The case study outlines methods for building machine learning models using the Collaborative Drug Discovery (CDD) vault and public datasets. It discusses techniques for exporting models for internal use or sharing, and includes examples of utilizing specific datasets like MMV for predictions on drug compounds. The document emphasizes the benefits of open-source implementations and collaboration in drug discovery research.

1. CASE STUDY #2
SEAN EKINS
COLLABORATIVE DRUG DISCOVERY, 1633
BAYSHORE HIGHWAY, SUITE 342, BURLINGAME, CA
94010, USA

2. MoDELS RESIDE IN PAPERS
NOT ACCESSIBLE…THIS IS
UNDESIRABLE
How do we share them?
How do we use Them?

3. What if we could build Machine Learning Models in the CDD Vault
We could then use them to score public or private libraries in the
Vault
We can leverage models from other companies or groups to help
internal projects
We can export models to use in other software
We can develop our own private database of models
Deliverable: This Case Study walks you through building a model
with a dataset in CDD Public and generating predictions in a CDD
Vault

4. 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

5. Using MMV Schistosoma mansoni data for CDD Models

6. The MMV data is present in CDD Public

7. Select actives – you want DEAD = active

8. Build Model

9. Model name

10. Model ROC PLOT

11. Predictions for some approved drugs in Vault with Model – select compounds

12. Predictions for some approved drugs in Vault with Model – select
model from protocol section
Select protocol for
model in explore
data
Or customize your
report

13. Predictions for some approved drugs in Vault with Model – output
You can rank molecules by these scores

14. You can create a private database of CDD Models in your CDD Vault

15. You can also export your CDD Model
Search under protocols tab

16. Clark et al., JCIM 55: 1231-1245 (2015)
Exporting models from CDD

17. Clark et al., JCIM 55: 1231-1245 (2015)9R44TR000942-02
You can import your model in a mobile app like
MMDS for private use of the model or sharing
with a collaborator

18. Find out more about
Clark AM, Dole K, Coulon-Spektor A, McNutt A, Grass G, Freundlich JS, Reynolds RC and Ekins S, Open
Source Bayesian Models: 1. Application to ADME/Tox and Drug Discovery Datasets, J Chem Inf Model,
55(6):1231-45, 2015
Clark AM, and Ekins S Open Source Bayesian Models: 2. Mining a “Big Dataset” to Create and Validate
Models with ChEMBL, J Chem Inf Model, 55(6):1246-60, 2015.
Ekins S, Clark, AM and Wright SH, Making transporter models for drug-drug interaction prediction mobile,
Drug Metab Dispos, 43:1642-5, 2015
Clark AM, Dole K and Ekins S, Open Source Bayesian Models: 3. Composite Models for prediction of
binned responses, 56: 275-85, 2016.
Perryman AL, Stratton TP, Ekins S and Freundlich, Predicting mouse liver microsomal stability with
"pruned' machine learning models and public data, Pharm Res, 33: 433-49, 2016.
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