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Scored all 71 million possible single-letter protein changes in humans as likely harmful or benign.
AlphaMissense is a Google DeepMind model derived from AlphaFold and fine-tuned on population variant frequencies to score whether a missense change is likely pathogenic or benign. The Science 2023 paper released classifications for all 71 million possible single-amino-acid substitutions in the human proteome, a complete catalogue rather than a tool that must be run per variant. Its scores are widely used to triage variants of uncertain significance in cancer genes, helping laboratories decide which findings deserve follow-up. The key caveat is that pathogenicity is not the same as actionability: a variant predicted to damage a protein does not tell a clinician whether a drug targets it or whether it changes management. For a newcomer: AlphaMissense has pre-scored every possible single-letter protein change so laboratories can see which ones look harmful.
AlphaFold-derived model fine-tuned on population variant frequencies.
Query for this technology: (TITLE:"AlphaMissense" OR ABSTRACT:"AlphaMissense") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about AlphaMissense, not a curated reading list.
Shares Google DeepMind (and Google Research) and the tags foundation-model, genome.
Shares Google DeepMind (and Google Research) and the tags foundation-model, genome.
Shares the tags foundation-model, genome.
Shares the tags foundation-model, genome.
Shares Google DeepMind (and Google Research) and the tag foundation-model.
Shares Google DeepMind (and Google Research) and the tag foundation-model.
Shares Google DeepMind (and Google Research) and the tag foundation-model.
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DeepMind's model and released predictions of pathogenicity for every possible human missense variant, used to triage variants of uncertain significance.