Public articles linked to the same research event.
FEBS letters This review surveys the landscape of deep learning-driven protein design pipelines, discusses tailored applications in peptide, small molecule, binder, vaccine, and antibody design, argues that current confidence metrics for filtering and evaluating designs remain optimized for static protein interfaces and can fail on underrepresented or conformationally complex targets, proposes ensemble-based methods as a promising avenue for improving design success rates, and highlights emerging strategies such as fold-switching scaffolds and molecular glues realized through engineered cyclic peptides that expand the functional scope of designed proteins.
This review surveys the landscape of deep learning-driven protein design pipelines, discusses tailored applications in peptide, small molecule, binder, vaccine, and antibody design, argues that current confidence metrics for filtering and evaluating designs remain optimized for static protein interfaces and can fail on underrepresented or conformationally complex targets, proposes ensemble-based methods as a promising avenue for improving design success rates, and highlights emerging strategies such as fold-switching scaffolds and molecular glues realized through engineered cyclic peptides that expand the functional scope of designed proteins.
This review surveys the landscape of deep learning-driven protein design pipelines, discusses tailored applications in peptide, small molecule, binder, vaccine, and antibody design, argues that current confidence metrics for filtering and evaluating designs remain optimized for static protein interfaces and can fail on underrepresented or conformationally complex targets, proposes ensemble-based methods as a promising avenue for improving design success rates, and highlights emerging strategies such as fold-switching scaffolds and molecular glues realized through engineered cyclic peptides that expand the functional scope of designed proteins.
This review surveys the landscape of deep learning-driven protein design pipelines, discusses tailored applications in peptide, small molecule, binder, vaccine, and antibody design, argues that current confidence metrics for filtering and evaluating designs remain optimized for static protein interfaces and can fail on underrepresented or conformationally complex targets, proposes ensemble-based methods as a promising avenue for improving design success rates, and highlights emerging strategies such as fold-switching scaffolds and molecular glues realized through engineered cyclic peptides that expand the functional scope of designed proteins.