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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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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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Factors Affecting Activity Coefficient01:17

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The extended Debye-Hückel equation indicates that the activity coefficient of an ion in an aqueous solution at 25°C depends on three partially interdependent properties: the ionic strength of the solution, the charge of the ion, and the ion size. 
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Exploring QSAR models for activity-cliff prediction.

Markus Dablander1, Thierry Hanser2, Renaud Lambiotte1

  • 1Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter (550), Woodstock Road, Oxford, OX2 6GG, UK.

Journal of Cheminformatics
|April 18, 2023
PubMed
Summary
This summary is machine-generated.

Quantitative structure-activity relationship (QSAR) models often fail to predict activity cliffs (ACs), which are pairs of similar compounds with large differences in binding affinity. Improving AC-sensitivity in QSAR models is crucial for enhancing prediction accuracy.

Keywords:
Activity cliff predictionActivity cliffsBinding affinity predictionDeep learningExtended-connectivity fingerprintsGraph isomorphism networksMachine learningMolecular representationPhysicochemical descriptorsQSAR modelling

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Drug discovery

Background:

  • Activity cliffs (ACs) represent pairs of structurally similar compounds with significant differences in biological activity.
  • QSAR models are hypothesized to struggle with predicting ACs, contributing to prediction errors.
  • The AC-prediction power of modern QSAR methods and its relation to general QSAR performance remains underexplored.

Purpose of the Study:

  • To systematically evaluate the AC-prediction performance of modern QSAR models.
  • To investigate the relationship between AC-prediction and general QSAR prediction performance.
  • To compare different molecular representation methods and regression techniques for AC classification.

Main Methods:

  • Constructed nine QSAR models by combining three molecular representations (extended-connectivity fingerprints, physicochemical-descriptor vectors, graph isomorphism networks) with three regression techniques (random forests, k-nearest neighbours, multilayer perceptrons).
  • Classified compound pairs as ACs or non-ACs using each model.
  • Predicted individual molecule activities in three case studies: dopamine receptor D2, factor Xa, and SARS-CoV-2 main protease.

Main Results:

  • QSAR models frequently fail to predict ACs, exhibiting low AC-sensitivity when both compound activities are unknown.
  • AC-sensitivity significantly increased when the activity of one compound in a pair was known.
  • Graph isomorphism networks showed competitive or superior performance for AC classification compared to classical representations.
  • Extended-connectivity fingerprints consistently yielded the best performance for general QSAR prediction.

Conclusions:

  • QSAR models demonstrate a notable weakness in predicting activity cliffs.
  • Graph isomorphism networks offer a viable approach for AC classification and compound optimization.
  • Improving AC-sensitivity is a key area for future QSAR model development.
  • Extended-connectivity fingerprints remain a robust choice for general QSAR prediction tasks.