OralAbsPredict: A data-driven framework to predict human intestinal absorption (HIA) and human oral bioavailability (HOB) from chemical structures.

Bibliographic Details
Title: OralAbsPredict: A data-driven framework to predict human intestinal absorption (HIA) and human oral bioavailability (HOB) from chemical structures.
Authors: Pore S; Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, Kolkata, 700032, India., Roy K; Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, Kolkata, 700032, India. Electronic address: kunalroy_in@yahoo.com.
Source: European journal of medicinal chemistry [Eur J Med Chem] 2026 Oct 15; Vol. 316, pp. 119043. Date of Electronic Publication: 2026 Jun 08.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Editions Scientifiques Elsevier Country of Publication: France NLM ID: 0420510 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1768-3254 (Electronic) Linking ISSN: 02235234 NLM ISO Abbreviation: Eur J Med Chem Subsets: MEDLINE
Imprint Name(s): Publication: Paris : Editions Scientifiques Elsevier
Original Publication: Paris, S.E.C.T. [etc.]
MeSH Terms: Intestinal Absorption* , Software*, Pharmaceutical Preparations/chemistry ; Pharmaceutical Preparations/administration & dosage ; Pharmaceutical Preparations/metabolism ; Humans ; Administration, Oral ; Biological Availability ; Molecular Structure ; Machine Learning
Abstract: Oral absorption is a key pharmacokinetic process for orally administered drugs, determining therapeutic efficacy and influencing distribution, metabolism, and excretion. One of the major reasons a drug fails in early development is poor oral absorption. Therefore, it is important to determine the oral absorption properties of drugs during early development, even before synthesis. Human intestinal absorption (HIA) and human oral bioavailability (HOB) are two important pharmacokinetic properties of orally administered drugs that provide a good measure of drug absorption through the oral route. The experimental determination of the HIA and HOB required substantial time, resources, and money. Therefore, an alternative approach is needed to quickly screen the HIA and HOB of orally administered compounds. In this study, we introduce a Python-based software tool, "OralAbsPredict," that predicts HIA and HOB from chemical structures provided as SMILES strings. This tool returns three predicted values, HIA and HOB at two different experimental cutoffs, i.e., 50% and 20%, based on the developed machine learning models. The tool is based on the developed machine learning models showing good performance not only on the training set (accuracytr > 0.9) but also on the test set (accuracyte > 0.7). These models are developed using Functional-Class Fingerprints (FCFP) and Mordred descriptors: a count-based FCFP2 fingerprint for the HIA model, a Mordred descriptor for HOB_50% model, and a count-based FCFP4 fingerprint for the HOB_20% model. One of the major drawbacks of existing models is that they do not adequately address class imbalance in the modeled data. However, we have addressed the class imbalance problem in this work using hyperparameters like class_weight. Here, we have identified and interpreted the important features using the SHAP method. Additionally, we have performed a substructure analysis to identify key substructures present in most of the active molecules. From this analysis, we have found that most of the orally active compounds contain the aromatic ring system with chlorine, fluorine, and amine groups. A comparative analysis with the benchmark expert systems was also conducted, demonstrating that our models achieved similar test performance.
(Copyright © 2026 Elsevier Masson SAS. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Human intestinal absorption; Human oral bioavailability; Oral absorption; OralAbsPredict
Substance Nomenclature: 0 (Pharmaceutical Preparations)
Entry Date(s): Date Created: 20260609 Date Completed: 20260712 Latest Revision: 20260712
Update Code: 20260713
DOI: 10.1016/j.ejmech.2026.119043
PMID: 42263584
Database: MEDLINE
Description
ISSN:1768-3254
DOI:10.1016/j.ejmech.2026.119043