CDD: VAULT, CDD: VISION AND CDD: MODELS SOFTWARE FOR

BIOLOGISTS AND CHEMISTS DOING DRUG DISCOVERY

SEAN EKINS AND BARRY A. BUNIN

COLLABORATIVE DRUG DISCOVERY, 1633 BAYSHORE

HIGHWAY, SUITE 342, BURLINGAME, CA 94010, USA

•2016: CDD Vault = Activity & Registration + Inventory (New) + Visualization

•2015: CDD Vault adds advanced calculations, modeling, and visualization

•2014: CDD celebrates 10 Year Users Meeting together with Leading Scientists

•2014: CDD Vault hosts >23 Million Compounds

•2013: CDD Vault securely hosted >9 years – with 99.98% up-time

•2013: CDD surpassed >100,000 customer logins

•2012: CDD FISMA compliant and accredited

•2012: NIH picked CDD Vault for Neuroscience Blueprint Network

•2012: CDD securely hosted >160,000,000 datapoints

•2011: CDD won Bio-IT World Editors’ Choice Best Practices Award

•2011: MM4TB 5 year EU funded project with AstraZeneca, Sanofi-Aventis

•2010: GSK, Novartis, Pfizer, and NIH Collaborations announced

•2009: CDD Vault surpasses >1 Million Compounds

•2008: Gates Foundation 2 year grant (extended to 8 years)

•2005: Eli Lilly co-invested in a syndicate with Omidyar Network and Founders Fund

•2004: CDD spun out of Lilly, UCSF signs up as first customer

Overview: Balancing grants, contracts and sales

Barry Bunin

CEO and Board Director

Visualization

Inventory

ELNELN: Electronic

Laboratory Notebook

(Coming Soon)

Inventory:

Reagents &

Samples

CDD Visualization:

Calculations & Models

CDD Vault: Public +

Collaborative Value Efforts to Advance our Field

for the Public Good

Beyond What the

Market Naturally

Supports

Activity &

Registration

Full Suite:

Commercial

Capabilities

CDD Vault – Modern Drug Discovery Informatics

Ideas focused around neglected diseases

• Work on neglected diseases is less sensitive

• Provides more exposure for diseases

• Less competitive than major diseases like cancer

• Opportunities for grants and funding

• Can build a following by publishing and networking

• Leverages what we do best – we use the product in collaborations

• Find new angles to combine collaboration with software

• Create new knowledge, mine data, model data

• Help create IP for collaborators

• Combine in silico and in vitro

• = ‘Scientific marketing’ adds credibility

NIH GRANTS

• Non - dilutive

• STTR

• Find an academic collaborator lab with data and willing to

make / test compounds

• E.g. Tuberculosis, Chagas, Biocomputation grants

• SBIR

• Develop software with collaborators

• E.g. Ontology development

https://www.collaborativedrug.com/

info@collaborativedrug.com

CDD- Over a decade of drug discovery collaborations

SaaS

Easy to use

Used by

Academia

Industry, Biotech

Private

Selective

collaboration

100’s of

published

datasets

Enterprise CapabilitiesWeb Interface, Management Tools, Integration,

Customizable

Drug Discovery Data Mining Search, Visualization, Presentation

Chemical IntelligenceChemical Drawing, Registration, Property

Calculators, Structure Search, SAR Tools

Collaborative Environment Controlled Access, Data Privacy, Security,

Community

Free Public Data AccessScreening Data, Compound Data

CDD Vault Features

• Online Zendesk

• CDD Models

• CDD Vision

• Integration of CDD Public,

ChemSpider, Zinc, and

PubChem.

Benefits of CDD Vault

CDD Vault® - Differentiators

• Registration, SAR, Tracking, Visualization for budget sensitive labs

• Easy for Your Whole Project Team to Embrace (Key Differentiation)

• Secure & Proven (>250,000 logins)

• 99.98% uptime for global access over 12 years

• No hardware, no software, login online, fast adoptions

Budget Sensitive Startup or Academic Scientists

Won’t lose data

Get better results

Easy to trial, set up, configure, be trained and GO!

ex-Big Pharma Scientist Familiar with Registration/SAR Software

Nimble

Save $$$ with modern cloud solution

Relax – data migration is a snap!

