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Dose-Response Relationship: Potency and Efficacy01:22

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The potency of a drug is the measure of its ability to produce a biological response and can be compared by looking at the half-maximum effective concentration or EC50 values of different drugs. A lower EC50 value indicates higher potency of the drug. In the dose–response curve of two antihypertensive drugs, candesartan and irbesartan, a significant difference is observed in their EC50 values. A lower EC50 value for candesartan indicates that it is more potent than irbesartan, as it...
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Drug Potency Prediction of SARS-CoV-2 Main Protease Inhibitors Based on a Graph Generative Model.

Sarah Fadlallah1, Carme Julià1, Santiago García-Vallvé2

  • 1Research Group ASCLEPIUS: Smart Technology for Smart Healthcare, Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain.

International Journal of Molecular Sciences
|May 27, 2023
PubMed
Summary

A new computational method accurately predicts the potency of drug candidates inhibiting SARS-CoV-2 main protease (M-pro). This fast and cost-effective tool aids virtual screening by identifying promising compounds for further research.

Keywords:
SARS-CoV-2druggraph autoencodersgraph convolutional networksgraph regressionmolecular descriptorsmolecular potencyneural networkspredictionvirtual screening

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Accurate prediction of ligand potency against SARS-CoV-2 main protease (M-pro) is crucial for efficient virtual screening.
  • Identifying potent inhibitors can accelerate the development of antiviral therapies.

Purpose of the Study:

  • To develop and validate a computational method for predicting drug potency against SARS-CoV-2 M-pro.
  • To create a fast and reliable tool for prioritizing drug candidates in virtual screening.

Main Methods:

  • A three-step computational approach was employed: 3D structure definition, graph autoencoder for latent vector generation, and a fitting model for potency prediction.
  • The method was tested on a database of 160 drug-M-pro pairs with known pIC50 values.

Main Results:

  • The computational method demonstrated high accuracy in predicting drug potency (pIC50).
  • The entire database was processed in mere seconds on a standard personal computer, highlighting computational efficiency.

Conclusions:

  • A reliable, fast, and cost-effective computational tool for predicting drug potency against SARS-CoV-2 M-pro has been successfully developed.
  • This tool can significantly aid in prioritizing hits from virtual screening for further experimental validation.