{
  "abstract": "The transition to sustainable energy relies heavily on rare earth elements (REEs), yet their separation remains a critical industrial bottleneck due to their nearly identical physicochemical properties. The discovery of lanmodulin (LanM), a highly selective REE-binding protein, has inspired bio-based separation strategies; however, predicting binding affinity between different LanM variants and REEs remains a significant computational challenge. Here, we present LANGER, a multimodal deep learning framework for predicting REE protein binding affinity (Kd) by synchronously integrating global evolutionary context, continuous ionic chemistry, and local three-dimensional coordinate geometry. Our architecture combines ESM2 protein sequence embeddings, REE physicochemical descriptions, and geometric graph learning (GGL) features derived from topologies extracted from protein-ion complexes. Using a comprehensive dataset of 616 evaluated LanM homologs against 15 REE ions, we demonstrate the robustness of this framework across two rigorous evaluation settings. In a cluster-based validation analysis designed to test extrapolation to held-out LanM sequence clusters, LANGER achieved a root mean square error (RMSE) of 0.212 and a concordance index (CI) of 0.757. More importantly, in a rigorous evaluation protocol that completely excludes individual ions from the training phase, the fully integrated multimodal framework achieved high predictive accuracy (RMSE = 0.119, Pearson correlation coefficient r = 0.860, CI = 0.800), demonstrating its unique ability to transfer learned physicochemical trends to unseen lanthanide elements. This work establishes a scalable, computationally accurate structure recognition platform for the discovery and design of data-driven REEselective proteins.",
  "abstract_shown": true,
  "affiliations": [
    "Bredesen Center",
    "University of Tennessee",
    "Department of Chemical and Biomolecular Engineering",
    "Department of Mathematics"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Trung Nguyen",
      "orcid": "0009-0005-4004-5312"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Christopher Cotter"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Seunghyun Ryu"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Cong T. Trinh"
    },
    {
      "affs": [
        3,
        1
      ],
      "name": "Duc Duy Nguyen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dqebsby7h88dx.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009150/v1",
  "fulltext_url": null,
  "id": "dqebsby7h88dx",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009150/v1",
  "language": null,
  "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|Machine Learning in Bioinformatics"
    ]
  ],
  "openalex": "W7213943481",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009150/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/dqebsby7h88dx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15009150",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009150/v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Machine Learning in Bioinformatics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "LANGER: Lanthanide Affinity Network via Graph and Evolutionary Representations",
  "title_text": "LANGER: Lanthanide Affinity Network via Graph and Evolutionary Representations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/dqebsby7h88dx",
  "venue": "ChemRxiv",
  "versions": []
}