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Retinal Development and Disorders / Photoreceptor and Optogenetics Research / CRISPR and Genetic Engineering · Journal article
Current Opinion in Ophthalmology · September 3, 2026
Raises a question worth testing. It does not answer one.
This is a narrative review of emerging AI-guided approaches to ocular drug discovery, spanning small molecule generation, gene therapy vector engineering, and precision gene editing. The source reports promising preclinical and computational advances but emphasizes that empirical validation remains essential and no clinical efficacy data are presented.
Journal article.
Reinforcement learning and diffusion-based generative models are producing structurally novel small molecule candidates for ophthalmic indications with demonstrated preclinical efficacy AI-guided AAV capsid engineering has yielded vectors with improved retinal transduction efficiency in nonhuman primates Deep learning models trained on chromatin accessibility data enable de novo design of compact, cell-type-specific regulatory elements for gene therapy vectors
Nonhuman primate data for AAV vectors only; translation to human safety and efficacy not yet demonstrated. The authors emphasize that rigorous experimental validation remains essential; AI-nominated candidates and predictions must be tested empirically to confirm safety, biological relevance, and therapeutic efficacy
This review describes emerging computational platforms and early-stage preclinical work that may eventually influence ophthalmic drug discovery pathways. Clinicians should recognize these as exploratory tools; no change to clinical practice is supported by the evidence presented.
This is a narrative review surveying AI-guided drug discovery approaches in ophthalmology with mostly preclinical findings; it raises questions about translational potential rather than reporting clinical evidence or definitive efficacy.
This review describes emerging computational platforms and early-stage preclinical work that may eventually influence ophthalmic drug discovery pathways. Clinicians should recognize these as exploratory tools; no change to clinical practice is supported by the evidence presented.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
PURPOSE OF REVIEW: This review surveys recent advances in artificial intelligence-guided small molecule discovery and gene therapy, with a focus on generative and foundation models, and their translational applications to ophthalmic therapeutics. RECENT FINDINGS: A unifying theme across recent work is the shift from artificial intelligence as a screening tool to artificial intelligence as an engine for generative design. In small molecule discovery, reinforcement learning and diffusion-based generative models are producing structurally novel candidates for ophthalmic indications with demonstrated preclinical efficacy, while structure-based approaches leveraging AlphaFold and co-folding models are improving target identification and virtual screening. In gene therapy, artificial intelligence-guided AAV capsid engineering has yielded vectors with improved retinal transduction efficiency in nonhuman primates. Complementing this, deep learning models trained on chromatin accessibility data are enabling de novo design of compact, cell-type-specific regulatory elements, with direct implications for the specificity and payload capacity of ocular gene therapy vectors. Artificial intelligence-guided CRISPR guide RNA optimization is further expanding the precision of gene editing-based approaches. SUMMARY: Artificial intelligence tools may influence the landscape of ophthalmic drug discovery, from nominating small molecule candidates to co-designing next-generation gene therapy vectors. Rigorous experimental validation remains essential; artificial intelligence-nominated candidates and predictions must be tested empirically to confirm their safety, biological relevance, and therapeutic efficacy.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.