San Bruno, CA, United States
San Bruno, CA, United States

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SAN BRUNO, Calif.--(BUSINESS WIRE)--Numerate, Inc., a computational drug design company applying artificial intelligence (AI) at cloud scale to transform small molecule drug discovery, announced the formation of a multi-year agreement with Takeda Pharmaceutical Company Limited (TSE: 4502) under which Numerate will identify and deliver multiple clinical candidates. Under the agreement, Numerate will drive discovery programs aimed at identifying clinical candidates for use in Takeda’s core therapeutic areas: oncology, gastroenterology, and central nervous system disorders. The projects will rely on Numerate leveraging its AI-driven platform, from hit finding and expansion through lead design/optimization and ADME (absorption, distribution, metabolism and excretion)/toxicity modeling. “This is an ideal arrangement for Numerate because our team will be working largely independently while having the opportunity to leverage Takeda’s global experience, therapeutic area insights, and unique R&D capabilities,” said Guido Lanza, President and CEO of Numerate. “We expect to produce multiple clinical candidates, while also continuing to refine, validate and expand our proprietary AI-driven platform as we work across a broad range of target types and drug design challenges.” Financial terms of the current agreement were not disclosed, but include a combination of milestone payments and royalties that reflect the value of the clinical candidates being delivered. “We are excited to partner with Numerate. Numerate has established an impressive track record of leveraging their AI platform to overcome drug design challenges, both for its own pipeline and in pharma/biotech collaborations,” said David Weitz, Head of Takeda California and Global Research Externalization. “By having Numerate select projects that align with Takeda’s strategy we expect the partnership to yield multiple assets that Takeda can develop into truly transformative medicines for patients.” Numerate is a privately-held computational drug design company that is transforming the discovery of new medicines that fill significant therapeutic gaps by harnessing the vast computational power of the cloud and the ever-increasing amounts of drug discovery data by applying proprietary artificial intelligence algorithms. Numerate’s drug design platform combines advances in computer science and statistics with traditional medicinal chemistry approaches to overcome major challenges in small molecule drug discovery and significantly accelerate candidate selection and optimization. Using this platform, coupled with innovative funding and partnership models, Numerate is developing a therapeutic pipeline focused on producing first-in-class candidates against emerging targets addressing major unmet medical needs in cardiovascular, metabolic and neurodegenerative disease. For more information, visit www.numerate.com.


Methods and systems in accordance with the present invention allow users to efficiently manipulate, analyze, and transmit eXtensible Business Reporting Language (XBRL) reports. They allow users to automatically build financial reports that are acceptable to governing agencies such as the IRS. In one embodiment, the reports are developed by a parser that transforms text documents into software elements containing a format with a hierarchal relationship between the software elements, and an editor that develops reports by referencing the software elements transformed from the text documents. Methods and systems in accordance with the present invention also enable reports to be automatically scheduled by gathering desired information from an accounting system, formatting the information into an XBRL document, and transmitting it to an end source. Furthermore, systems and methods in accordance with the present invention allow a user to translate an XBRL document into RDL format and use the RDL system to manipulate and analyze it.


A system, method, and computer program product are provided for identifying a first markup document including first numerical values and first tags reflecting first characteristics of the first numerical values associated with a first unit of measure, and a second markup document including second numerical values and second tags reflecting second characteristics of the second numerical values associated with a second unit of measure. The first characteristics of the first numerical values associated with the first unit of measure are different from the second characteristics of the second numerical values associated with the second unit of measure. At least a portion of the numerical values of at least one of the first markup document or the second markup document are automatically transformed, so that the at least some of the first numerical values of the first markup document and at least some of the second numerical values of the second markup document have a common unit of measure. Further, at least a part of the first markup document and at least a part of the second markup document are processed, resulting in a single markup document, for display.


A system, method, and computer program product are provided for use in connection with at least one computer-readable Extensible Markup Language (XML)-compliant data document capable of including: a plurality of line items with a plurality of data values, and a plurality of computer-readable semantic tags that describe a semantic meaning of the data values.


A system, method, and computer program product are provided for use in connection with at least one computer-readable Extensible Markup Language (XML)-compliant data document capable of including: a plurality of line items with a plurality of data values, and a plurality of computer-readable semantic tags that describe a semantic meaning of the data values.


Patent
Numerate | Date: 2012-03-06

Methods and articles of manufacture for modeling molecular properties using data regarding the partial orderings of compound properties, or by considering measurements of compound properties in terms of partial orderings are disclosed. One embodiment provides for constructing such partial orderings from data that is not already in an ordered form by processing training data to produce a partial ordering of the compounds with respect to a property of interest. Another embodiment of the invention may process the modified training data to construct a model that predicts the property of interest for arbitrary compounds.


A system, method, and computer program product are provided for use in connection with at least one computer-readable Extensible Markup Language (XML)-compliant data document capable of including: a plurality offline items with a plurality of data values, and a plurality of computer-readable semantic tags that describe a semantic meaning of the data values.


Patent
Numerate | Date: 2010-03-03

Methods and articles of manufacture for modeling molecular properties using data regarding the partial orderings of compound properties, or by considering measurements of compound properties in terms of partial orderings are disclosed. One embodiment provides for constructing such partial orderings from data that is not already in an ordered form by processing training data to produce a partial ordering of the compounds with respect to a property of interest. Another embodiment of the invention may process the modified training data to construct a model that predicts the property of interest for arbitrary compounds.


News Article | June 24, 2014
Site: www.finsmes.com

The round was co-led by Atlas Venture and Lilly Ventures, with participation from existing investors. In conjunction with the funding, Bruce Booth, Ph.D, Partner at Atlas Venture, and Steve Hall, Ph.D., Venture Partner at Lilly Ventures, joined Numerate’s Board of Directors. The company intends to use the funds to advance its lead programs. Led by Guido Lanza, president and chief executive officer, Numerate has developed a drug design platform that uses all relevant data (SAR, patents, phenotypic data, etc.) to treat cardiovascular, metabolic and neurodegenerative diseases. In metabolic disease, the company is targeting the free fatty acid receptors to address diabetes, obesity and related co-morbidities, while in cardiovascular disease they are targeting the Ryanodine receptor 2 to discover improved drugs for heart failure and arrhythmia.

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