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However, many compounds remain unidentified due to data complexity and limited number structures in chemical databases. In this work, we have developed and validated a novel machine learning algorithm to predict the retention index (r\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$_i$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msub>\n                            <mml:mrow\/>\n                            <mml:mi>i<\/mml:mi>\n                          <\/mml:msub>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ) values for structurally (un)known chemicals based on their measured fragmentation pattern. The developed model, for the first time, enabled the predication of r\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$_i$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msub>\n                            <mml:mrow\/>\n                            <mml:mi>i<\/mml:mi>\n                          <\/mml:msub>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    values without the need for the exact structure of the chemicals, with an\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$R^2$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msup>\n                            <mml:mi>R<\/mml:mi>\n                            <mml:mn>2<\/mml:mn>\n                          <\/mml:msup>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    of 0.91 and 0.77 and root mean squared error (RMSE) of 47 and 67 r\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$_i$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msub>\n                            <mml:mrow\/>\n                            <mml:mi>i<\/mml:mi>\n                          <\/mml:msub>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    units for the NORMAN (\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$n=3131$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mi>n<\/mml:mi>\n                            <mml:mo>=<\/mml:mo>\n                            <mml:mn>3131<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ) and amide (\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$n=604$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mi>n<\/mml:mi>\n                            <mml:mo>=<\/mml:mo>\n                            <mml:mn>604<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ) test sets, respectively. This fragment based model showed comparable accuracy in r\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$_i$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msub>\n                            <mml:mrow\/>\n                            <mml:mi>i<\/mml:mi>\n                          <\/mml:msub>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    prediction compared to conventional descriptor-based models that rely on known chemical structure, which obtained an\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$R^2$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msup>\n                            <mml:mi>R<\/mml:mi>\n                            <mml:mn>2<\/mml:mn>\n                          <\/mml:msup>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    of 0.85 with an RMSE of 67.\n                  <\/jats:p>","DOI":"10.1186\/s13321-023-00699-8","type":"journal-article","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T12:02:57Z","timestamp":1677240177000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data"],"prefix":"10.1186","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3922-6616","authenticated-orcid":false,"given":"Jim","family":"Boelrijk","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1940-9415","authenticated-orcid":false,"given":"Denice","family":"van Herwerden","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4913-3571","authenticated-orcid":false,"given":"Bernd","family":"Ensing","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4663-3842","authenticated-orcid":false,"given":"Patrick","family":"Forr\u00e9","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8270-6979","authenticated-orcid":false,"given":"Saer","family":"Samanipour","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"issue":"6476","key":"699_CR1","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1126\/science.aay3164","volume":"367","author":"R Vermeulen","year":"2020","unstructured":"Vermeulen R, Schymanski EL, Barab\u00e1si AL, Miller GW (2020) The exposome and health: where chemistry meets biology. 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