Application of 3D-QSAR Techniques in Anti-HIV-1 Drug Design - An Overview

Debnath, Asim K.

doi:10.2174/1381612054864902

Cited by 33 publications

(18 citation statements)

References 81 publications

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“…Specific mechanism-driven (Q)SAR tools are presently used successfully for future drug development (Bacilieri and Moro 2006;Dearden 2003;Debnath 2005;Gozalbes et al 2002;Guner 2002;Loew et al 1993;Ooms 2000;Ortiz et al 2006;Yang and Huang 2006;Zhong et al 2007). Avoiding unwanted (eco)toxicological properties such as CMR, PBT, and vPvB and at the same time targeting the intended properties should be a new application of the conceptual approach used in the pharmaceutical development.…”

Section: Introduction: Why (Q)sar?mentioning

confidence: 99%

The Use of (Q)SAR Methods in the Context of REACH

Lahl¹,

Gundert‐Remy²

2008

Toxicology Mechanisms and Methods

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The new European REACH (Registration, Evaluation, Authorisation of Chemicals) legislation requires new information on chemicals, which is tiered according to production volume. The need for data promotes the use of (quantitative) structure-activity relationships ([Q]SARs) in order to meet the European political goal to protect the lives of animals. Within the preparation for REACH, the EU Commission set up REACH implementation projects (RIPs). They are aimed at giving guidance to industry and regulators on how to interpret the legislation. Within the framework of REACH, (Q)SARs are expected to have specific applications in several fields such as raising concerns for chemicals with low production volume for which no (eco)toxicological data have to be submitted, supporting the grouping of chemicals (family approach), and supporting the classification of chemicals as PBT (persistent/bioaccumulative/toxic) or vPvB (very persistent, very bioaccumulative) substances. Currently, the use of (Q)SARs in European regulatory decision making is limited. However, a new European tool (Toxtree) will be made available along with training courses to promote the use of (Q)SAR approaches under REACH. It is expected that the experimental data, to be submitted by industry and made publicly available under REACH, will provide opportunities to improve existing prediction tools and to develop new approaches for (Q)SAR analysis. In the political perspective, the tools will provide support as a rational basis for the intelligent design of new chemicals with sustainable (eco)toxicological properties.

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Section: Introduction: Why (Q)sar?mentioning

confidence: 99%

The Use of (Q)SAR Methods in the Context of REACH

Lahl¹,

Gundert‐Remy²

2008

Toxicology Mechanisms and Methods

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“…3D-QSAR methods have been employed to design anti-HIV-1 drugs [68], matrix metalloproteinase inhibitors [69], therapeutic agents for Alzheimer's and Parkinson's diseases [70] and anti-tuberculosis agents [71], to name a few. Furthermore, 3D-QSAR has been applied along with molecular modeling and molecular dynamics in the design of pteridinederived therapeutic agents [72], indolomorphinan derivatives [73] and in vaccinology studies [74].…”

Section: Ai and Quantitative Structure-activity Relationships Qsar Apmentioning

confidence: 99%

Artificial Intelligence Approaches for Rational Drug Design and Discovery

Duch¹,

Swaminathan²,

Meller

2007

CPD

176

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Pattern recognition, machine learning and artificial intelligence approaches play an increasingly important role in rational drug design, screening and identification of candidate molecules and studies on quantitative structure-activity relationships (QSAR). In this review, we present an overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI). An emphasis is put on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand. We also discuss representative applications of AI methods to docking, screening and QSAR studies. The growing trend to integrate computational and experimental efforts in that regard and some future developments are discussed. In addition, we comment on a broader role of machine learning and artificial intelligence approaches in biomedical research.

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“…These enzymes are current targets for the antivirals clinically in use, whereas molecular recognition receptors on host CD4 cells (glycoprotein gp120, gp41 linked glycoprotein) are being explored for their potential (Debnath, 2005;Teixeira et al, 2011;Castro et al, 2011;Blanco et al, 2011;Pendri et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

HIV-1 Reverse Transcriptase: a potential target for marine products

Miceli¹,

Souza²,

Rodrigues³

et al. 2012

Rev. bras. farmacogn.

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HIV-1 reverse transcriptase (HIV-1 RT) is a therapeutic target for the treatment of HIV-positive individuals or those already showing AIDS symptoms. In this perspective, the identifi cation of new inhibitors for this enzyme is of great importance in view of the growing viral resistance to the existing treatments. This resistance has compromised the quality of life of those infected with multidrug-resistant strains, whose treatment options are already limited, putting at risk these individuals lives. The literature has recognized marine organisms and their products as natural sources for the identifi cation of new therapeutic options for different pathologies. In this brief review, we consider the structure of HIV-1 RT and its most common inhibitors, as well as some marine diterpenes originally reported as HIV-1 RT inhibitors to encourage the identifi cation and development of new marine antiviral prototypes.

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Application of 3D-QSAR Techniques in Anti-HIV-1 Drug Design - An Overview

Cited by 33 publications

References 81 publications

The Use of (Q)SAR Methods in the Context of REACH

The Use of (Q)SAR Methods in the Context of REACH

Artificial Intelligence Approaches for Rational Drug Design and Discovery

HIV-1 Reverse Transcriptase: a potential target for marine products

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