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  <front>
    <journal-meta>
      <journal-id journal-id-type="iso-abbrev">Pharmacophore</journal-id>
      <journal-id journal-id-type="publisher-id">pharmacophorejournal.com</journal-id>
      <journal-id journal-id-type="publisher-id">Pharmacophore</journal-id>
      <journal-title-group>
        <journal-title>Pharmacophore</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2229-5402</issn>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">pharmacophorejournal.com-6972</article-id>
      <article-id pub-id-type="doi">10.51847/eQYRUJt7YT</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Original research</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Tracing Protein–Ligand Predictions Back to Atomic Contacts, Binding Context, Structural Alternatives, and Model Uncertainty</article-title>
      </title-group>
                    <contrib-group>
                      <contrib contrib-type="author">
              <name>
                <surname>Bianchi</surname>
                <given-names>Giuseppe</given-names>
              </name>
                              <xref rid="aff1" ref-type="aff">1</xref>
                                                            <xref rid="cor1" ref-type="corresp" />
                          </contrib>
                      <contrib contrib-type="author">
              <name>
                <surname>Dubois</surname>
                <given-names>Marie</given-names>
              </name>
                              <xref rid="aff2" ref-type="aff">2</xref>
                                        </contrib>
                      <contrib contrib-type="author">
              <name>
                <surname>Reyes</surname>
                <given-names>Pedro</given-names>
              </name>
                              <xref rid="aff3" ref-type="aff">3</xref>
                                        </contrib>
                      <contrib contrib-type="author">
              <name>
                <surname>Lombardi</surname>
                <given-names>Chiara</given-names>
              </name>
                              <xref rid="aff4" ref-type="aff">4</xref>
                                        </contrib>
                  </contrib-group>
                  <aff id="aff1">
            <label>1</label>Department of Protein–Ligand Prediction Traceability, Faculty of Pharmacy, University of Naples Federico II, Naples, Italy.
          </aff>
                  <aff id="aff2">
            <label>2</label>Department of Atomic Contacts and Binding Context, Faculty of Pharmacy, University of Avignon, Avignon, France.
          </aff>
                  <aff id="aff3">
            <label>3</label>Department of Structural Alternatives and Conformational Ensembles, Faculty of Pharmacy, University of Chile, Santiago, Chile.
          </aff>
                  <aff id="aff4">
            <label>4</label>Department of Model Uncertainty in Molecular Recognition, Faculty of Pharmaceutical Sciences, University of Pisa, Pisa, Italy.
          </aff>
                          <author-notes>
            <corresp id="cor1">
              <bold>Address for correspondence:</bold> Prof. Wael Abu Dayyih, Department of
              Pharmaceutical Chemistry, Faculty of Pharmacy, Mutah University, Al-Karak 61710, Jordan.
                              E-mail: <email xlink:href="giuseppe.bianchi@unina.it">giuseppe.bianchi@unina.it</email>
                          </corresp>
          </author-notes>
                    <pub-date pub-type="epub">
        <day>28</day>
        <month>02</month>
        <year>2025</year>
      </pub-date>
      <volume>16</volume>
      <issue>1</issue>
      <fpage>71</fpage>
      <lpage>80</lpage>
      <permissions>
        <copyright-statement>
          Copyright: &#x000a9; 2026 Pharmacophore
        </copyright-statement>
        <copyright-year>2026</copyright-year>
        <license>
          <ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/"
            specific-use="textmining" content-type="ccbyncsalicense">
            https://creativecommons.org/licenses/by-nc-sa/4.0/</ali:license_ref>
          <license-p>This is an open access journal, and articles are distributed under the terms of
            the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows
            others to remix, tweak, and build upon the work non-commercially, as long as appropriate
            credit is given and the new creations are licensed under the identical terms.</license-p>
        </license>
      </permissions>
      <abstract>
        <title>A<sc>BSTRACT</sc></title>
        <p>Computational models can assign credible binding-affinity or interaction predictions to protein–ligand complexes while providing little defensible information about the atomic relationships, hydration patterns, chemical microstates, or conformational states on which those predictions depend. This distinction limits the scientific interpretation of model outputs in pharmaceutical research because a numerically accurate prediction does not necessarily identify a transferable binding hypothesis or explain how a molecular modification may alter affinity or selectivity. This Original Molecular Evidence-Tracing Article proposes the Protein–Ligand Molecular Evidence Trace, a conceptual record that connects a versioned model output to atomic-contact hypotheses, binding-context assumptions, structural alternatives, evidence provenance, and multidimensional uncertainty. The approach treats direct contacts, water-mediated interactions, protonation and tautomer states, ligand poses, receptor conformations, and model-derived contributions as related but non-interchangeable evidence objects. It further distinguishes model faithfulness from mechanistic confirmation and separates numerical confidence from uncertainty about structural state, applicability, and external evidence quality. The proposed construct is intended to support later perturbation testing, counterfactual analysis, structural corroboration, and bounded interpretation in hit assessment, selectivity reasoning, and lead optimization. Its principal contribution is not a new affinity predictor, but an explicit evidentiary architecture for showing what a prediction refers to, which structural explanation it assumes, what alternatives remain plausible, and which claims are scientifically admissible. The construct remains conceptual and requires architecture-specific, system-specific, and prospective validation. It cannot establish binding mechanism, causal interaction importance, experimental confirmation, pharmaceutical developability, clinical utility, or operational readiness on its own.</p>
      </abstract>
      <kwd-group>
                <kwd>Protein–ligand binding</kwd>
                <kwd>Binding-affinity prediction</kwd>
                <kwd>Atomic contacts</kwd>
                <kwd>Binding-site water</kwd>
                <kwd>Protonation states</kwd>
                <kwd>Structural uncertainty</kwd>
              </kwd-group>
    </article-meta>
  </front>
</article>