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Novel deep learning strategy to better predict pharmacological properties of candidate drugs and focus discovery efforts

Award Number: R44TR002527
ORGANIZATION: NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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Novel deep learning strategy to better predict pharmacological properties of candidate drugs and focus discovery efforts - PROJECT SUMMARY Collaborative Drug Discovery, Inc. (CDD) proposes to continue development of a novel approach based on deep learning neural networks to encode molecules into chemically rich vectors. In Phase 1 we demonstrated that this representation enables computational models that more accurately predict the chemical properties of molecules than state-of-the-art models, yet are also far simpler to build because they do not require any expert decisions or optimization to achieve high performance. In Phase 2 we will exploit this unprecedented simplicity to develop an intuitive software package that will for the first time enable any chemist or biologist working in drug discovery to create and run their own predictive models – without relying on specialized cheminformatics expertise – yet still achieve or exceed the accuracy of the best currently available techniques. Scientists engaged in drug discovery research from academic laboratories to large pharmaceutical companies rely on computational QSAR models to predict pharmacologically relevant properties and obviate the need to perform expensive, time-consuming assays (many of which require animal studies) for every molecule of interest. Improved models will enable researchers to select lead candidate series more effectively, explore chemical space around leads to generate novel IP more efficiently, reduce failure rates for compounds advancing through the drug discovery pipeline, and accelerate the entire drug discovery process. These benefits will be realized broadly across most therapeutic areas. We also plan to take the technology one step further, leveraging our chemically rich vector representation to enable the software to creatively suggest novel compounds (which do not appear in the training libraries, screening libraries, or lead series) that outperform the lead candidates simultaneously on bioactivity, ADME/Tox and PK assays . Solving this inverse problem is the Holy Grail of computational medicinal chemistry and has the potential to revolutionize drug discovery. !


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: 2023 ( Subtotal = $0 )
2023	2021	COLLABORATIVE DRUG DISCOVERY INC	1633 BAYSHORE HWY	BURLINGAME	CA	94010	SAN MATEO	USA	National Center for Advancing Translational Sciences	000	3	12/16/2022	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2021 ( Subtotal = $749,928 )
2021	2021	COLLABORATIVE DRUG DISCOVERY, INC.	1633 BAYSHORE HWY STE 342	BURLINGAME	CA	94010	SAN MATEO	USA	National Center for Advancing Translational Sciences	000	3	3/1/2021	NON-COMPETING CONTINUATION	$749,928
														Subtotal = $749,928

Issue Date FY: 2020 ( Subtotal = $749,928 )
2020	2020	COLLABORATIVE DRUG DISCOVERY, INC.	1633 BAYSHORE HWY STE 342	BURLINGAME	CA	94010	SAN MATEO	USA	National Center for Advancing Translational Sciences	000	2	3/25/2020	COMPETING CONTINUATION	$749,928
														Subtotal = $749,928

Grand Total All Awards = $1,499,856

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Novel deep learning strategy to better predict pharmacological properties of candidate drugs and focus discovery efforts

Award Number: R44TR002527

ORGANIZATION: NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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