Pith Number

pith:DOKN6GXG

pith:2026:DOKN6GXGZO67PWRBFL22LITC76

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Biophysical Considerations for Rational Antibody and ADC Design

Alberto Ocana, Jorge R. Espinosa

Computational biophysics can map how conjugation site, linker, and drug ratio reshape antibody shape to predict ADC binding and uptake.

arxiv:2605.16070 v1 · 2026-05-15 · cond-mat.soft · physics.bio-ph

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2 Internet Archive

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4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We argue that computational biophysics provides an underexploited framework to address this gap by connecting molecular interactions to biological outcomes.

C2weakest assumption

That molecular dynamics, coarse-grained simulations, and free energy calculations can accurately reveal how conjugation site, linker chemistry, and drug-antibody ratio reshape conformational landscapes with direct implications for antigen binding, internalization, and developability.

C3one line summary

Advocates integrating physics-based simulations into antibody and ADC development pipelines to link molecular details to therapeutic outcomes.

References

75 extracted · 75 resolved · 0 Pith anchors

[1] López de Sá, A., et al. (2023). Considerations for the design of antibody drug conjugates (ADCs) for clinical development: lessons learned. J. Hematol. Oncol.J Hematol Oncol 16, 118 2023

[2] Blay, V ., et al. (2024). Strategies to boost antibody selectivity in oncology. Trends Pharmacol. Sci. 45, 1135–1149 2024

[3] Beck, A., et al. (2017). Strategies and challenges for the next generation of antibody- drug conjugates. Nat. Rev. Drug Discov. 16, 315–337 2017

[4] Croitoru, A., et al. (2025). Harnessing computational technologies to facilitate antibody-drug conjugate development. Nat. Chem. Biol. 21, 1138–1147 2025

[5] Robustelli, P ., et al. (2018). Developing a molecular dynamics force field for both folded and disordered protein states. Proc. Natl. Acad. Sci. U. S. A. 115, E4758–E4766 2018

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:51.397579Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1b94df1ae6cbbdf7da212af5a5a262ffb0a8e6f85c10d77f563422621f1c3cbe

Aliases

arxiv: 2605.16070 · arxiv_version: 2605.16070v1 · doi: 10.48550/arxiv.2605.16070 · pith_short_12: DOKN6GXGZO67 · pith_short_16: DOKN6GXGZO67PWRB · pith_short_8: DOKN6GXG

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/DOKN6GXGZO67PWRBFL22LITC76 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1b94df1ae6cbbdf7da212af5a5a262ffb0a8e6f85c10d77f563422621f1c3cbe

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "31bf0cd3b5db1e387b3d0840798bec173ad33bf61357c33448cf388c1e18a214",
    "cross_cats_sorted": [
      "physics.bio-ph"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cond-mat.soft",
    "submitted_at": "2026-05-15T15:32:54Z",
    "title_canon_sha256": "46feb0e5e03eefd5d750d11a131be7864b138929c5ad0f3d9d9bca6ca78a3bce"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16070",
    "kind": "arxiv",
    "version": 1
  }
}