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The pipeline can run end-to-end to produce versioned artifact bundles (serialized models) and analyst-oriented reports suitable for deployment and audit.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      On representative  benchmarks evaluated under Bemis\u2013Murcko scaffold split,  attains state-of-the-art descriptor-based performance: the lowest mean RMSE across the regression suite (, , ; mean RMSE\n                      <jats:inline-formula>\n                        <jats:alternatives>\n                          <jats:tex-math>$$0.658\\pm 0.11$$<\/jats:tex-math>\n                          <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                            <mml:mrow>\n                              <mml:mn>0.658<\/mml:mn>\n                              <mml:mo>\u00b1<\/mml:mo>\n                              <mml:mn>0.11<\/mml:mn>\n                            <\/mml:mrow>\n                          <\/mml:math>\n                        <\/jats:alternatives>\n                      <\/jats:inline-formula>\n                      ), including a substantial improvement on  (RMSE\n                      <jats:inline-formula>\n                        <jats:alternatives>\n                          <jats:tex-math>$$0.494$$<\/jats:tex-math>\n                          <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                            <mml:mrow>\n                              <mml:mn>0.494<\/mml:mn>\n                            <\/mml:mrow>\n                          <\/mml:math>\n                        <\/jats:alternatives>\n                      <\/jats:inline-formula>\n                      vs.\n                      <jats:inline-formula>\n                        <jats:alternatives>\n                          <jats:tex-math>$$0.731$$<\/jats:tex-math>\n                          <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                            <mml:mrow>\n                              <mml:mn>0.731<\/mml:mn>\n                            <\/mml:mrow>\n                          <\/mml:math>\n                        <\/jats:alternatives>\n                      <\/jats:inline-formula>\n                      for a leading graph method). On quantum mechanical benchmarks,  demonstrated superior performance on the single-task dataset  and maintained competitive results on the multi-task  dataset. For classification,  achieves the top ROC\u2013AUC on  (91.4%) while remaining competitive across other benchmark (overall classification average\n                      <jats:inline-formula>\n                        <jats:alternatives>\n                          <jats:tex-math>$$70.4\\pm 11.6$$<\/jats:tex-math>\n                          <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                            <mml:mrow>\n                              <mml:mn>70.4<\/mml:mn>\n                              <mml:mo>\u00b1<\/mml:mo>\n                              <mml:mn>11.6<\/mml:mn>\n                            <\/mml:mrow>\n                          <\/mml:math>\n                        <\/jats:alternatives>\n                      <\/jats:inline-formula>\n                      ). Crucially, all predictions are accompanied by cross-conformal prediction and explicit applicability-domain flags that identify out-of-distribution entries, enabling calibrated and decision support.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>is released on , , and ; all releases embed full provenance (parameters, package versions, checksums) to ensure reproducibility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Scientific contribution<\/jats:title>\n                    <jats:p>(i) enforces best-practice, group-aware validation together with formal statistical comparisons across models, (ii) integrates calibrated uncertainty quantification (cross-conformal prediction) and applicability-domain diagnostics for interpretable, risk-aware predictions, and (iii) exposes both a composable developer API and a one-click pipeline that generates deployment-ready artifacts and human-readable reports, demonstrated on representative benchmarks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13321-026-01175-9","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T09:06:12Z","timestamp":1776848772000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ProQSAR: A modular and reproducible framework for small-data QSAR modeling with fit-and-use models"],"prefix":"10.1186","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9014-9651","authenticated-orcid":false,"given":"Tuyet-Minh","family":"Phan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3532-2064","authenticated-orcid":false,"given":"Tieu-Long","family":"Phan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1825-2781","authenticated-orcid":false,"given":"Phuoc-Chung","family":"Van-Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2473-6309","authenticated-orcid":false,"given":"Lai Hoang Son","family":"Le","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7640-0807","authenticated-orcid":false,"given":"Van-Thinh","family":"To","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0952-1633","authenticated-orcid":false,"given":"Tuyen Ngoc","family":"Truong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7792-375X","authenticated-orcid":false,"given":"Daniel","family":"Merkle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5016-5191","authenticated-orcid":false,"given":"Peter F.","family":"Stadler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"issue":"12","key":"1175_CR1","doi-asserted-by":"publisher","first-page":"4977","DOI":"10.1021\/jm4004285","volume":"57","author":"A Cherkasov","year":"2014","unstructured":"Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R et al (2014) Qsar modeling: where have you been? where are you going to? 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