A patent data base of 6.7 million compounds generated by a very high performance computer (Blue Gene) requires new techniques for exploitation when extensive use of chemical similarity is involved. Such exploitation includes the taxonomic classification of chemical themes, and data mining to assess mutual information between themes and companies. Importantly, we also launch candidates that evolve by "natural selection" as failure of partial match against the patent data base and their ability to bind to the protein target appropriately, by simulation on Blue Gene. An unusual feature of our method is that algorithms and workflows rely on dynamic interaction between match-and-edit instructions, which in practice are regular expressions. Similarity testing by these uses SMILES strings and, less frequently, graph or connectivity representations. Examining how this performs in high throughput, we note that chemical similarity and novelty are human concepts that largely have meaning by utility in specific contexts. For some purposes, mutual information involving chemical themes might be a better concept.

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Text analytics is becoming an increasingly important tool used in biomedical research. While advances continue to be made in the core algorithms for entity identification and relation extraction, a need for practical applications of these technologies arises. We developed a system that allows users to explore the US Patent corpus using molecular information. The core of our system contains three main technologies: A high performing chemical annotator which identifies chemical terms and converts them to structures, a similarity search engine based on the emerging IUPAC International Chemical Identifier (InChI) standard, and a set of on demand data mining tools. By leveraging this technology we were able to rapidly identify and index 3, 623, 248 unique chemical structures from 4, 375, 036 US Patents and Patent Applications. Using this system a user may go to a web page, draw a molecule, search for related Intellectual Property (IP) and analyze the results. Our results prove that this is a far more effective way for identifying IP than traditional keyword based approaches.

SignificanceWe adapted natural language processing to the biological literature and demonstrated end-to-end automated knowledge discovery by exploring subtle word connections. General text mining scanned 21 million publication abstracts and selected a reliable 130,000 from which hypothesis generation algorithms predicted kinases not known to phosphorylate p53, but likely to do so. Six of these p53 kinase candidates passed experimental validation. Among them NEK2 was examined in depth and shown to repress p53 and promote cell division. This work demonstrates the possibility of integrating a vast corpora of written knowledge to compute valuable hypotheses that will often test true and fuel discovery.