Artificial Intelligence-Based Quantitative Structure–Property Relationship Model for Predicting Human Intestinal Absorption of Compounds with Serotonergic Activity

Oral medicines represent the largest pharmaceutical market area. To achieve a therapeutic effect, a drug must penetrate the intestinal walls, the main absorption site for orally delivered active pharmaceutical ingredients (APIs). Indeed, predicting drug absorption can facilitate candidate screening...

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Bibliographic Details
Main Author:	Natalia Czub (15313668) (author)
Other Authors:	Jakub Szlęk (15313671) (author), Adam Pacławski (15313674) (author), Klaudia Klimończyk (15313677) (author), Matteo Puccetti (655374) (author), Aleksander Mendyk (2837087) (author)
Published:	2023
Subjects:	Biophysics Biochemistry Genetics Pharmacology Biotechnology Computational Biology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified single system widens promising tool useful optimized system enables main absorption site facilitate candidate screening experimental data scarcity predicting drug absorption oral drug screening drug permeability looking drug must penetrate proposed system based https :// github considerable therapeutic relevance seemingly two models human intestinal absorption good prediction accuracy based prediction drug discovery therapeutic effect high accuracy github platform regression models obtained models intestinal walls intestinal bioavailability turned toward serotonergic activity reduce time process complexity molecules classified ht ). however focus highly permeable high degree false positives early stage artificial intelligence ai represents
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Artificial Intelligence-Based Quantitative Structure–Property Relationship Model for Predicting Human Intestinal Absorption of Compounds with Serotonergic Activity

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