Chem-Bioinformatics and QSAR:  A Review of QSAR Lacking Positive Hydrophobic Terms

Hansch, Corwin; Kurup, Alka; Garg, Rajni; Gao, Hua

doi:10.1021/cr0000067

Cited by 234 publications

(174 citation statements)

References 144 publications

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“…The studied acridines have been taken with their reactivity from literature (Hansch et al, 2001). Chemical structures and experimental biological activities are gathered in Table 1.…”

Section: Methodsmentioning

confidence: 99%

“…The presence of the outliers could be accounted for as a result of the possibility that the molecules may act by different mechanisms or interact with the receptor in different binding modes and due to the intrinsic noise associated with both the original data and methodological aspects involved in the construction of a QSAR model (Hansch et al, 2001).…”

Section: Refractivity (Ref) Homo-lumo Energy Gap (∆ε) (Ev)mentioning

confidence: 99%

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Theoretically Predicted Descriptors Based Quantitative Structure Activity Relationship Study of the Activity of Acridines Against B-16 Melanoma

Saeed¹

2011

American Journal of Applied Sciences

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Problem statement: The probability of success and reducing time and coast in drug discovery process could be increased on the basis of QSAR techniques. The study involves the QSAR investigation of 20 bioactive acridines that have activity against Approach: Molecular descriptors, total energy, van der Waals volume, molecular volume, HOMO energy, HOMO-LUMO energy gap, polarizability, refractivity, bond angle of C8-N9-C2 and bond length of C14-N6 were calculated. Initial geometry optimizations were carried out with RM1 Hamiltonian. Lowest energy conformers were subjected to single point calculations by DFT method. Several models for the prediction of biological activity have been drawn up by using the multiple regression technique. Results: Four models with R2 ranges from 0.88-0.93 were predicted. A model with hepta-parametric equation with R2 0.93 was used to predict the biological activities, the agreement between the observed and the predicted values was up to 93%. Conclusion: The biological activity of the studied acridines can be modeled with quantum chemical molecular descriptors.

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“…The studied acridines have been taken with their reactivity from literature (Hansch et al, 2001). Chemical structures and experimental biological activities are gathered in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Section: Refractivity (Ref) Homo-lumo Energy Gap (∆ε) (Ev)mentioning

confidence: 99%

Theoretically Predicted Descriptors Based Quantitative Structure Activity Relationship Study of the Activity of Acridines Against B-16 Melanoma

Saeed¹

2011

American Journal of Applied Sciences

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“…[10][11][12] Quantitative structure-activity and structure-property relationships (QSAR/QSPR) are powerful technologies that correlate descriptors based on molecular structures and use computational algorithms to relate the key descriptors to relevant ADME properties. 10,[13][14][15] Drugs to treat human central nervous system (CNS) diseases and disorders, such as epilepsy, Alzheimer's disease, Parkinson disease, schizophrenia, depression and brain tumors are required to cross the blood-brain barrier (BBB) by passive diffusion or through the help of transporters. In contrast, drugs that do not target the CNS should present limited capacity to cross the BBB in order to avoid drug-induced side effects in the brain.…”

Section: Introductionmentioning

confidence: 99%

A fragment-based approach for the in silico prediction of blood-brain barrier permeation

Moda¹,

Carrara²,

Andricopulo³

2012

J. Braz. Chem. Soc.

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A permeabilidade da barreira hematoencefálica (BBB, do inglês blood-brain barrier) é uma propriedade fundamental no planejamento de fármacos que atuam no sistema nervoso central (CNS) no tratamento de doenças como a epilepsia, depressão, mal de Alzheimer, mal de Parkinson, esquizofrenia, entre outras. No presente trabalho, estudos das relações quantitativas entre a estrutura e propriedade (QSPR) foram conduzidos para o desenvolvimento e validação de modelos in silico para a predição da permeabilidade da BBB. O conjunto de dados utilizado possui significativa diversidade química e ampla distribuição dos valores da propriedade alvo. Os modelos de QSPR gerados apresentaram bons parâmetros estatísticos e foram empregados com sucesso na predição de um conjunto teste de 48 compostos. Os modelos desenvolvidos são úteis na identificação, seleção e planejamento de candidatos a novos fármacos com propriedades farmacocinéticas otimizadas.Blood-brain barrier (BBB) permeation is an essential property for drugs that act in the central nervous system (CNS) for the treatment of human diseases, such as epilepsy, depression, Alzheimer's disease, Parkinson disease, schizophrenia, among others. In the present work, quantitative structure-property relationship (QSPR) studies were conducted for the development and validation of in silico models for the prediction of BBB permeation. The data set used has substantial chemical diversity and a relatively wide distribution of property values. The generated QSPR models showed good statistical parameters and were successfully employed for the prediction of a test set containing 48 compounds. The predictive models presented herein are useful in the identification, selection and design of new drug candidates having improved pharmacokinetic properties.Keywords: ADME, central nervous system, pharmacokinetics, QSPR, blood-brain barrier IntroductionThe challenges facing the pharmaceutical industry are tremendous at every step of the drug discovery and development process. Technology-based discovery certainly is one of the most important elements to increase research and development (R&D) productivity. The new paradigm of drug discovery involves a combination of classical and modern technologies with innovative strategies addressed to the design of new chemical entities (NCEs) with improved properties. 1-3 NCEs expected to advance into clinical trials should have a good balance of pharmacodynamic and pharmacokinetic properties. For the past decades, problems with absorption, distribution, metabolism and excretion (ADME) have been one of the major reasons for the failure of attractive compounds in advanced stages of drug development. [4][5][6] Traditionally, in vivo and in vitro models are employed in the pharmaceutical industry for the evaluation of pharmacokinetic parameters. However, animal models and cell-based assays are typically time consuming and expensive, and thus not applicable to the early screening of large libraries of compounds. [7][8][9] In recent years, the appearance and consolida...

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“…When SARs can be quantitatively measured within these data sets, they become quantitative structure-activity relationships (QSAR). QSAR is a valuable drug design tool that applies statistical analysis of relationships between chemical structure and biological activity in a quantitative and mechanism-oriented manner [16][17][18][19]. QSAR technologies has been employed, and continue to be developed and employed, both to correlate information in data sets and to facilitate, for example, the discovery of enzyme inhibitors, agonist or antagonists of important drug targets [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Fragment-based QSAR: perspectives in drug design

Salum

Andricopulo

2009

Mol Divers

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Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure-activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.

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Chem-Bioinformatics and QSAR: A Review of QSAR Lacking Positive Hydrophobic Terms

Cited by 234 publications

References 144 publications

Theoretically Predicted Descriptors Based Quantitative Structure Activity Relationship Study of the Activity of Acridines Against B-16 Melanoma

Theoretically Predicted Descriptors Based Quantitative Structure Activity Relationship Study of the Activity of Acridines Against B-16 Melanoma

A fragment-based approach for the in silico prediction of blood-brain barrier permeation

Fragment-based QSAR: perspectives in drug design

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