bioRxivBioinformaticsPreprint
Open access in silico tools to predict the ADMET profiling and PASS (Prediction of Activity Spectra for Substances of Bioactive compounds of Garlic (Allium sativum L.)
Abstract
BackgroundGarlic (Allium sativum L.) is a common spice with many health benefits, mainly due to its diverse bioactive compounds, (see below) such as organic sulphides, saponins, phenolic compounds, and polysaccharides. Several studies have demonstrated its functions such as anti-inflammatory, antibacterial, and antiviral, antioxidant, cardiovascular protective and anticancer property. In this work we have investigated the main bioactive components of garlic through a bioinformatics approach. Indeed, we are in an era of bioinformatics where we can predict data in the fields of medicine. Approaches with open access in silico tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs. MethodsThis paper encompasses the fundamental functions of open access in silico prediction tools, as PASS database (Prediction of Activity Spectra for Substances) that it estimates the probable biological activity profiles for compounds. This paper also aims to help support new researchers in the field of drug design and to investigate best bioactive compounds in garlic. Resultsscreening through each of pharmacokinetic criteria resulted in identification of Garlic compounds that adhere to all the ADMET properties. ConclusionsIt was established an open-access database (PASS database, available bioinformatics tool SwissADME, PreADMET pkCSM database) servers were employed to determine the ADMET (metabolism, distribution, excretion, absorption, and toxicity) attributes of garlic molecules and to enable identification of promising molecules that follow ADMET properties. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/452815v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@1344e43org.highwire.dtl.DTLVardef@fe1674org.highwire.dtl.DTLVardef@1742577org.highwire.dtl.DTLVardef@47a966_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: metagenomics
- assay: proteomics
- research focus: drug response / drug discovery
- research focus: benchmarking
- research focus: database / resource
- approach: statistical model
- approach: benchmark study
- Citations
- 11
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Accountable Prediction of Drug ADMET Properties with Molecular Descriptors
Nilavo Boral, Promita Ghosh, … Malay Bhattacharyya·bioRxiv·Jun 29, 2022
- Small in size, big on taste: metabolomics analysis of flavor compounds from Philippine garlic
Ralph John Emerson J. Molino, Klidel Fae Rellin, … Hiyas A. Junio·PLoS ONEPeer-reviewed·May 20, 2021
- ADMETron: An AI-driven SaaS platform for comprehensive ADMET prediction and compound prioritisation
Divya N. Nair, Rohit Singh Yadav, … Sahil Arora·bioRxiv·Jun 13, 2026
- A Meta-model for ADMET Property Prediction Analysis
Sarala Padi, Antonio Cardone, Ram D. Sriram·bioRxiv·Dec 5, 2023
- ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries
Kyle Swanson, Parker Walther, … James Zou·BioinformaticsPeer-reviewed·Jun 21, 2024
- Human ADME/PK is lost in translation and prediction from in silico to in vitro to in vivo
Urban Fagerholm, Sven Hellberg·bioRxiv·Feb 17, 2025