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Virtual Approaches to New Chemistries

Award Number: U18TR004138
ORGANIZATION: NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES
OPDIV: NIH
AWARD CLASS: COOPERATIVE AGREEMENT
AWARD ACTIVITY TYPE: KDA (KNOWLEDGE/DEVELOPMENT/APPLICATION)
PERIOD OF PERFORMANCE START DATE: 06/06/2022
PERIOD OF PERFORMANCE END DATE: 05/31/2024

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Virtual Approaches to New Chemistries - Project Summary/Abstract Two new virtual chemistry technologies will be added to the NCATS ASPIRE project as separate modules. The first module will enable new chemistries to be modelled and selected from cutting edge (deep) machine learning technology using the latest structure/activity data taken directly from instruments. The second module will be a novel informatics system for capturing chemistry-rich data in a semantic template as machine-readable reactions which will increase the utility of chemical reactions in electronic lab notebooks and allow more precise interrogation and automation of reaction analyses (and their corresponding reaction products). The deep learning technology in module 1 is based on our new chemically rich vector (CRV) methodology, which is able to compress information about chemical structures into a vector of 64 numbers with an efficiency that allows the encoding process to be reversed: not only can a CRV be converted back into its original structure with high success (>90% exact match), but a modified CRV can be converted into a structure that is representative of that point in chemical space. CRVs make excellent descriptors for SAR/QSAR iteration because they contain much more chemical information in a small space, allowing the automation of structure-activity models to be more streamlined, relative to conventional descriptors. The resulting models will explore the multi-dimensional space via an interactive visual interface (human-directed) or a back-end algorithm to constantly search for new and better structures (machine-directed). Both interactive and automated processes will be connected back into the ASPIRE automation cycle so that they can be synthesized and measured (hypothesis evaluation and iterative optimization). The second module, machine-readable reactions, draws from our extensive experience developing the BioHarmony Annotator (formerly: BioAssay Express) which uses natural language models to assign semantic ontology terms to biological assay protocols, turning them from unstructured text into machine-readable data. Extracting the full content of reactions from protocols and chemical structure diagrams is remarkably difficult given the unstructured nature of text, abbreviations, shortcuts and assumptions that go into diagrams. It is further complicated by the need to connect the materials in the scheme with the reaction text description (e.g. reagents, solvents, the sequences involved in the recipe, reaction workup, and product characterization). As an alternative, we will modularize the CDD stoichiometric sketcher, which will allow us to extract this data. We will work with NCATS to identify important fields to capture, creating a machine readable chemical reaction template.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $0 )
2024	2023	COLLABORATIVE DRUG DISCOVERY, INC.	1633 BAYSHORE HWY	BURLINGAME	CA	94010	SAN MATEO	USA	National Center for Advancing Translational Sciences	000	2	8/15/2024	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2023 ( Subtotal = $440,000 )
2023	2023	COLLABORATIVE DRUG DISCOVERY INC	1633 BAYSHORE HWY	BURLINGAME	CA	94010	SAN MATEO	USA	National Center for Advancing Translational Sciences	002	2	5/12/2023	NON-COMPETING CONTINUATION	$440,000
2023	2022	COLLABORATIVE DRUG DISCOVERY INC	1633 BAYSHORE HWY	BURLINGAME	CA	94010	SAN MATEO	USA	National Center for Advancing Translational Sciences	000	1	3/16/2023	NEW	$0
2023	2022	COLLABORATIVE DRUG DISCOVERY INC	1633 BAYSHORE HWY	BURLINGAME	CA	94010	SAN MATEO	USA	National Center for Advancing Translational Sciences	001	1	5/2/2023	NEW	$0
														Subtotal = $440,000

Issue Date FY: 2022 ( Subtotal = $440,000 )
2022	2022	COLLABORATIVE DRUG DISCOVERY, INC.	1633 BAYSHORE HWY STE 342	BURLINGAME	CA	94010	SAN MATEO	USA	National Center for Advancing Translational Sciences	000	1	6/6/2022	NEW	$440,000
														Subtotal = $440,000

Grand Total All Awards = $880,000

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Virtual Approaches to New Chemistries

Award Number: U18TR004138

ORGANIZATION: NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES

OPDIV: NIH

AWARD CLASS: COOPERATIVE AGREEMENT

AWARD ACTIVITY TYPE: KDA (KNOWLEDGE/DEVELOPMENT/APPLICATION)

PERIOD OF PERFORMANCE START DATE: 06/06/2022

PERIOD OF PERFORMANCE END DATE: 05/31/2024

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