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Structure-Activity Relationships and Drug Design

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...

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Related Experiment Video

Updated: Jan 19, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Quantitative Structure-Activity Relationship Study for HIV-1 LEDGF/p75 Inhibitors.

Yang Li¹, Yujia Tian², Yao Xi²

¹Institute of Science and Technology, Shandong University of Traditional Chinese Medicine, Ji'nan, Shandong, 250355, China.

Current Computer-Aided Drug Design

|September 21, 2019

Summary

Computational Quantitative Structure-Activity Relationship (QSAR) models were developed to predict the bioactivity of HIV-1 integrase LEDGF/p75 inhibitors. These models demonstrate good predictive power, aiding in the design of new anti-AIDS drugs by identifying key molecular descriptors.

Keywords:

HIV-1 integrase LEDGF/p75 inhibitor chemical scaffold molecular descriptor quantitative structure-activity relationship (QSAR) model

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Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors

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Last Updated: Jan 19, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Assessment of Immunologically Relevant Dynamic Tertiary Structural Features of the HIV-1 V3 Loop Crown R2 Sequence by ab initio Folding

Assessment of Immunologically Relevant Dynamic Tertiary Structural Features of the HIV-1 V3 Loop Crown R2 Sequence by ab initio Folding

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Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors

Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors

Published on: April 9, 2014

Area of Science:

Medicinal Chemistry
Computational Chemistry
Drug Discovery

Background:

HIV-1 Integrase (IN) is a critical target for novel anti-AIDS drug development.
Inhibitors targeting the HIV-1 integrase and LEDGF/p75 interaction show promise in reducing viral replication.

Purpose of the Study:

To develop and validate computational Quantitative Structure-Activity Relationship (QSAR) models for predicting the bioactivity of HIV-1 integrase LEDGF/p75 inhibitors.
To identify key molecular descriptors influencing the activity of these inhibitors.

Main Methods:

Collected 190 HIV-1 integrase LEDGF/p75 inhibitors and their bioactivities.
Utilized T-distributed Stochastic Neighbor Embedding (TSNE) for scaffold classification.
Employed Kohonen's self-organizing map (SOM) for dataset splitting.
Developed Multiple Linear Regression (MLR), Support Vector Machine (SVM), and consensus models using 20 CORINA Symphony descriptors.

Main Results:

All developed models exhibited good prediction of pIC50 values.
Correlation coefficients on the test set exceeded 0.7 for all models.
Consensus Model C1 achieved the highest performance with r=0.909 on the training set and r=0.804 on the test set.

Conclusions:

The study successfully developed predictive QSAR models for HIV-1 integrase LEDGF/p75 inhibitors.
Key molecular descriptors, including hydrogen bond acceptors, atom charges, and electronegativities, are crucial for predicting inhibitor activity.
These findings can guide the rational design of more potent anti-HIV-1 agents.