CDD Awarded Phase 2 SBIR Grant on Deep Learning Strategy for Drug Discovery

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CDD Awarded Phase 2 SBIR Grant on Deep Learning Strategy for Drug Discovery

Burlingame, California —April 7, 2020— Collaborative Drug Discovery, provider of CDD Vault web-based drug discovery informatics platform, announced they won a competitive, peer reviewed Phase 2 SBIR grant from NIH NCATS titled: “Novel deep learning strategy to better predict pharmacological properties of candidate drugs and focus discovery efforts”.

Collaborative Drug Discovery, Inc. (CDD) proposes to continue development of a novel computational modeling 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. We will also extend our validation beyond the Phase 1 proof-of-concept to encompass a wider range of bioactivity, ADME/ Tox, and pharmacokinetic properties. These aims already suffice to create a breakthrough product that will help scientists to accelerate discovery of new drugs broadly across many therapeutic areas.

Concept for a QSAR model utilizing the pre-trained encoder

Computational models that predict pharmacologically relevant properties play a ubiquitous role in drug discovery research from academic laboratories to large pharmaceutical companies. Some properties (e.g. logP) can now be modeled with such high confidence that the models have replaced the need to perform the assays, but many other critical properties (e.g. solubility, ADME, PK, hERG) remain far from this goal. We expect that our proposed chemically rich vectors will significantly advance the state of the art beyond what can be achieved with conventional descriptors and fingerprints. 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.

About this grant

The Small Business Innovation Research (SBIR) is part of a program to enable sharing of biological data. The previous Phase 1 Award Number #1R43TR002527-01 from the National Center for Advancing Translational Sciences is described on NIH Reporter.  This content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Advancing Translational Sciences or the National Institutes of Health.

About Collaborative Drug Discovery, Inc.

CDD’s ( flagship product, “CDD Vault®”, is used to manage chemical registration, structure-activity relationships (SAR), and securely scale collaborations. CDD Vault® is a hosted database solution for secure management and sharing of biological and chemical data. It lets you intuitively organize chemical structures and biological study data, and collaborate with internal or external partners through an easy to use web interface. Available modules within CDD Vault include Activity & Registration, Visualization, Inventory, and ELN.

A complete list of more than 60 publications and patents from CDD can be found online on our resources page at

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CDD logo200 CDD Awarded Phase 2 SBIR Grant on Deep Learning Strategy for Drug Discovery

Media Contact: Barry Bunin, Ph.D., Collaborative Drug Discovery, [email protected]