Evidence map›Paper›PMID 42240762›Full record

ArticleMolecular diversity2026

Acetylcholinesterase inhibitory activity of phthalimide derivatives as anti-alzheimer agents: QSAR, ARKA, Hybrid ARKA-RASAR, virtual screening, molecular docking and ADMET studies.

Shubhanshu Shukla, Jitesh Pradhan, Nikita Chhabra, Pragya Gawande, Anjali Murmu, Yogendra Chandra, Jagadish Singh, Partha Pratim Roy

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Article in Molecular diversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

8 authors.

Shubhanshu ShuklaLaboratory of Drug Discovery and Ecotoxicology, Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, 495009, India.
Jitesh PradhanLaboratory of Drug Discovery and Ecotoxicology, Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, 495009, India.
Nikita ChhabraLaboratory of Drug Discovery and Ecotoxicology, Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, 495009, India.
Pragya GawandeLaboratory of Drug Discovery and Ecotoxicology, Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, 495009, India.
Anjali MurmuLaboratory of Drug Discovery and Ecotoxicology, Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, 495009, India.
Yogendra ChandraLaboratory of Drug Discovery and Ecotoxicology, Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, 495009, India.
Jagadish SinghLaboratory of Drug Discovery and Ecotoxicology, Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, 495009, India. jagadishpharm09@gmail.com.
Partha Pratim RoyLaboratory of Drug Discovery and Ecotoxicology, Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, 495009, India. parthapratim.roy@ggu.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is a chronic neurodegenerative disorder and a leading cause of dementia worldwide, characterized by progressive cognitive and memory decline. The impaired cholinergic neurotransmission in the brain is a major pathological feature of AD. Therefore, enhancing cholinergic function is a key therapeutic strategy for its management. In this study, a dataset of 111 phthalimide derivatives with experimental anti-AD activity was collected from various literature. Further, quantitative structure-activity relationship (QSAR) modelling was performed to determine key structural features governing acetylcholinesterase (AChE) inhibitory activity of phthalimide derivatives. Furthermore, Arithmetic Residuals in K-Groups Analysis (ARKA) was employed to develop ARKA and hybrid ARKA-RASAR models, that enabled the interpretation of QSAR descriptors in different response ranges and identification of similarity between closely related compounds as well as improving the predictive reliability of the QSAR model. All the developed models showed strong internal predictivity (R

Indexed as

AcetylcholinesteraseAlzheimer’s diseaseARKA-RASARMulti-class ARKAPhthalimideQSAR

Identifiers

PMID42240762

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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.