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Comparative Analysis of Machine Learning Algorithms for Predicting PIC50 Values of COVID-19 Compounds

In: Information Systems and Technological Advances for Sustainable Development

Author

Listed:
  • Imane Aitouhanni

    (Mohammed V University)

  • Amine Berqia

    (Mohammed V University)

Abstract

This study addresses the urgent need for efficient drug discovery methodologies in combating COVID-19, focusing on predicting PIC50 values as a key indicator of compound potency against the virus. Utilizing machine learning algorithms, particularly Biblio LazyPredict, the research delves into predictive modeling to expedite the identification of promising drug candidates. By emphasizing the significance of PIC50 prediction and the pivotal role of machine learning in drug discovery efforts, the study contributes to accelerating drug discovery timelines and fostering innovation in the face of global health challenges, bridging the gap between computational analysis and experimental validation.

Suggested Citation

  • Imane Aitouhanni & Amine Berqia, 2024. "Comparative Analysis of Machine Learning Algorithms for Predicting PIC50 Values of COVID-19 Compounds," Lecture Notes in Information Systems and Organization, in: Mohamed Ben Ahmed & Anouar Abdelhakim Boudhir & Hany Farhat Abd Elhamid Attia & Adriana Eštoková & M (ed.), Information Systems and Technological Advances for Sustainable Development, pages 66-78, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-75329-9_8
    DOI: 10.1007/978-3-031-75329-9_8
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