Data · dataset · 2026
Generative AI-Assisted Molecular Design of AChEIs
Listed in ZivaHub
Alzheimer’s disease (AD) remains a major neurodegenerative disorder with limited therapeutic options, while currently approved acetylcholinesterase inhibitors (AChEIs), such as donepezil, are associated with adverse effects including cardiotoxicity.
Description
Here, we integrated a deep learning-based framework to design novel AChEIs candidates with improved predicted cardiac safety. A reinforcement learning-guided GraphVAE (RL-GraphVAE) was employed for target-biased molecular generation.
The generated compounds were prioritized through CardiotoxPred-based cardiotoxicity screening, molecular docking, triplicate molecular dynamics simulations, and chemical synthesis. Experimental evaluation identified D0209 as a lead molecule exhibiting a noncompetitive inhibition mechanism. Notably, compared with donepezil, D0209 showed ∼43-fold lower hERG channel inhibition, indicating an improved cardiac safety profile.
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Overall, this integrated computational and experimental workflow demonstrates the utility of generative modeling for the discovery of novel leads with improved predicted safety profiles, providing promising starting points for further optimization and biological evaluation.
Links
Where it is published
- DOI doi.org/10.1021/acs.jcim.6c02233.s001 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Cancer · Chemistry · Computer Science & AI · Deep learning · Earth & Environmental Science · Genetics · Life Sciences · Medicine & Health · Reinforcement learning
Provenance · 1 source records, 16 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/34025076 | 6 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
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| concepts[anatomy].local:anatomy:heart | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (65%) |
| concepts[disease].local:disease:cancer | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Cancer'] |
| concepts[disease].local:disease:disease | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].anzsrc:field:461103 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['deep learning'] |
| concepts[field].anzsrc:field:461105 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['reinforcement learning'] |
| concepts[field].anzsrc:group:3105 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Genetics'] |
| concepts[field].local:field:chemistry | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/rights |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |