{
  "abstract": "Accurate prediction of host-guest binding affinities with end-point free energy approaches requires a balance between molecular sampling, energetic description, and computational cost, yet the relative importance and interplay of these factors remain poorly understood for chemically diverse supramolecular systems. Here, we establish a systematic benchmark of end-point affinity calculations across more than 300 host-guest pairs involving pillararenes, cucurbiturils, octa acid, and cyclodextrins. We systematically evaluate single-(1T) and three-trajectory (3T) sampling, harmonic entropy corrections, multiple extended tight-binding and DFT levels, and implicit-solvent treatments, and then compare them with established MM/GBSA protocols. We find that different electronic-structure treatments can produce substantially different, host-dependent affinity landscapes, underscoring that the nominal level of theory alone is insufficient to determine predictive reliability. In contrast, the 3T scheme with independent sampling of bound and unbound states consistently improves affinity prediction, whereas MM-based normal-mode entropy provides little systematic benefit. A multi-objective Pareto analysis further reveals a pronounced accuracy-cost trade-off: although selected DFT protocols achieve high predictive accuracy, MM/GBSA 2 / 26 provides a remarkably favorable and broadly transferable balance between accuracy and computational expense. Finally, host-wise analysis and interpretable decision-tree modeling reveal systematic dependence of protocol preference on readily accessible host descriptors, particularly host charge and size. These results establish a practical framework for moving from universal method benchmarking toward host-aware selection of end-point computational protocols, providing a basis for more efficient and adaptive affinity prediction in supramolecular chemistry.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer Science and Engineering",
    "Beihang University",
    "Faculty of Synthetic Biology",
    "Shenzhen University of Advanced Technology",
    "College of Life Sciences",
    "Nankai University",
    "Department of Biomedical Engineering",
    "Southern University of Science and Technology",
    "School of Physics and Technology",
    "University of Jinan",
    "School of Physical Science and Technology",
    "Yangzhou University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Wenting Tang"
    },
    {
      "affs": [
        2,
        3,
        4,
        5
      ],
      "name": "Zhenhua Lu"
    },
    {
      "affs": [
        2,
        3,
        6,
        7
      ],
      "name": "Lizhen Huo",
      "orcid": "0009-0002-1739-9858"
    },
    {
      "affs": [
        2,
        3,
        6,
        7
      ],
      "name": "Zhixuan Huang"
    },
    {
      "affs": [
        8,
        9
      ],
      "name": "Yang Li"
    },
    {
      "affs": [
        10,
        11
      ],
      "name": "Zuo-yuan Zhang"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Zhaoxi Sun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4h3n5gakkdsbq.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008308/v1",
  "fulltext_url": null,
  "id": "4h3n5gakkdsbq",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008308/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1605|Supramolecular Chemistry and Complexes"
    ]
  ],
  "openalex": "W7208760369",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008308/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-04",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/4h3n5gakkdsbq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008308",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008308/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1605|Supramolecular Chemistry and Complexes",
      "subfield": "organic"
    }
  ],
  "title": "Learning Multiscale Computational Strategies for Supramolecular Recognition",
  "title_text": "Learning Multiscale Computational Strategies for Supramolecular Recognition",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/4h3n5gakkdsbq",
  "venue": "ChemRxiv",
  "versions": []
}