Crowdsourcing functional cell therapy binders from generative artificial intelligence
Abstract
Over thirty years ago, structural biologists established the Critical Assessment of Structure Prediction (CASP) to rigorously assess methods for predicting protein structures from amino acid sequences. Participants submitted predictions before the corresponding experimental structures became publicly available (Moult et al, 1995). This framework established a common standard against which methodological advances could be measured. Its value became particularly evident at CASP14 in 2020, when AlphaFold2 produced predictions approaching experimental accuracy for many targets (Jumper et al, 2021; Pereira et al, 2021). CASP’s independent, prospective assessment enabled the community to recognize the scale of this advance by providing rapid, independent validation. The next generation of CASP-like open competitions is similarly poised to help discern the impact of emerging artificial intelligence (AI)-based methods across several domains of biology, from genomics to protein design to therapeutic discovery. The implementation and results of one such effort, the Bits to Binders competition, are described in this issue of Molecular Systems Biology, where Kosonocky, Abel and colleagues evaluated 12,000 designed proteins as recognition domains for chimeric antigen receptor (CAR) T cells (Kosonocky et al, 2026).
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BibTeX
@article{dozic2026crowdsourcing,
title = {{Crowdsourcing functional cell therapy binders from generative artificial intelligence}},
author = {Abdul Vehab Dozic and Caleb A Lareau},
journal = {Molecular Systems Biology},
year = {2026},
month = oct,
doi = {10.1038/s44320-026-00249-y},
url = {https://link.springer.com/10.1038/s44320-026-00249-y},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Crowdsourcing functional cell therapy binders from generative artificial intelligence
AU - Abdul Vehab Dozic
AU - Caleb A Lareau
PY - 2026
DA - 2026/10/08
JO - Molecular Systems Biology
DO - 10.1038/s44320-026-00249-y
UR - https://link.springer.com/10.1038/s44320-026-00249-y
AB - Over thirty years ago, structural biologists established the Critical Assessment of Structure Prediction (CASP) to rigorously assess methods for predicting protein structures from amino acid sequences. Participants submitted predictions before the corresponding experimental structures became publicly available (Moult et al, 1995). This framework established a common standard against which methodological advances could be measured. Its value became particularly evident at CASP14 in 2020, when AlphaFold2 produced predictions approaching experimental accuracy for many targets (Jumper et al, 2021; Pereira et al, 2021). CASP’s independent, prospective assessment enabled the community to recognize the scale of this advance by providing rapid, independent validation. The next generation of CASP-like open competitions is similarly poised to help discern the impact of emerging artificial intelligence (AI)-based methods across several domains of biology, from genomics to protein design to therapeutic discovery. The implementation and results of one such effort, the Bits to Binders competition, are described in this issue of Molecular Systems Biology, where Kosonocky, Abel and colleagues evaluated 12,000 designed proteins as recognition domains for chimeric antigen receptor (CAR) T cells (Kosonocky et al, 2026).
SN - 1744-4292
ER -