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These resources typically depend heavily on human curation. Natural language processing systems that read the primary literature have the potential to substantially extend knowledge resources while reducing the burden on human curators. However, machine\u2010reading systems are limited by high error rates and commonly generate fragmentary and redundant information. Here, we describe an approach to precisely assemble molecular mechanisms at scale using multiple natural language processing systems and the Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA identifies full and partial overlaps in information extracted from published papers and pathway databases, uses predictive models to improve the reliability of machine reading, and thereby assembles individual pieces of information into non\u2010redundant and broadly usable mechanistic knowledge. Using INDRA to create high\u2010quality corpora of causal knowledge we show it is possible to extend protein\u2013protein interaction databases and explain co\u2010dependencies in the Cancer Dependency Map.<\/jats:p>","DOI":"10.15252\/msb.202211325","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T05:53:33Z","timestamp":1679291613000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Automated assembly of molecular mechanisms at scale from text mining and curated databases"],"prefix":"10.1038","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6095-2466","authenticated-orcid":false,"given":"John A","family":"Bachman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9439-5346","authenticated-orcid":false,"given":"Benjamin M","family":"Gyori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3364-1838","authenticated-orcid":false,"given":"Peter K","family":"Sorger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,20]]},"reference":[{"key":"BFMSB202211325_CR1","doi-asserted-by":"crossref","DOI":"10.1093\/bioadv\/vbab006","volume":"1","author":"Y Ahmed","year":"2021","unstructured":"Ahmed Y, Telmer CA, Miskov\u2010Zivanov N (2021) CLARINET: efficient learning of dynamic network models from literature. 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