Literature DB >> 12462157

Application of topological descriptors in QSAR and drug design: history and new trends.

R Gozalbes1, J P Doucet, F Derouin.   

Abstract

Powerful methodologies for drug design and drug database screening and selection are presently available. Studies relating the structure of molecules to a property or a biological activity by means of statistical tools (QSPR and QSAR studies, respectively) are particularly relevant. An important point for this methodology is the use of good structural descriptors that are representative of the molecular features responsible for the relevant activity. Topological indices (TIs) are two-dimensional descriptors which take into account the internal atomic arrangement of compounds, and which encode in numerical form information about molecular size, shape, branching, presence of heteroatoms and multiple bonds. The usefulness of TIs in QSPR and QSAR studies has been extensively demonstrated, and they have also been used as a measure of structural similarity or diversity by their application to databases virtually generated by computer. In this article we will briefly review the history of TIs, their advantages and limitations with respect to other descriptors, and their possibilities in drug design and database selection. These applications rely on new computational techniques such as virtual combinatorial synthesis, virtual computational screening or inverse QSAR.

Mesh:

Substances:

Year:  2002        PMID: 12462157     DOI: 10.2174/1568005024605909

Source DB:  PubMed          Journal:  Curr Drug Targets Infect Disord        ISSN: 1568-0053


  19 in total

1.  Can topological indices transmit information on properties but not on structures?

Authors:  Alexandru T Balaban
Journal:  J Comput Aided Mol Des       Date:  2005-11-23       Impact factor: 3.686

2.  True prediction of lowest observed adverse effect levels.

Authors:  R García-Domenech; J V de Julián-Ortiz; E Besalú
Journal:  Mol Divers       Date:  2006-05-24       Impact factor: 2.943

Review 3.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling.

Authors:  S Ekins; J Mestres; B Testa
Journal:  Br J Pharmacol       Date:  2007-06-04       Impact factor: 8.739

Review 4.  Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.

Authors:  Linlin Zhao; Heather L Ciallella; Lauren M Aleksunes; Hao Zhu
Journal:  Drug Discov Today       Date:  2020-07-11       Impact factor: 7.851

Review 5.  Big Data and Artificial Intelligence Modeling for Drug Discovery.

Authors:  Hao Zhu
Journal:  Annu Rev Pharmacol Toxicol       Date:  2019-09-13       Impact factor: 13.820

6.  Databases and QSAR for cancer research.

Authors:  Adeel Malik; Hemajit Singh; Munazah Andrabi; Syed Akhtar Husain; Shandar Ahmad
Journal:  Cancer Inform       Date:  2007-02-15

Review 7.  Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.

Authors:  Neetu Tripathi; Manoj Kumar Goshisht; Sanat Kumar Sahu; Charu Arora
Journal:  Mol Divers       Date:  2021-06-10       Impact factor: 2.943

8.  Models for antitubercular activity of 5â-O-[(N-Acyl)sulfamoyl]adenosines.

Authors:  Rakesh K Goyal; Harish Dureja; Gajendra Singh; Anil Kumar Madan
Journal:  Sci Pharm       Date:  2010-08-13

9.  Chemical properties of air pollutants and cause-specific hospital admissions among the elderly in Atlanta, Georgia.

Authors:  Helen H Suh; Antonella Zanobetti; Joel Schwartz; Brent A Coull
Journal:  Environ Health Perspect       Date:  2011-06-27       Impact factor: 9.031

Review 10.  The role of multiscale computational approaches for rational design of conventional and nanoparticle oral drug delivery systems.

Authors:  Nahor Haddish-Berhane; Jenna L Rickus; Kamyar Haghighi
Journal:  Int J Nanomedicine       Date:  2007
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.