aceArtificial Intelligence in Organic Synthesis
Artificial intelligence (AI) is rapidly reshaping organic synthesis; nevertheless, currently most laboratory practice still remains driven by human intuition, trial-and-error optimization, and manual interpretation of analytical data. Here, we synthesize recent advances that move AI from isolated demonstrations to a practical toolkit spanning the full experimental cycle: molecular design and prioritization, computer-assisted synthesis planning and route selection, catalyst and condition optimization, and AI-enab…
AI tools now span molecular design, synthesis planning, catalyst optimization, and product verification to compress candidate spaces and accelerate decision-making in organic synthesis workflows.
Current AI synthesis tools face limits from poor data quality, laboratory variability, underreported negative results, and black-box failure modes that require calibrated reliance and plausibility checks.
Adoption is constrained by data quality issues, lab-to-lab variability, missing negative results, and opaque failure modes requiring human validation and mechanistic checks.
Evidence
- Peer-reviewedThe Chemical Record2026-09-28
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Truvace Impact Record TRV-2026-1216, v1: “Artificial Intelligence in Organic Synthesis.” Truvace, 2026-09-30. /record/TRV-2026-1216 (accessed at citation time). sha256 2ae51987c1c68cdc…
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