{
  "abstract": "Computational design of molecular recognition remains challenging despite advances in deep learning 1–3 . The design of proteins that bind to small molecules has been particularly difficult because it requires simultaneous optimization of protein sequence, protein structure, and ligand conformation 1–7 . Despite their promise, current deep-learning algorithms have struggled to navigate this landscape, precluding the zero- or few-shot design of binders. Here we show that the combination of two neural networks in an iterative design algorithm can create small-molecule binding proteins from scratch with high accuracy. To optimize a design in the joint distribution of sequence, structure, and ligand conformation, we use a pair of neural networks that were trained on reciprocal tasks. We train and use a graph neural network, LASErMPNN, to design protein sequence given protein−ligand co-structure, and we use RoseTTAFold-All Atom 8 (RFAA) to predict protein−ligand co-structure given protein sequence. We iteratively apply these two networks to design proteins that bind the drug, exatecan, a topoisomerase I inhibitor that is prone to inactivation by hydrolysis 9 . Each of four experimentally tested designs bound the drug, with the lowest dissociation constant ( K d ) near 100 nM. The hit rate and highest affinity design each surpassed the current state-of-the-art method by 5- and 70-fold, respectively. We further show that LASErMPNN can improve upon its own designs in a manner resembling chain-of-thought reasoning. Without experimental input, LASErMPNN suggested two mutations that increased affinity by over two orders of magnitude ( K d = 1.2 ± 0.2 nM). Designs were selective, structurally accurate, and achieved their intended purpose to protect the drug from hydrolysis. Our work describes a recipe for using neural networks to automate the design of high affinity small-molecule binding proteins, which should have wide application in the creation of novel drug-delivery vehicles, antidotes, sensors, and enzymes.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA",
    "Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA",
    "Harvard Graduate Program in Biophysics, Harvard University, Boston, MA, USA",
    "Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA. nicholasf_polizzi@dfci.harvard.edu",
    "Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA. nicholasf_polizzi@dfci.harvard.edu"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Benjamin N. Fry",
      "orcid": "0000-0003-0981-9605"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Kaia Slaw",
      "orcid": "0009-0000-2322-1373"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Nicholas F. Polizzi",
      "orcid": "0000-0002-1779-8907"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4y6565rq0bd5m.bib",
  "cited_by": 21,
  "doi": "10.1038/s41586-026-10670-w",
  "fulltext_url": null,
  "id": "4y6565rq0bd5m",
  "issns": [
    "0028-0836",
    "1476-4687"
  ],
  "landing_url": "https://doi.org/10.1038/s41586-026-10670-w",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Protein Structure and Dynamics"
    ]
  ],
  "openalex": "W7165762908",
  "pdf_url": null,
  "pmcid": "PMC13441969",
  "pmid": "42343133",
  "published": "2026-06-24",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/4y6565rq0bd5m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:46Z",
      "native_id": "W7165762908",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41586-026-10670-w"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Protein Structure and Dynamics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Zero-shot design of drug-binding proteins via neural iterative selection−expansion",
  "title_text": "Zero-shot design of drug-binding proteins via neural iterative selection−expansion",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:41Z",
  "url": "https://research.draconitic.com/paper/4y6565rq0bd5m",
  "venue": "Nature",
  "versions": []
}