<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.3 20210610//EN" "JATS-archivearticle1-3-mathml3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"
  dtd-version="1.3" xml:lang="en" article-type="research-article">
  <?DTDIdentifier.IdentifierValue -//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN?>
  <?DTDIdentifier.IdentifierType public?>
  <?SourceDTD.DTDName JATS-journalpublishing1.dtd?>
  <?SourceDTD.Version 1.2?>
  <?ConverterInfo.XSLTName jats2jats3.xsl?>
  <?ConverterInfo.Version 1?>
  <?properties open_access?>
  <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-6929</article-id>
      <article-id pub-id-type="doi">10.51847/rWq6JO8bqU</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Original research</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>One Pharmaceutical Reasoning Space for Molecules, Targets, Pathways, Phenotypes, Diseases, and Therapeutic Evidence</article-title>
      </title-group>
                    <contrib-group>
                      <contrib contrib-type="author">
              <name>
                <surname>Wang</surname>
                <given-names>Yifan</given-names>
              </name>
                              <xref rid="aff1" ref-type="aff">1</xref>
                                                            <xref rid="cor1" ref-type="corresp" />
                          </contrib>
                      <contrib contrib-type="author">
              <name>
                <surname>Hansen</surname>
                <given-names>Kristina</given-names>
              </name>
                              <xref rid="aff2" ref-type="aff">2</xref>
                                        </contrib>
                      <contrib contrib-type="author">
              <name>
                <surname>Diarra</surname>
                <given-names>Moussa</given-names>
              </name>
                              <xref rid="aff3" ref-type="aff">3</xref>
                                        </contrib>
                      <contrib contrib-type="author">
              <name>
                <surname>Lund</surname>
                <given-names>Henrik</given-names>
              </name>
                              <xref rid="aff4" ref-type="aff">4</xref>
                                        </contrib>
                  </contrib-group>
                  <aff id="aff1">
            <label>1</label>Department of Unified Pharmaceutical Reasoning, Faculty of Pharmacy, Nanjing Agricultural University, Nanjing, China.
          </aff>
                  <aff id="aff2">
            <label>2</label>Department of Molecular–Target–Pathway Integration, Faculty of Pharmaceutical Sciences, Aarhus University, Aarhus, Denmark.
          </aff>
                  <aff id="aff3">
            <label>3</label>Department of Disease–Phenotype–Therapeutics Reasoning, Faculty of Pharmacy, University of Bamako, Bamako, Mali.
          </aff>
                  <aff id="aff4">
            <label>4</label>Department of Therapeutic Evidence and Decision Space, Faculty of Pharmacy, Lund University, Lund, Sweden.
          </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="yifan.wang@njau.edu.cn">yifan.wang@njau.edu.cn</email>
                          </corresp>
          </author-notes>
                    <pub-date pub-type="epub">
        <day>28</day>
        <month>12</month>
        <year>2024</year>
      </pub-date>
      <volume>15</volume>
      <issue>6</issue>
      <fpage>118</fpage>
      <lpage>126</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>Pharmaceutical knowledge is distributed across chemical, biological, preclinical, clinical, safety, formulation, and regulatory-adjacent data environments that encode different objects, relations, contexts, and evidentiary standards. Although contemporary computational methods can connect parts of this landscape, predictive performance on an isolated task does not establish that the underlying representation is scientifically coherent, evidentially traceable, or suitable for pharmaceutical reasoning. This article proposes a unified pharmaceutical reasoning space as an original knowledge-representation architecture for organizing molecules and drug products, targets, pathways, phenotypes, diseases, and therapeutic evidence within one context-sensitive semantic substrate. The approach is architectural rather than empirical: it synthesizes representational requirements from pharmaceutical data integration, biomedical knowledge graphs, curated molecular resources, pathway knowledgebases, phenotype ontologies, and evidence-aware reasoning. The central contribution is the context-qualified, evidence-bearing assertion, defined as a typed relation between canonical entities that retains provenance, evidence class, biological and experimental context, temporal status, uncertainty, and contradiction without collapsing these dimensions into a single score. The architecture separates source ingestion, semantic representation, reasoning services, validation, and decision use so that graph connectivity, embedding proximity, or ranking performance cannot silently become claims of mechanism, causality, clinical utility, or regulatory acceptability. Its principal limitations are dependence on source quality, incomplete ontological alignment, unresolved contextual gaps, and the absence of empirical validation. The proposed reasoning space may nevertheless provide a disciplined basis for integrating heterogeneous pharmaceutical evidence, comparing competing explanations, and designing future evaluation programs across discovery, safety, and repurposing.</p>
      </abstract>
      <kwd-group>
                <kwd>Pharmaceutical knowledge representation</kwd>
                <kwd>Biomedical knowledge graphs</kwd>
                <kwd>Semantic interoperability</kwd>
                <kwd>Evidence provenance</kwd>
                <kwd>Context-qualified assertions</kwd>
                <kwd>Drug discovery</kwd>
              </kwd-group>
    </article-meta>
  </front>
</article>