{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T20:46:44Z","timestamp":1783370804100,"version":"3.54.6"},"reference-count":31,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2020,10,28]],"date-time":"2020-10-28T00:00:00Z","timestamp":1603843200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,10,28]],"date-time":"2020-10-28T00:00:00Z","timestamp":1603843200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Canada 150 Research Chair Program"},{"DOI":"10.13039\/100000006","name":"Office of Naval Research.","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Austrian Science Fund","award":["Erwin Schroedinger fellowship No. J4309."],"award-info":[{"award-number":["Erwin Schroedinger fellowship No. J4309."]}]},{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["Marie Sklodowska-Curie grant agreement No 795206."],"award-info":[{"award-number":["Marie Sklodowska-Curie grant agreement No 795206."]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2020,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The discovery of novel materials and functional molecules can help to solve some of society\u2019s most urgent challenges, ranging from efficient energy harvesting and storage to uncovering novel pharmaceutical drug candidates. Traditionally matter engineering\u2013generally denoted as inverse design\u2013was based massively on human intuition and high-throughput virtual screening. The last few years have seen the emergence of significant interest in computer-inspired designs based on evolutionary or deep learning methods. The major challenge here is that the standard strings molecular representation SMILES shows substantial weaknesses in that task because large fractions of strings do not correspond to valid molecules. Here, we solve this problem at a fundamental level and introduce S<jats:sc>ELFIES<\/jats:sc> (SELF-referencIng Embedded Strings), a string-based representation of molecules which is 100% robust. Every S<jats:sc>ELFIES<\/jats:sc> string corresponds to a valid molecule, and S<jats:sc>ELFIES<\/jats:sc> can represent every molecule. S<jats:sc>ELFIES<\/jats:sc> can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid. In our experiments, the model\u2019s internal memory stores two orders of magnitude more diverse molecules than a similar test with SMILES. Furthermore, as all molecules are valid, it allows for explanation and interpretation of the internal working of the generative models.<\/jats:p>","DOI":"10.1088\/2632-2153\/aba947","type":"journal-article","created":{"date-parts":[[2020,7,27]],"date-time":"2020-07-27T14:00:43Z","timestamp":1595858443000},"page":"045024","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":592,"title":["Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation"],"prefix":"10.1088","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1620-9207","authenticated-orcid":false,"given":"Mario","family":"Krenn","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"H\u00e4se","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"AkshatKumar","family":"Nigam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pascal","family":"Friederich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan","family":"Aspuru-Guzik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2020,10,28]]},"reference":[{"key":"mlstaba947bib1","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J. 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