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QSAR (Quantitative Structure-Activity Relationship)

Definition
Definition
Definition

Quantitative Structure-Activity Relationship (QSAR) is a computational technique that models the relationship between the chemical structure of compounds and their biological activity. Relationships between structure and binding or pharmacokinetic (PK) properties may also be quantified. It involves statistical and mathematical methods to derive correlations and build predictive models. The models rely on large amounts of good data.

Importance in Computational Drug Discovery

  1. Predictive Modeling: QSAR models enable the prediction of binding, biological activity and bioavailability for new compounds, speeding up the design and test cycle.
  2. Lead Optimization: QSAR can identify structural features that influence binding, activity, or bioavailability. It can help in optimizing lead compounds to enhance those properties.
  3. Virtual Screening: QSAR models can be used to screen large libraries of compounds virtually, identifying potential drug candidates efficiently.
  4. Cost and Time Efficiency: Reduces the cost and time associated with experimental drug discovery by focusing resources on the most promising compounds.

Key Tools

1. QSAR Toolbox: Developed by the OECD for building and applying QSAR models.

2. KNIME: An open-source platform integrating various QSAR modeling tools and workflows.

3. DRAGON: Software for calculating molecular descriptors used in QSAR modeling.

4. Molecular Operating Environment (MOE): A comprehensive suite for molecular modeling, including QSAR analysis.

Literature

"Challenges with Multi-Objective QSAR in Drug Discovery"    

Publication Date: 2018-07-12    

DOI:10.1080/17460441.2018.1496079    

Summary: Reviews the challenges and advancements in multi-objective QSAR, emphasizing its application in multi-target drug design and model prioritization.

"Integrating QSAR Modelling and Deep Learning in Drug Discovery: The Emergence of Deep QSAR"    

Publication Date: 2023-12-08    

DOI:10.1038/s41573-023-00832-0    

Summary: Discusses key advances in integrating deep learning approaches with QSAR modeling, including deep generative and reinforcement learning in molecular design.

"Application of Ensemble Machine Learning Methods for QSAR Classification of Leukotriene A4 Hydrolase Inhibitors in Drug Discovery"    

Publication Date: 2024-09-20    

DOI:10.60084/mp.v2i2.217    

Summary: Demonstrates how ensemble machine learning methods enhance QSAR model accuracy, aiding in the identification of promising LTA4H inhibitors.

"Multi-Dimensional QSAR in Drug Discovery"    

Publication Date: 2007-12-01  

DOI:10.1016/J.DRUDIS.2007.08.004    

Summary: Discusses recent QSAR concepts exploring higher dimensions, such as induced fit, alternative binding modes, and solvation scenarios.

"QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery"    

Publication Date: 2018-11-13    

DOI:10.3389/fphar.2018.01275    

Summary: Summarizes recent trends and applications of QSAR-based virtual screening in identifying compounds with desired properties.

"Cloud 3D-QSAR: A Web Tool for the Development of Quantitative Structure-Activity Relationship Models in Drug Discovery"    

Publication Date: 2020-11-03    

DOI:10.1093/bib/bbaa276    

Summary: Introduces Cloud 3D-QSAR, a web tool integrating molecular structure generation, alignment, and interaction field computing for QSAR model development.

"Large Scale Comparison of QSAR and Conformal Prediction Methods and Their Applications in Drug Discovery"    

Publication Date: 2019-01-10    

DOI:10.1186/s13321-018-0325-4    

Summary: Compares conformal prediction with traditional QSAR methods for large-scale predictions of target-ligand binding, highlighting similarities and differences.

"Virtual Screening, Molecular Docking and QSAR Studies in Drug Discovery and Development Programme"    

Publication Date: 2020-07-15    

DOI:10.22270/jddt.v10i4.4218    

Summary: Reviews computational tools in structure-based and ligand-based drug design, including virtual screening, molecular docking, and QSAR methods.

"Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery"    

Publication Date: 2023-07-20    

DOI:10.60084/mp.v1i2.60    

Summary: Explores the use of QSAR studies with genetic algorithm and LightGBM to identify acetylcholinesterase inhibitors for Alzheimer's disease.

"Chemical-Informatics Approach to COVID-19 Drug Discovery: Monte Carlo Based QSAR, Virtual Screening and Molecular Docking Study of Some In-House Molecules as Papain-Like Protease (PLpro) Inhibitors"    

Publication Date: 2020-06-22    

DOI:10.1080/07391102.2020.1780946    

Summary: Integrates ligand-based drug design strategies to identify potential inhibitors for SARS-CoV-2 PLpro using QSAR, virtual screening, and molecular docking.