Evidence map›Paper›PMID 41331397›Full record

ArticleJournal of computer-aided molecular design2025

Deep learning model for activity cliffs prediction: a comprehensive approach to protein kinase inhibitors.

Said Elaiwat, Safa Daoud, Nour Jamal Jaradat, Mutasem Taha

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Article in Journal of computer-aided molecular design, 2025. 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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0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Said ElaiwatFaculty of Architecture and Design, Al-Zaytoonah University of Jordan, Amman, Jordan. S.elaiwat@zuj.edu.jo.
Safa DaoudDepartment of Pharmaceutical Chemistry and Pharmacognosy, Faculty of Pharmacy, Applied Science Private University, Amman, Jordan.
Nour Jamal JaradatDepartment of Applied Pharmaceutical Sciences and Clinical Pharmacy, Faculty of Pharmacy, Isra University, Amman, Jordan.
Mutasem TahaFaculty of Pharmacy, University of Jordan, Amman, Jordan. Mutasem@ju.edu.jo.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Activity cliffs (ACs) present a significant challenge in structure-activity relationship (SAR) studies, characterized by pairs of similar compounds exhibiting substantial differences in biological activity. This paper investigates the interactions between protein kinases and their inhibitors to predict ACs by utilizing advanced deep learning techniques. Matched Molecular Pairs (MMPs) and MMP-ACs are systematically defined based on a minimum 100-fold difference in potency. A deep autoencoder model is developed for feature extraction phase followed by classification phase using various algorithms, including Support Vector Machines (SVM) and neural networks. Our results demonstrate that deep learning approaches can effectively capture complex patterns in molecular data, leading to robust predictions of ACs. Across all classifiers, our experiments show a strong correlation between the structural properties of inhibitors and their activity profiles against specific protein targets.

Indexed as

Deep LearningProtein Kinase InhibitorsProtein KinasesAlgorithmsAutoencoderClassification AlgorithmsHumansNeural Networks, ComputerPrediction AlgorithmsPredictive Learning ModelsStructure-Activity RelationshipSupport Vector MachineProtein Kinase InhibitorsProtein KinasesActivity cliffsAuto-encoderDeep learningNeural networkSVM

Identifiers

PMID41331397

What OpenQuestion holds

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Registered trials

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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.