Evidence map›Paper›PMID 41967764›Full record

ReviewDrug discovery today2026

Artificial intelligence revolutionizing CNS drug discovery and development.

Md E K Talukder, Mohammad Rashedul Islam, Saghir Ali, Jia Zhou, Andrew A Bolinger

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Drug discovery today, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

5 authors.

Md E K TalukderSchool of Basic Pharmaceutical and Toxicological Sciences, College of Pharmacy, The University of Louisiana Monroe, Monroe, LA 71201, USA.
Mohammad Rashedul IslamSchool of Basic Pharmaceutical and Toxicological Sciences, College of Pharmacy, The University of Louisiana Monroe, Monroe, LA 71201, USA.
Saghir AliChemical Biology Program, Department of Pharmacology and Toxicology, University of Texas Medical Branch (UTMB), Galveston, TX 77550, USA.
Jia ZhouChemical Biology Program, Department of Pharmacology and Toxicology, University of Texas Medical Branch (UTMB), Galveston, TX 77550, USA.
Andrew A BolingerSchool of Basic Pharmaceutical and Toxicological Sciences, College of Pharmacy, The University of Louisiana Monroe, Monroe, LA 71201, USA. Electronic address: bolinger@ulm.edu.

Funding

Small molecule modulators of ΔFosB FunctionR01DA040621 · NIDA · UNIVERSITY OF TEXAS MED BR GALVESTON · PI NESTLER, ERIC J., ROBISON, ALFRED J · 2016 to 2025
$7.1M
5-HT2 Receptor Allosterism in Cocaine Use DisorderR01DA038446 · NIDA · UNIVERSITY OF TEXAS MED BR GALVESTON · PI Kathryn A. Cunningham, Jia Zhou · 2015 to 2026
$5.3M
Discovery of GPR52 ligand probes for cocaine use disorderR01DA060228 · NIDA · UNIVERSITY OF TEXAS MED BR GALVESTON · PI John A Allen, Jia Zhou · 2025 to 2026
$1.6M
NIDA NIH HHS R01 DA038446NIDA NIH HHS R01 DA040621NIDA NIH HHS R01 DA060228
6 · The paper itself

Abstract

Central nervous system (CNS) drug discovery faces high attrition rates, long timelines and substantial costs due to complex disease biology and the difficulties in safe drug delivery. Conventional CNS processes remain slow and trial-and-error driven. These challenges often result in poor brain penetration, off-target toxicity or limited efficacy after years of development. Recently, the integration of artificial intelligence (AI) with computer-aided drug design (CADD) has enabled more precise and scalable approaches for therapeutic development. AI-powered tools prioritize high-value analogs, streamlining design and optimization. This review provides an overview of how AI technologies are redefining early-stage CNS drug discovery, particularly for complex and underserved neurological diseases.

Indexed as

Artificial IntelligenceCentral Nervous System AgentsDrug DevelopmentDrug DiscoveryAnimalsCentral Nervous System DiseasesComputer-Aided DesignDrug DesignHumansCentral Nervous System Agentsartificial intelligencecentral nervous systemcomputer-aided drug designdeep learningmachine learningmedicinal chemistryneurodegenerative diseases

Identifiers

PMID41967764
PMCPMC13322018

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

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.