An, L. et al. Binding and sensing diverse small molecules using shape-complementary pseudocycles. Science 385, 276–282 (2024).
Dou, J. et al. De novo design of a fluorescence-activating β-barrel. Nature 561, 485–491 (2018).
Lu, L. et al. De novo design of drug-binding proteins with predictable binding energy and specificity. Science 384, 106–112 (2024).
Polizzi, N. F. & DeGrado, W. F. A defined structural unit enables de novo design of small-molecule-binding proteins. Science 369, 1227–1233 (2020).
Kortemme, T. De novo protein design—From new structures to programmable functions. Cell 187, 526–544 (2024).
Lee, G. R. et al. Small-molecule binding and sensing with a designed protein family. Nat. Commun. 17, 4533 (2026).
Chen, Y. et al. Emergence of specific binding and catalysis from a designed generalist binding protein. Nat. Chem. https://doi.org/10.1038/s41557-026-02125-6 (2026).
Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).
Marchand, A. et al. Targeting protein–ligand neosurfaces with a generalizable deep learning tool. Nature 639, 522–531 (2025).
Noske, J., Kynast, J. P., Lemm, D., Schmidt, S. & Höcker, B. PocketOptimizer 2.0: a modular framework for computer-aided ligand-binding design. Protein Sci. 32, e4516 (2023).
Glasgow, A. A. et al. Computational design of a modular protein sense-response system. Science 366, 1024–1028 (2019).
Leonard, A. C. et al. Rationalizing diverse binding mechanisms to the same protein fold: insights for ligand recognition and biosensor design. ACS Chem. Biol. 19, 1757–1772 (2024).
Zhu, J. et al. De novo design of transmembrane fluorescence-activating proteins. Nature 640, 249–257 (2025).
Zhang, Z., Shen, W. X., Liu, Q. & Zitnik, M. Efficient generation of protein pockets with PocketGen. Nat. Mach. Intell. 6, 1382–1395 (2024).
Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).
Torres, S. V. et al. De novo design of high-affinity binders of bioactive helical peptides. Nature 626, 435–442 (2024).
Vázquez Torres, S. et al. De novo designed proteins neutralize lethal snake venom toxins. Nature 639, 225–231 (2025).
Eguchi, R. R., Choe, C. A. & Huang, P.-S. Ig-VAE: generative modeling of protein structure by direct 3D coordinate generation. PLoS Comput. Biol. 18, e1010271 (2022).
Bhat, S. et al. De novo design of peptide binders to conformationally diverse targets with contrastive language modeling. Sci. Adv. 11, eadr8638 (2025).
Shuai, R. W., Ruffolo, J. A. & Gray, J. J. IgLM: infilling language modeling for antibody sequence design. Cell Syst. 14, 979–989 (2023).
Ingraham, J. B. et al. Illuminating protein space with a programmable generative model. Nature 623, 1070–1078 (2023).
Bennett, N. R. et al. Atomically accurate de novo design of antibodies with RFdiffusion. Nature 649, 183–193 (2026).
Pacesa, M. et al. One-shot design of functional protein binders with BindCraft. Nature 646, 483–492 (2025).
Zambaldi, V. et al. De novo design of high-affinity protein binders with AlphaProteo. Preprint at https://doi.org/10.48550/arXiv.2409.08022 (2024).
Bryant, P. & Elofsson, A. Peptide binder design with inverse folding and protein structure prediction. Commun. Chem. 6, 229 (2023).
Hayes, T. et al. Simulating 500 million years of evolution with a language model. Science 387, 850–858 (2025).
Tinberg, C. E. et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501, 212–216 (2013).
Polizzi, N. F. et al. De novo design of a hyperstable non-natural protein-ligand complex with sub-Å accuracy. Nat. Chem. 9, 1157–1164 (2017).
McCann, J. J. et al. Computational design of a sensitive, selective phase-changing sensor protein for the VX nerve agent. Sci. Adv. 8, eabh3421 (2022).
Kuhlman, B. et al. Design of a novel globular protein fold with atomic-level accuracy. Science 302, 1364–1368 (2003).
Goudy, O. J., Nallathambi, A., Kinjo, T., Randolph, N. Z. & Kuhlman, B. In silico evolution of autoinhibitory domains for a PD-L1 antagonist using deep learning models. Proc. Natl Acad. Sci. USA 120, e2307371120 (2023).