Big Collaborations funded by Pharma, NIH, Foundations (PPP)

Control exactly which data you share with others

Relax – security is built in

Foster interactions between biologists and chemists

Passed Big Pharma & NIH FISMA audits (CDD does not own IP)

CDD Vault® - “Value Proposition”

Login/month

TB Project overview

Phase I STTR – Proof of concept of mimic strategy

Phase II STTR – Expand mimic strategy and validation of phase I hits

Ekins et al., PLoS One. 2015, 10:e0141076

We have published 27 papers on tuberculosis with collaborators since 2010

~50 papers and book chapters etc. published on CDD since 2009

Getting the word out

Copyright © 2013 All Rights Reserved Collaborative Drug Discovery

MM4TB: 25 organizations

New

Old

Neuroscience

Kinetoplastid Drug Development

Consortium

CDD Vault: Visualization, Calculations, & Models

• Data Visualization Analytics

• Scatterplots, Histograms, Interactive

• Excel Type Calculations

• On Experimental and/or Calculated Values

• Automatically During Data Capture

• Machine Learning Algorithms

• Simplifies Model Building with Public or Private Data

CDD VISION

Data taken from CDD Vault and utilized in CDD Vision

Backend formed using immutable and Crossfilter.js, binding layer uses

d3.js and jQuery, Rendering uses d3.js and Pixi.js

Launching CDD Vision

CDD VISION

Filters

CDD VISION

Compound details

CDD VISION

Multiple plots, different sizes

CDD VISION

MoDELS RESIDE IN PAPERS

NOT ACCESSIBLE…THIS IS

UNDESIRABLE

How do we share them?

How do we use Them?

Open Extended Connectivity Fingerprints

ECFP_6 FCFP_6

• Collected,

deduplicated,

hashed

• Sparse integers

• Invented for Pipeline Pilot: public method, proprietary details

• Often used with Bayesian models: many published papers

• Built a new implementation: open source, Java, CDK

– stable: fingerprints don't change with each new toolkit release

– well defined: easy to document precise steps

– easy to port: already migrated to iOS (Objective-C) for TB Mobile app

• Provides core basis feature for CDD open source model service

Clark et al., J Cheminform 6:38 2014

Predictions for the InhA target: (a) the ROC curve with ECFP_6 and FCFP_6 fingerprints; (b)

modified Bayesian estimators for active and inactive compounds; (c) structures of selected

binders.

For each listed target with at least two binders, it is first assumed that all of the molecules in

the collection that do not indicate this as one of their targets are inactive.

In the app we used ECFP_6 fingerprints

Building Bayesian models for each target in TB Mobile

Clark et al., J Cheminform 6:38 2014

TB Mobile

Ekins et al., J Cheminform 5:13, 2013Clark et al., J Cheminform 6:38 2014

Predict targetsCluster molecules

http://goo.gl/vPOKS

http://goo.gl/iDJFR

Single point data > 300K molecules

Uses Bayesian algorithm and FCFP_6 fingerprints

Clark et al., J Cheminform 6:38 2014

Using AZ-ChEMBL data for CDD Models

• Human microsomal intrinsic

clearance • Rat hepatocyte intrinsic

clearance

Clark et al., JCIM 55: 1231-1245 (2015)

Exporting models from CDD

Clark et al., JCIM 55: 1231-1245 (2015)9R44TR000942-02

Open Models in MMDS

9R44TR000942-02

Composite Models – Binned Bayesians

Clark et al., J Chem Inf Model. 2016, 56(2):275-85

Summary

• Accessible software

• Used widely in academia and industry

• Leader in collaboration and security

• Grown steadily through sales and grants

• Dedicated sales in Europe, Asia

• Coming soon: ELN

• CDD provides integrated software for

drug discovery

Anna Coulon-Spektor, Kellan Gregory, Charlie Weatherall, Krishna Dole,

Andrew McNutt, Peter Nyberg, Tom Gilligan, Xiao Ba, Barbara Holtz,

Sylvia Ernst, Frank Cole, Marc Navre, Alex M. Clark

Joel Freundlich

Peter Madrid

Carolyn Talcott

Malabika Sarker

Jair Lage de Siqueira-Neto

EU FP7 funding MM4TB

NIH NIAID

NIH NLM

NIH NCATS

Bill and Melinda Gates Foundation (Grant#49852)

Acknowledgments