Reviewnpj drug discovery2025
Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy.
Review in npj drug discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Bridging BET bromodomain and immune checkpoint inhibitors through generative bioorganic frameworks for next-generation cancer immunotherapy.RSC medicinal chemistry · 2026Review
- Three privileged scaffolds, one pipeline: chalcones, pyrazolines and pyrazoles in drug discovery and fluorescent sensing.RSC medicinal chemistry · 2026Review
- Identification of GPI-Anchored Wall Transfer Protein 1 Modulators for Fungal Infections Through Generative AI and Physics-Based Approaches.International journal of molecular sciences · 2026Article
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Checkpoint inhibition and beyond: Precision immune engineering for the immune-privileged landscape of ocular malignancies.BioImpacts : BI · 2026Review
- Next-Generation Immune Checkpoints and Tumor Microenvironment Modulation in Cancer Immunotherapy.Journal of immunology research · 2026Review
- Recent Advances and Emerging Directions in Machine Learning-Based Breast Cancer Drug Discovery: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- Structural and Functional Perspectives of Optineurin in Autophagy, Immune Signaling, and Cancer.Cells · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This perspective examines how artificial intelligence (AI) is transforming small-molecule development for precision cancer immunomodulation therapy. It outlines AI-driven approaches for de novo design, virtual screening, multi-parameter optimization, and ADMET prediction, targeting immune checkpoints, tumor microenvironment modulation, antigen presentation, and metabolic pathways. The article highlights patient stratification, multi-omics integration, digital twin simulations, translational challenges, and future directions, underscoring AI's potential to deliver effective, personalized immunomodulatory therapeutics.
Identifiers
What OpenQuestion holds
Registered trials
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.