Chemoinformatics and Drug Discovery

Xu, Jun; Hagler, A. T.

doi:10.3390/70800566

Cited by 207 publications

(158 citation statements)

References 125 publications

(118 reference statements)

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“…Conceptually, the approach used by CA, to address this problem, can be described well by the saying ''birds of a feather flock together.'' [47][48][49][50][51] Many CA algorithms have been invented, and they belong to two categories: hierarchical clustering and partitional (nonhierarchical) clustering. Hierarchical clustering rearranges objects in a binary tree-structure (joining clustering), and these methods are implemented in either an agglomerative (bottom-up) or divisive (top-down) procedure.…”

Section: Clusteringmentioning

confidence: 99%

Applying pattern recognition methods plus quantum and physico‐chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids

et al. 2007

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The great cost associated with the development of new anabolic-androgenic steroid (AASs) makes necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, quantum, and physicochemical molecular descriptors, plus linear discriminant analysis (LDA) were used to analyze the anabolic/androgenic activity of structurally diverse steroids and to discover novel AASs, as well as also to give a structural interpretation of their anabolic-androgenic ratio (AAR). The obtained models are able to correctly classify 91.67% (86.27%) of the AASs in the training (test) sets, respectively. The results of predictions on the 10% full-out cross-validation test also evidence the robustness of the obtained model. Moreover, these classification functions are applied to an "in house" library of chemicals, to find novel AASs. Two new AASs are synthesized and tested for in vivo activity. Although both AASs are less active than some commercially AASs, this result leaves a door open to a virtual variational study of the structure of the two compounds, to improve their biological activity. The LDA-assisted QSAR models presented here, could significantly reduce the number of synthesized and tested AASs, as well as could increase the chance of finding new chemical entities with higher AAR.

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Section: Clusteringmentioning

confidence: 99%

Applying pattern recognition methods plus quantum and physico‐chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids

et al. 2007

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“…By means of the procedures mentioned above, instead of assaying a large number of chemicals in a series of biological tests, one "virtually assays" these compounds by evaluating their activities with the models developed to this effect; this process is known today as computational (virtual or in silico) screening [142 -144]. Virtual screening techniques may be classified according to their particular modeling of molecular recognition and to the type of algorithm used in database searching [69,142,143]. If the target (or at least its active site) 3-D structure is known, one of the structure-based virtual screening methods can be applied.…”

Section: Cross-validation Methods As the Key For Qsar Model Internal mentioning

confidence: 99%

Discovery of Novel Trichomonacidals Using LDA‐Driven QSAR Models and Bond‐Based Bilinear Indices as Molecular Descriptors

et al. 2009

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Few years ago, the World Health Organization estimated the number of adults with trichomoniasis at 170 million worldwide, more than the combined numbers for gonorrhea, syphilis, and chlamydia. To combat this sexually transmitted disease, Metronidazole (MTZ) has emerged, since 1959, as a powerful drug for the systematic treatment of infected patients. However, increasing resistance to MTZ, adverse effects associated to high-dose MTZ therapies and very expensive conventional technologies related to the development of new trichomonacidals necessitate novel computational methods that shorten the drug discovery pipeline. Therefore, bond-based bilinear indices, new 2-D bond-based TOMOCOMD-CARDD Molecular Descriptors (MDs), and Linear Discriminant Analysis (LDA) are combined to discover novel antitrichomonal agents. Generated models, using non-stochastic and stochastic indices, are able to classify correctly the 90.11% (93.75%) and the 87.92% (87.50%) of chemicals in the training (test) sets, respectively. In addition, they show large Matthews correlation coefficients (C) of 0.80 (0.86) and 0.76 (0.71) for the training (test) sets, respectively. The result of predictions on the 10% full-out cross-validation test also evidences the quality of both models. In order to test the models predictive power, 12 compounds, already proved against Trichomonas vaginalis (Tv), are screened in a simulated virtual screening experiment. As a result, they correctly classified 9 out of 12 (75.00%) and 10 out of 12 (83.33%) of the chemicals, respectively, which were the most important criteria to validate the models. Finally, in order to prove the reach of TOMOCOMD-CARDD approach and to discover new trichomonacidals, these classification functions were applied to a set of QSAR Comb. Sci. 28, 2009, No. 1, 9 -26 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 9 * To whom correspondence should be addressed (contact for chem-and bioinformatics methods) ** Contact for biological assays *** Contact for chemical methods Full Paperseight chemicals which, in turn, were synthesized and tested toward in vitro activity against Tv. As a result, experimental observations confirm theoretical predictions to a great extent, since it is gained a correct classification of 87.50% (7/8) of chemicals. Biological tests also show several candidates as antitrichomonals, since almost all the compounds [VAM2-(3 -8)] exhibit pronounced cytocidal activities of 100% at the concentration of 100 mg/mL and at 24 h (48 h) but VAM2 -2: 99.37% (100%), and it is remarkable that these compounds do not show toxic activity in macrophage assays at this concentration. The Quantitative Structure -Activity Relationship (QSAR) models presented here could significantly reduce the number of synthesized and tested compounds as well as could act as virtual shortcuts to new chemical entities with trichomonacidal activity.

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“…Modelled after traditional experimental procedures, which typically follow a sequential optimization paradigm, most de novo design research has been ignoring the multiobjective nature of the problem and focused on the optimization of one molecular property at a time [10]. Typically the property serving as the primary objective has been similar to a known ligand or an interaction score with a target receptor.…”

Section: Drug Discovery and De Novo Drug Designmentioning

confidence: 99%

A Parallel implementation of a Multi-objective Evolutionary Algorithm

Kannas

Nicolaou

Pattichis

2009

2009 9th International Conference on Information Technology and Applications in Biomedicine

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The use of Evolutionary Algorithms (EAs) in difficult problems, where the search space is unknown, urges researches to find ways to exploit their parallel aspect. Multi-objective Evolutionary Algorithms (MOEAs) have features that can be exploited to harness the processing power offered by modern multi-core CPUs. Modern programming languages offer the ability to use threads and processes in order to achieve parallelism that is inherent in multi-core CPUs. This thesis presents a parallel implementation of a MOEA algorithm and its application to the de novo drug design problem. Drug discovery and De novo Drug design is a complex task that has to satisfy a number of conflicting objectives, where a MOEA finds a suitable problem to be used on. Further more such a task needs high amount of execution time. The aim is to minimize this time by the use of a parallel MOEA. The results indicate that using multiple processes that execute independent tasks of a MOEA can reduce significantly the execution time required and maintain comparable solution quality thereby achieving improved performance.iii APPROVAL PAGE

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Chemoinformatics and Drug Discovery

Cited by 207 publications

References 125 publications

Applying pattern recognition methods plus quantum and physico‐chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids

Applying pattern recognition methods plus quantum and physico‐chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids

Discovery of Novel Trichomonacidals Using LDA‐Driven QSAR Models and Bond‐Based Bilinear Indices as Molecular Descriptors

A Parallel implementation of a Multi-objective Evolutionary Algorithm

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