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To generate on a computer molecules with optimized properties, scoring functions can be coupled with a molecular generator to design novel molecules with a desired property profile.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this article, a simple method is described to generate only valid molecules at high frequency (<jats:inline-formula><jats:alternatives><jats:tex-math>$$&gt;300,000$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mo>&gt;<\/mml:mo>\n                      <mml:mn>300<\/mml:mn>\n                      <mml:mo>,<\/mml:mo>\n                      <mml:mn>000<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> molecule\/s using a single CPU core), given a molecular training set. The proposed method generates diverse SMILES (or DeepSMILES) encoded molecules while also showing some propensity at training set distribution matching. When working with DeepSMILES, the method reaches peak performance (<jats:inline-formula><jats:alternatives><jats:tex-math>$$&gt;340,000$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mo>&gt;<\/mml:mo>\n                      <mml:mn>340<\/mml:mn>\n                      <mml:mo>,<\/mml:mo>\n                      <mml:mn>000<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> molecule\/s) because it relies almost exclusively on string operations. The \u201cFast Assembly of SMILES Fragments\u201d software is released as open-source at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/UnixJunkie\/FASMIFRA\">https:\/\/github.com\/UnixJunkie\/FASMIFRA<\/jats:ext-link>. Experiments regarding speed, training set distribution matching, molecular diversity and benchmark against several other methods are also shown.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13321-021-00566-4","type":"journal-article","created":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T19:02:32Z","timestamp":1636916552000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Molecular generation by Fast Assembly of (Deep)SMILES fragments"],"prefix":"10.1186","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1377-944X","authenticated-orcid":false,"given":"Francois","family":"Berenger","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koji","family":"Tsuda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,14]]},"reference":[{"issue":"3","key":"566_CR1","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1021\/acs.jcim.9b00943","volume":"60","author":"F Grisoni","year":"2020","unstructured":"Grisoni F, Moret M, Lingwood R, Schneider G (2020) Bidirectional molecule generation with recurrent neural networks. 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