{"entity":{"id":"idea-bio1-ai-binders-disordered-regions","kind":"idea","name":"AI-designed proteins that grip the floppy parts of cancer drivers","aka":[],"tldr":"MYC, fusion oncoproteins and transcription factors have shapeless, flexible regions that drugs cannot grip. Deep-learning protein design tools such as RFdiffusion may be able to invent binders that clamp them, for use as degradation handles, intrabodies or targeting domains for CAR and bispecific therapies rather than as drugs themselves.","summary":"Deep-learning protein design (RFdiffusion, AlphaFold-based hallucination) has produced high-affinity binders to structured targets and, increasingly, to peptides and disordered segments. Intrinsically disordered regions of MYC, fusion oncoproteins and transcription factors are the classic undruggable surfaces. Designed binders could serve as degradation handles, intrabodies, or CAR and bispecific targeting domains rather than as drugs themselves.","asOf":"2026-09-08","links":[{"label":"Bottleneck evidence (The undruggable drivers): Dang et al., Drugging the 'undruggable' cancer targets (Nature Reviews Cancer 2017)","url":"https://doi.org/10.1038/nrc.2017.36"}],"tags":[],"related":[],"cancers":[],"sections":[],"technologies":["ai-drug-design"],"targets":[],"drugs":[],"companies":["isomorphic-labs","generate-biomedicines"],"institutions":[],"pathways":[],"terms":[],"trials":[],"people":[],"bottlenecks":["b-undruggable-targets","b-ai-validation"],"keyPapers":["paper-dang-nat-rev-cancer"],"journals":[],"dependsOn":[],"notes":[],"hypothesis":"Designed miniproteins achieve nanomolar binding to at least one disordered oncoprotein region and, when fused to a degradation domain, deplete the target in cells.","rationale":"Design methods have crossed the threshold for structured epitopes and now handle conformational ensembles; the modality is intracellular expression or conjugation, not oral dosing, which relaxes the chemistry constraints.","test":"Design and test 100 binders per target region against MYC and one fusion oncoprotein, with biophysical validation and a cell-based degradation reporter; publish successes and failures for model improvement.","maturity":"speculative","actor":"research","cost":"medium","horizonYears":7},"route":"/ideas/idea-bio1-ai-binders-disordered-regions/","neighbours":{"technology":[{"id":"ai-drug-design","kind":"technology","name":"AI-driven drug & target discovery","route":"/technologies/ai-drug-design/"}],"company":[{"id":"generate-biomedicines","kind":"company","name":"Generate:Biomedicines","route":"/companies/generate-biomedicines/"},{"id":"isomorphic-labs","kind":"company","name":"Isomorphic Labs","route":"/companies/isomorphic-labs/"}],"bottleneck":[{"id":"b-ai-validation","kind":"bottleneck","name":"AI that is built but not validated or deployed","route":"/bottlenecks/b-ai-validation/"},{"id":"b-undruggable-targets","kind":"bottleneck","name":"The undruggable drivers","route":"/bottlenecks/b-undruggable-targets/"}],"paper":[{"id":"paper-dang-nat-rev-cancer","kind":"paper","name":"Drugging the 'undruggable' cancer targets","route":"/key-papers/paper-dang-nat-rev-cancer/"}]}}