Wohlwend, J. et al. Boltz-1 democratizing biomolecular interaction modeling. Preprint at bioRxiv https://doi.org/10.1101/2024.11.19.624167 (2024).
Passaro, S. et al. Boltz-2: towards accurate and efficient binding affinity prediction. Preprint at bioRxiv https://doi.org/10.1101/2025.06.14.659707 (2025).
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Besag, J. On the statistical analysis of dirty pictures. J. R. Stat. Soc. B 48, 259–302 (1986).
Dauparas, J. et al. Atomic context-conditioned protein sequence design using LigandMPNN. Nat. Methods 22, 717–723 (2025).
Eastman, P., Pritchard, B. P., Chodera, J. D. & Markland, T. E. Nutmeg and SPICE: models and data for biomolecular machine learning. J. Chem. Theory Comput. 20, 8583–8593 (2024).
Eastman, P. et al. SPICE, a dataset of drug-like molecules and peptides for training machine learning potentials. Sci. Data 10, 11 (2023).
McConnell, D. B. Biotin’s lessons in drug design. J. Med. Chem. 64, 16319–16327 (2021).
Demonte, D., Drake, E. J., Lim, K. H., Gulick, A. M. & Park, S. Structure-based engineering of streptavidin monomer with a reduced biotin dissociation rate. Proteins 81, 1621–1633 (2013).
Minami, H. et al. Phase I and pharmacological study of a new camptothecin derivative, exatecan mesylate (DX-8951f), infused over 30 minutes every three weeks. Clin. Cancer Res. 7, 3056–3064 (2001).
Fu, Z., Li, S., Han, S., Shi, C. & Zhang, Y. Antibody drug conjugate: the “biological missile” for targeted cancer therapy. Signal Transduct. Target. Ther. 7, 93 (2022).
Li, W. et al. Synthesis and evaluation of camptothecin antibody-drug conjugates. ACS Med. Chem. Lett. 10, 1386–1392 (2019).
Grigoryan, G. & DeGrado, W. F. Probing designability via a generalized model of helical bundle geometry. J. Mol. Biol. 405, 1079–1100 (2011).
Petrenas, R. et al. Rapid assessment of size, shape, and chemical complementarity of ligands for computational protein design. Preprint at bioRxiv https://doi.org/10.1101/2025.06.30.662286 (2025).
Zhou, J. & Grigoryan, G. Rapid search for tertiary fragments reveals protein sequence-structure relationships. Protein Sci. 24, 508–524 (2015).
Dauparas, J. et al. Robust deep learning-based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Leman, J. K. et al. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat. Methods 17, 665–680 (2020).
Eastman, P. et al. OpenMM 8: molecular dynamics simulation with machine learning potentials. J. Phys. Chem. B 128, 109–116 (2024).
Yeh, A. H.-W. et al. De novo design of luciferases using deep learning. Nature 614, 774–780 (2023).
Luettgen, J. M., Knabb, R. M., He, K., Pinto, D. J. P. & Rendina, A. R. Apixaban inhibition of factor Xa: microscopic rate constants and inhibition mechanism in purified protein systems and in human plasma. J. Enzyme Inhib. Med. Chem. 26, 514–526 (2011).
Jankowski, W. et al. Engineering and evaluation of FXa bypassing agents that restore hemostasis following apixaban associated bleeding. Nat. Commun. 15, 3912 (2024).
Cho, Y., Pacesa, M., Zhang, Z., Correia, B. & Ovchinnikov, S. BoltzDesign1: inverting all-atom structure prediction model for generalized biomolecular binder design. Preprint at bioRxiv https://doi.org/10.1101/2025.04.06.647261 (2025).
Stark, H. et al. BoltzGen: toward universal binder design. Preprint at bioRxiv https://doi.org/10.1101/2025.11.20.689494 (2025).
Eastman, P. et al. SPICE 2.0.1 (2.0.1) [Data set]. Zenodo https://doi.org/10.5281/ZENODO.10975225 (2024).
Fry, B., & Polizzi, N. LASErMPNN Training Dataset (REDUCE Protonated PDB) [PDB File Format], Chunk 1/2 [Data set]. Zenodo https://doi.org/10.5281/ZENODO.17990180 (2025).
Polizzi, N. Designs and analysis code for neural iterative selection–expansion paper. Zenodo https://doi.org/10.5281/ZENODO.18308430 (2026).
