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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
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Wind Turbine Machine Models01:24

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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Related Experiment Video

Updated: Jan 28, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Machine Learning and Molecular Modeling for Drug Repurposing Targeting Potential PI3Kα Inhibitors in Post-CoViD-19

Carine Ribeiro Dos Santos1, Priscila Goes Camargo1,2, Carlos Rangel Rodrigues2

  • 1Universidade Federal do Rio de Janeiro (UFRJ), Centro de Ciências Matemáticas e da Natureza (CCMN), Instituto de Química (IQ), Departamento de Química Orgânica (DQO), Programa de Pós-Graduação em Química (PGQu), Laboratório de Modelagem Molecular Prof. Ricardo Bicca de Alencastro (LabMMol), Avenida Athos da Silveira Ramos, n° 149, Centro de Tecnologia, Bloco A, Cidade Universitária, Rio de Janeiro, Rio de Janeiro CEP 21941-909, Brazil.

ACS Omega
|January 26, 2026
PubMed
Summary

Researchers computationally screened FDA-approved drugs to find new inhibitors for the phosphoinositide 3-kinase-alpha (PI3Kα) pathway, crucial in post-COVID-19 pulmonary fibrosis. Simeprevir and ceritinib showed the most promise for therapeutic development.

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Area of Science:

  • Pharmacology
  • Computational Chemistry
  • Drug Discovery

Background:

  • Dysregulation of the phosphoinositide 3-kinase-alpha (PI3Kα) pathway is linked to post-COVID-19 pulmonary fibrosis.
  • There is a critical need for effective therapeutic agents targeting this pathway.

Purpose of the Study:

  • To identify novel PI3Kα inhibitors by computationally repurposing FDA-approved drugs.
  • To evaluate potential drug candidates using advanced computational methods.

Main Methods:

  • A hybrid approach combining machine learning (random forest model) and molecular modeling.
  • Virtual screening of FDA-approved drugs from the DrugBank database.
  • Molecular docking, 200 ns molecular dynamics simulations, and MM/PBSA binding free energy calculations.

Main Results:

  • A robust random forest model was developed and validated for PI3Kα inhibitor prediction.
  • Five promising FDA-approved drugs were identified as potential PI3Kα inhibitors.
  • Simeprevir and ceritinib exhibited the most favorable binding affinities and stable interactions with the PI3Kα kinase domain.

Conclusions:

  • Simeprevir and ceritinib are strong candidates for PI3Kα inhibition.
  • These drugs warrant further experimental investigation for treating post-COVID-19 fibrotic conditions.