Evidence map›Paper›PMID 40784908›Full record

ArticleScientific reports2025

Computational machine learning estimation of digitoxin solubility in supercritical solvent at different temperatures utilizing ensemble methods.

Hadil Faris Alotaibi, Waqed H Hassan, Ahmed Kateb Jumaah Al-Nussairi, Narinderjit Singh Sawaran Singh, Ahmed Salah Al-Shati, M M Rekha, Subhashree Ray, Aashna Sinha, Gunjan Garg

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Hadil Faris AlotaibiDepartment of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint AbdulRahman University, 11671, Riyadh, Saudi Arabia. Hfalotaibi@pnu.edu.sa.
Waqed H HassanUniversity of Warith Al-Anbiyaa, Kerbala, 56001, Iraq.
Ahmed Kateb Jumaah Al-NussairiAl-Manara College for Medical Sciences, Amarah, Maysan, Iraq.
Narinderjit Singh Sawaran SinghFaculty of Data Science and Information Technology, INTI International University, Persiaran Perdana BBN, Putra Nilai, 71800, Nilai, Malaysia.
Ahmed Salah Al-ShatiDepartment of Chemical Engineering and Petroleum Refining, Kut University College, Kut, Wasit, 52001, Iraq.
M M RekhaDepartment of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India.
Subhashree RayDepartment of Biochemistry, IMS and SUM Hospital, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751003, India.
Aashna SinhaSchool of Applied and Life Sciences, Division of Research and Innovation, Uttaranchal University, Dehradun, Uttarakhand, India.
Gunjan GargCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.

Funding

Princess Nourah Bint Abdulrahman University PNURSP2025R205
6 · The paper itself

Abstract

The solubility of medications in supercritical solvent is the most important factor that can be determined via appropriate computational tools. This work explores the modeling of digitoxin solubility as the case study in supercritical CO

Indexed as

Drug particlesMachine learningPharmaceutical processSolubilitySupercritical CO2

Identifiers

PMID40784908
PMCPMC12336311

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.