{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T19:03:34Z","timestamp":1790881414441,"version":"4.1.0"},"reference-count":40,"publisher":"American Physical Society (APS)","issue":"2","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100017924","name":"La Trobe Institute for Molecular Science, La Trobe University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100017924","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.aps.org"],"crossmark-restriction":true},"short-container-title":["Phys. Rev. E"],"accepted":{"date-parts":[[2026,6,17]]},"abstract":"<jats:p>\n                    Using tools from network theory, we analyze the organization of a MYH9-oriented druglike library in chemical space using a multidescriptor framework. The dataset is drawn from ZINC, a publicly available database of commercially accessible compounds curated for virtual screening and drug discovery. Starting from 6004 molecules, preprocessing yields 5000 structurally valid and descriptor-complete compounds. Similarity is defined via Tanimoto distance on Morgan fingerprints and single-descriptor distances for xLogP, HBD, HBA, molecular weight, and rotatable bonds. For each representation we construct\n                    <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                      <a:mi>k<\/a:mi>\n                    <\/a:math>\n                    -nearest-neighbor networks and identify communities using the Louvain\u2013Leiden algorithm. All networks exhibit highly significant modularity (\n                    <b:math xmlns:b=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                      <b:mrow>\n                        <b:mi>Q<\/b:mi>\n                        <b:mo>=<\/b:mo>\n                        <b:mn>0.91<\/b:mn>\n                        <b:mo>\u2212<\/b:mo>\n                        <b:mn>0.99<\/b:mn>\n                      <\/b:mrow>\n                    <\/b:math>\n                    ) relative to degree-preserving null models, demonstrating pronounced nonrandom chemical organization across descriptors. Cross-descriptor robustness is quantified through a coclustering matrix over\n                    <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                      <c:mrow>\n                        <c:mn>1.25<\/c:mn>\n                        <c:mo>\u00d7<\/c:mo>\n                        <c:msup>\n                          <c:mn>10<\/c:mn>\n                          <c:mn>7<\/c:mn>\n                        <\/c:msup>\n                      <\/c:mrow>\n                    <\/c:math>\n                    molecular pairs, measuring how consistently compound pairs co-occur within the same community across descriptor-specific networks. Although most pairs show limited agreement, a sparse high-consensus core emerges, highlighting the complementarity of the descriptors. Minimum spanning trees derived from structural and consensus similarities reveal distinct backbone topologies: a scaffold-driven, sparse structure versus a compact, hub-rich consensus network. Betweenness centrality on these backbones identifies compounds that are both structurally central and descriptor-balanced. These results provide a statistically validated network representation of chemical space and a principled strategy to extract consensus-stable compounds for downstream screening.\n                  <\/jats:p>","DOI":"10.1103\/v4k3-49dx","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T18:35:40Z","timestamp":1781721340000},"update-policy":"https:\/\/doi.org\/10.1103\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Network-based drug repurposing for MYH9-related nephritis"],"prefix":"10.1103","volume":"114","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4035-0839","authenticated-orcid":true,"given":"Muhammad","family":"Ali","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04yzxz566","id-type":"ROR","asserted-by":"publisher"}],"name":"Ca'Foscari University of Venice"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tommaso","family":"Gili","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/035gh3a49","id-type":"ROR","asserted-by":"publisher"}],"name":"Scuola IMT Alti Studi Lucca"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9377-3616","authenticated-orcid":true,"given":"Guido","family":"Caldarelli","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04yzxz566","id-type":"ROR","asserted-by":"publisher"}],"name":"Ca'Foscari University of Venice"},{"id":[{"id":"https:\/\/ror.org\/05rcgef49","id-type":"ROR","asserted-by":"publisher"}],"name":"Institute of Complex Systems"},{"id":[{"id":"https:\/\/ror.org\/0390mzx53","id-type":"ROR","asserted-by":"publisher"}],"name":"London Institute for Mathematical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"16","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"v4k3-49dxCc1R1","doi-asserted-by":"publisher","DOI":"10.1038\/nrd1468","type":"journal-article"},{"key":"v4k3-49dxCc2R1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1132939","type":"journal-article"},{"key":"v4k3-49dxCc3R1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg2918","type":"journal-article"},{"key":"v4k3-49dxCc4R1","doi-asserted-by":"publisher","DOI":"10.1038\/s41421-020-0153-3","type":"journal-article"},{"key":"v4k3-49dxCc5R1","doi-asserted-by":"publisher","DOI":"10.4103\/0975-8453.59519","type":"journal-article"},{"key":"v4k3-49dxCc6R1","doi-asserted-by":"publisher","DOI":"10.1038\/s41573-019-0024-5","type":"journal-article"},{"key":"v4k3-49dxCc7R1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2018.01.039","type":"journal-article"},{"key":"v4k3-49dxCc8R1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty294","type":"journal-article"},{"key":"v4k3-49dxCc10R1","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.26726","type":"journal-article"},{"key":"v4k3-49dxCc11R1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae461","type":"journal-article"},{"key":"v4k3-49dxCc12R1","doi-asserted-by":"crossref","DOI":"10.1093\/acprof:oso\/9780199211517.001.0001","type":"book","volume-title":"Scale-Free Networks: Complex Webs in Nature and Technology","author":"G. 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All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright_statement","label":"Copyright statement","group":{"name":"copyright","label":"copyright"}},{"value":"2026","name":"copyright_year","label":"Copyright year","group":{"name":"copyright","label":"copyright"}},{"value":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/","name":"creative_commons_license","label":"Creative Commons license","group":{"name":"licenses","label":"Licenses"}},{"value":"\u00a92026 The authors","name":"copyright_holder","label":"Copyright holder","group":{"name":"copyright","label":"copyright"}}],"article-number":"024309"}}