Centralized assay datasets for modelling support of small drug discovery organizations

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R43GM122196-01
Agency Tracking Number: R43GM122196
Amount: $149,999.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 400
Solicitation Number: PA15-269
Timeline
Solicitation Year: 2015
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-01-01
Award End Date (Contract End Date): 2017-08-31
Small Business Information
5616 HILLTOP NEEDMORE RD, Fuquay Varina, NC, 27526-9278
DUNS: 079704473
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 SEAN EKINS
 (215) 687-1320
 ekinssean@yahoo.com
Business Contact
 SEAN EKINS
Phone: (215) 687-1320
Email: collaborationspharma@gmail.com
Research Institution
N/A
Abstract
Summary The objective of Assay Central is to compile a comprehensive collection of datasets for structure activity data for a broad variety of disease targets and absorption distribution metabolism excretion and toxicology ADMET properties in a form that is immediately ready for model building and other forms of analysis using cheminformatics methods This is aided by the existence of many sources of curated open data and one in particular ChEMBL will be used as the nucleus in Phase I This bioassay data collection is incredibly valuable but not currently provided in a form that is ready to go for use by small research and development Randamp D organisations that do not have their own in house cheminformatics teams The effort required to preprocess filter merge validate and normalize the structure and activity data requires a great deal of software expertise and medicinal chemistry domain knowledge which are key skillsets that are rare and expensive to combine within the same team Create a script to analyze the databases like ChEMBL selected parts of PubChem and others and partition it into groups of compatible activity measurements against the same target We will seed the dataset collection with a set of target assay groups that have been recently extracted from the ChEMBL v database as well as EPA Tox measurements using methodology that we have already developed similar to that described in We will build error checking and correction software We will apply best of breed methodology for checking and correcting structure activity data which errs on the side of caution for problems with non obvious solutions so that we can manually identify problems and either apply patches or datasource specific automated corrections We will build and validate Bayesian models with the datasets collected and cleaned For each of the target activity groups we will create a Bayesian model using ECFP or FCFP fingerprints and this will be one of the primary outputs from the project Models will be evaluated using internal and external testing with receiver operator characteristic ROC andgt the integral of the true negative rate true positive rate curve as well as the enrichment Kappa value and positive predicted value We will develop new data visualization tools as a proof of concept in phase I We have already begun to explore preliminary visualization methods using multiple models but these have so far focused primarily on a handful of machine learning models selected from a very large list New visualization techniques are required to summarize large matrices of data e g a list of proposed structures vs thousands of target models In Phase II we will expand by upgrading to newer ChEMBL releases selectively incorporating screening runs from other databases such as PubChem These tools will consist of software created explicitly for this project particularly web based interfaces as well as enhanced functionality added to rd party tools that we influence e g mobile apps and open source projects that we have already contributed to e g CDK for fingerprints and Bayesian modelling We will widely publicise Assay Central at conferences and in papers Being able to use transparent computational models simultaneously for visualizing activity trends for multiple targets both diseases and ADMET removes the burden of curation or purchasing and maintaining expensive software and drastically simplifies the addition of new data It also represents a new frontier of drug discovery as a world of small agile distributed Randamp D organizations has access to valuable public datasets that can inform their research Such computational models will assist in drug repurposing efforts internally and with our collaborators while likely identifying new compounds for a wide array of drug discovery projects Narrative There are massive publically accessible databases that include a broad variety of disease targets and absorption distribution metabolism excretion and toxicology ADMET properties that are not a form that is immediately ready for machine learning model building The Assay Central project will compile a comprehensive collection of these datasets from PubChem and ChEMBL for structure activity data This will enable the user to quickly and automatically use machine learning models for various targets and properties The approach will also have high value for drug repurposing efforts and identifying new compounds for targets with creation of new IP in our own research on neglected and rare diseases and in the laboratories of customers

* Information listed above is at the time of submission. *

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