Drug repurposing for obsessive-compulsive disorder using deep learning-based binding affinity prediction models.

Thomas Papikinos, Marios Krokidis, Aris Vrahatis, Panagiotis Vlamos, Themis P Exarchos

Abstract read

In one paragraph

Article in AIMS neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

Sequence-based prediction of drug-target binding using machine learning, deep learning and ensemble models without 3D structural information.Scientific reports · 2026
Article

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

5 authors.

Thomas PapikinosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.

Marios KrokidisBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.

Aris VrahatisBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.

Panagiotis VlamosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.

Themis P ExarchosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Obsessive-compulsive disorder (OCD) is a chronic psychiatric disease in which patients suffer from obsessions compelling them to engage in specific rituals as a temporary measure to alleviate stress. In this study, deep learning-based methods were used to build three models which predict the likelihood of a molecule interacting with three biological targets relevant to OCD, SERT, D2, and NMDA. Then, an ensemble model based on those models was created which underwent external validation on a large drug database using random sampling. Finally, case studies of molecules exhibiting high scores underwent bibliographic validation showcasing that good performance in the ensemble model can indicate connection with OCD pathophysiology, suggesting that it can be used to screen molecule databases for drug-repurposing purposes.

Indexed as

binding affinity predictiondeep learningdrug repositioningdrug repurposingdrug-target interaction predictionobsessive-compulsive disorderOCD

Identifiers

PMID38988885

DOI10.3934/Neuroscience.2024013

PMCPMC11230860

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.