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

Imane Aitouhanni () and Amine Berqia
Additional contact information
Imane Aitouhanni: Mohammed V University
Amine Berqia: Mohammed V University

A chapter in Information Systems and Technological Advances for Sustainable Development, 2024, pp 66-78 from Springer

Abstract: 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.

Keywords: Drug Discovery; Log S Prediction; Deep Learning FNNs (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-75329-9_8

Ordering information: This item can be ordered from
http://www.springer.com/9783031753299

DOI: 10.1007/978-3-031-75329-9_8

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().