Version 1 of this summary, as approved on . It weighed 27 gains, 3 problems and 3 Traces. This is the current version. See the topic as it stands today
Most of the evidence here comes from benchmarks and lab tests rather than trials in patients, and the strongest results are in predicting protein shapes. TRV-2026-0203TRV-2026-0211 Authors and reviewers in other areas say their predictions still need confirmation in living systems or on wider data. TRV-2026-0288TRV-2026-2034
In the CASP14 blind test, AlphaFold matched experimental accuracy in a majority of cases, even when no similar structure was known. TRV-2026-0203 RoseTTAFold's authors report that it approaches AlphaFold2's accuracy and also models protein-protein complexes. TRV-2026-0211 The AlphaFold database's authors report it grew from 300,000 predicted structures in 2021 to over 214 million. TRV-2026-1205 On held-out questions about unseen proteins, Genolator scored an MCC of 0.959 on confirm-or-deny function questions, against 0.179 for GPT 4.1, in a benchmark test. TRV-2026-1133
Retrained on 7.8 million RNA sequence-structure pairs, SincFold scored worse on average than the published version, with training F1 of 0.983 against validation F1 of 0.420. TRV-2026-1980 A herbarium pipeline labeled 11.1 million of 22 million specimen records as having flowers, but only 2.9 million had the complete metadata researchers need. TRV-2026-1263 In a rice gene benchmark, the deep learning models overfit early and a tuned Random Forest did better. TRV-2026-1675 A review of AI bone scaffold design found small datasets and weak links between computational predictions and biological outcomes hold the field back. TRV-2026-0288
A study of lab cultures from three donors found AI reading Raman signals told exhausted T cells from active ones, but it was shown only in culture. TRV-2026-1084 A mock-task study with one expert and three trainees suggests AI hand tracking can show cell culture technique improving with feedback. TRV-2026-1519 The authors of one study rated 35 of 55 ChatGPT-4o hypotheses on fungus and cervical cancer as high quality; none were tested in a lab. TRV-2026-1343 SPROTify flagged 334 small-protein candidates in cancer data, and their function is unconfirmed. TRV-2026-2034
News reports describe 16 of 285 AI-designed virus genomes producing viable viruses that killed E. coli, including strains resistant to a natural virus. TRV-2026-0732 The same work draws a warning from commentators, who say governance to steer such a capability safely does not yet exist, though no misuse is reported. TRV-2026-0671TRV-2026-1040
This is the best-measured thread, with blind-test and benchmark results showing near-experimental accuracy for structure and strong scores on function questions. TRV-2026-0203TRV-2026-0211TRV-2026-1133
Benchmarks show sequence models doing well on curated data, while an RNA structure model retrained on millions of examples did not improve. TRV-2026-1927TRV-2026-1122TRV-2026-1980
Classifiers separate cell types and states with high accuracy in lab data, and the authors of several say clinical use is not yet shown. TRV-2026-2263TRV-2026-1084TRV-2026-2034
Lab tests show some AI-designed viruses and enzymes work, though only a minority of designs did; commentators say safety governance is missing.
Language models and image pipelines match experts on narrow extraction tasks, but gaps in metadata, uneven sensitivity and the need for human guidance remain. TRV-2026-1862TRV-2026-1263TRV-2026-2199TRV-2026-1820
Each record sits in one thread at most. A record with both a gain and a problem counts on both sides. 4 records fit none of these threads.
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