SAR Knowledge Bases in Drug Discovery

Author(s):  
Stefan Senger ◽  
Andrew R. Leach
2019 ◽  
Author(s):  
Mary Pat Reeve ◽  
Andrew Kirby ◽  
Jamey Wierzbowski ◽  
Mark Daly ◽  
Janna Hutz

AbstractTarget Gene Notebook was developed to enable more efficient linking of genetic associations to functional biological information. This process is essential to translating genetic insights into therapeutic hypotheses and, eventually, drug discovery. Although many public databases provide access to unfiltered genome annotations and genetic results, there was no existing tool to maintain group curation and integration with proprietary experimental data. We provide Target Gene Notebook freely via the MIT open-source license for the purposes of assisting therapeutic target evaluation and the creation of durable institutional and public knowledge bases. Implemented as a Java backend serving mainly Javascript content derived from gene-specific SQLite databases, Target Gene Notebook enables automated access to the most widely used sources of genetic association, expression and protein QTL data, provides intuitive interfaces to credible set and colocalization information, and enables comprehensive literature review and annotation by multiple users simultaneously to create a consistent target knowledgebase within an organization or across a consortium. TargetGeneNotebook is freely available from GitHub https://github.com/targetgenenotebook/tgn.git under the MIT open-source end-user license agreement and a live version of the interface is provided at http://tgn.broadinstitute.org/.


2021 ◽  
Author(s):  
The COVID Moonshot Consortium ◽  
John Chodera ◽  
Alpha Lee ◽  
Nir London ◽  
Frank von Delft

The COVID-19 pandemic is a stark reminder that a barren global antiviral pipeline has grave humanitarian consequences. Future pandemics could be prevented by accessible, easily deployable broad-spectrum oral antivirals and open knowledge bases that derisk and accelerate novel antiviral discovery and development. Here, we report the results of the COVID Moonshot, a fully open-science structure-enabled drug discovery campaign targeting the SARS-CoV-2 main protease. We discovered a novel chemical scaffold that is differentiated to current clinical candidates in terms of toxicity and pharmacokinetics liabilities, and developed it into orally-bioavailable inhibitors with clinical potential. Our approach leverages crowdsourcing, high throughput structural biology, machine learning, and exascale molecular simulations. In the process, we generated a detailed map of the structural plasticity of the main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. In a first for a structure-based drug discovery campaign, all compound designs (>18,000 designs), crystallographic data (>500 ligand-bound X-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2,400 compounds) for this campaign were shared rapidly and openly, creating a rich open and IP-free knowledgebase for future anti-coronavirus drug discovery.


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