand of constitutional, topological, geometrical, quantum-mechanical and electronic descriptor types.Fil: Wong, Kai Y.. Imperial College London; Reino UnidoFil: Mercader, Andrew Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Saavedra Reyes, Laura Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Centro de Investigación y Desarrollo En Ciencias Aplicadas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Honarparvar, Bahareh. University of Kwazulu-Natal; SudáfricaFil: Romanelli, Gustavo Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Centro de Investigación y Desarrollo En Ciencias Aplicadas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina. Universidad Nacional de La Plata; Argentin

QSAR study and molecular design of open-chain enaminones as anticonvulsant agents

Present work employs the QSAR formalism to predict the ED50 anticonvulsant activity of ringed-enaminones, in order to apply these relationships for the prediction of unknown open-chain compounds containing the same types of functional groups in their molecular structure. Two different modeling approaches are applied with the purpose of comparing the consistency of our results: (a) the search of molecular descriptors via multivariable linear regressions; and (b) the calculation of flexible descriptors with the CORAL (CORrelation And Logic) program. Among the results found, we propose some potent candidate open-chain enaminones having ED50 values lower than 10 mg·kg-1 for corresponding pharmacological studies. These compounds are classified as Class 1 and Class 2 according to the Anticonvulsant Selection Project.Instituto de Investigaciones Fisicoquímicas Teóricas y AplicadasFacultad de Ciencias Exacta

Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods

QSPR studije karbonilnih, hidroksilnih, polienskih indeksa i prosječne molekulske težine polimera pod fotostabilizacijom pristupom ANN i MLR

One of the main disadvantages of the use of synthetic or semi-synthetic polymeric materials is their degradation and aging. The purpose of this study was to use artificial neural networks (ANN) and multiple linear regressions (MLR) to predict the carbonyl, hydroxyl, and polyene indices (ICO, IOH, and IOP), and viscosity average molecular weight (MV) of poly(vinyl chloride), polystyrene, and poly(methyl methacrylate). These physicochemical properties are considered fundamental during the study of photostabilization of polymers. From the five repeating units of monomers, the structure of the polymer studied is shown. Quantitative structure-property relationship (QSPR) models obtained by using relevant descriptors showed good predictability. Internal validation {R2, RMSE, and Q2LOO}, external validation {R2, RMSE, Q2pred, rm2, Δrm2, k, and k’}, and applicability domain were used to validate these models. The comparison of the results shows that the ANN models are more efficient than those of the MLR models. Accordingly, the QSPR model developed in this study provides excellent predictions, and can be used to predict ICO, IOH, IOP, and MV of polymers, particularly for those that have not been tested. This work is licensed under a Creative Commons Attribution 4.0 International License.Jedan od glavnih nedostataka upotrebe sintetičkih ili polusintetičkih polimernih materijala je njihova razgradnja i starenje. Svrha ove studije je primjena umjetnih neuronskih mreža (ANN) i višestrukih linearnih regresija (MLR) za predviđanje karbonilnih, hidroksilnih i polienskih indeksa (ICO, IOH i IOP) i prosječne molekulske mase viskoznosti (MV) poli(vinil-klorida), polistirena i poli(metil metakrilata). Ta fizikalno-kemijska svojstva smatraju se važnim tijekom proučavanja fotostabilizacije polimera. Iz pet ponavljajućih jedinica monomera prikazana je struktura ispitivanog polimera. Kvantitativni modeli odnosa strukture-svojstava (QSPR) dobiveni primjenom relevantnih deskriptora pokazali su dobru predvidljivost. Za potvrdu tih modela provedene su: interna provjera {R2, RMSE i Q2LOO}, vanjska provjera {R2, RMSE, Q2pred, rm2, Δrm2, k i k’} i domena primjenjivosti. Usporedba rezultata pokazuje da su modeli ANN učinkovitiji od modela MLR. Prema tome, model QSPR razvijen u ovoj studiji pruža izvrsna predviđanja i može se primjenjivati za predviđanje ICO, IOH, IOP i MV polimera, posebno za one koji nisu testirani. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds

The objective of the article was to perform a predictive analysis, based on quantitative structure-property relationships, of the dissociation constants (pKa) of different medicinal compounds (e.g., salicylic acid, salbutamol, lidocaine). Given the importance of this property in medicinal chemistry, it is of interest to develop theoretical methods for its prediction. The descriptors selection from a pool containing more than a thousand geometrical, topological, quantum-mechanical, and electronic types of descriptors was performed using the enhanced replacement method. Genetic algorithm and the replacement method (RM) techniques were used as reference points. A new methodology for the selection of the optimal number of descriptors to include in a model was presented and successfully used, showing that the best model should contain four descriptors. The best quantitative structure-property relationships linear model constructed using 62 molecular structures not previously used in this type of quantitative structure-property study showed good predictive attributes. The root mean squared error of the 26 molecules test set was 0.5600. The analysis of the quantitative structure-property relationships model suggests that the dissociation constants depend significantly on the number of acceptor atoms for H-bonds and on the number of carboxylic acids present in the molecules.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicada

QSAR analysis on tacrine-related acetylcholinesterase inhibitors

Background: The evaluation of the clinical effects of Tacrine has shown efficacy in delaying the deterioration of the symptoms of Alzheimer's disease, while confirming the adverse events consisting mainly in the elevated liver transaminase levels. The study of tacrine analogs presents a continuous interest, and for this reason we establish Quantitative Structure-Activity Relationships on their Acetylcholinesterase inhibitory activity. Results: Ten groups of new developed Tacrine-related inhibitors are explored, which have been experimentally measured in different biochemical conditions and AChE sources. The number of included descriptors in the structure-activity relationship is characterized by 'Rule of Thumb'. The 1502 applied molecular descriptors could provide the best linear models for the selected Alzheimer's data base and the best QSAR model is reported for the considered data sets. Conclusion: The QSAR models developed in this work have a satisfactory predictive ability, and are obtained by selecting the most representative molecular descriptors of the chemical structure, represented through more than a thousand of constitutional, topological, geometrical, quantum-mechanical and electronic descriptor types.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicada

Asymmetric bagging and feature selection for activities prediction of drug molecules

<p>Abstract</p> <p>Background</p> <p>Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation.</p> <p>Results</p> <p>Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability.</p> <p>Conclusion</p> <p>Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.</p