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Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning
Sharmistha Dutta, M. Prabha, R.R. Siva Kiran, Hadagali Ashoka. SAR and QSAR in environmental research, 2025-02-01
Topic: Brain proteins, microglia and lab models
Butyrylcholinesterase inhibition offers one of the formulated solutions to tackle the aggravating symptoms of dementia that downgrades to cholinergic neuronal loss in Alzheimer’s disease. We developed a QSAR model to facilitate the identification of effective butyrylcholinesterase inhibitors. The model employs multi-feature selection and feature learning, improving the in silico screening efficiency and accelerating drug discovery efforts. This study aims to integrate Human Intestinal Absorption (HIA) values of butyrylcholinesterase (BChE) target inhibitors and their 50% inhibitory concentration (IC50) with machine learning tools. The model was developed using chemical descriptors in combination with supervised machine learning classification algorithms. Random Forest Classifier algorithm proved to be the ultimate best fit for classification model metrics including log loss probability (0.04225), accuracy score (98.88%) and Matthew’s correlation coefficient (0.98). Furthermore, a subset of the active dataset was used to study the regression based on HIA values using multi-feature selection and feature learning. The models were validated using precision, recall and F1 score for regression modelling. After integrating HIA data with existing machine learning algorithms, we observed a significant reduction of 89.63% in the number of inhibitors. The findings provide valuable pharmacological insights that can help in future design of drug development schemes different from conventional methods.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Dutta, S., Prabha, M., Kiran, R. S., & Ashoka, H. (2025). Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning. SAR and QSAR in environmental research. https://doi.org/10.1080/1062936x.2025.2466020
Vancouver
Dutta S, Prabha M, Kiran RS, Ashoka H. Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning. SAR and QSAR in environmental research. 2025. doi:10.1080/1062936x.2025.2466020. PMID: 39980415.
BibTeX
@article{dutta2025-39980415,
author = {Dutta, Sharmistha and Prabha, M. and Kiran, R.R. Siva and Ashoka, Hadagali},
title = {{Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning}},
journal = {SAR and QSAR in environmental research},
year = {2025},
doi = {10.1080/1062936x.2025.2466020},
pmid = {39980415},
url = {https://doi.org/10.1080/1062936x.2025.2466020}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- After integrating HIA data with existing machine learning algorithms, we observed a significant reduction of 89.63% in the number of inhibitors.
Why it matters
This in silico QSAR study developed a machine learning model to screen for butyrylcholinesterase inhibitors, finding a significant reduction of 89.63% in the number of inhibitors after integrating human intestinal absorption data. It is a computational drug-discovery screening step, not a tested treatment or cure.
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Study details
- Effect size98.88%; 89.63%
- Evidence strengthLow
How this study scored 34/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomized / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 33 of the 96 points we could judge it on, which is 34 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (SAR and QSAR in environmental research) |
| Journal's citation record | 0 / 4 | SAR and QSAR in environmental research: journal h-index 63 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 5 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 2.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 5 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author Hadagali Ashoka holds only 8 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 0 / 3 | Ramaiah Institute of Technology: institution h-index 122 (OpenAlex) |
| Multicenter collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39980415) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 33 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 33 of the 96 points we could judge it on is 34 out of 100. |
| Total | 34 / 100 | — |
What each of these 20 components measures, and what this score is not →
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