HTS Follow-up using JChem Base: Virtual and Vendor Neighbors

1. HTS Follow-up using JChem Base:Virtual and Vendor Neighbors James Baxendale, Ajay, Keana Scott, Lalit Verma, Noel Southall, Trung Nguyen

2. Problem – unintended consequences of HTS campaign success • Chemists overrun with HTS hits to follow up • Workflow (GUI support) • Visually identify SAR gaps • Simplify access to gap filling sources (vendors, ideas, literature) • Track progress in filling gaps • Plot results to confirm / deny hypothesis • Technical (Under the covers with JChem Base) • Capture and organize chemists follow up ideas • Live test of virtual compounds against model • Triage vendor compounds • Pipeline to ease housekeeping, substructure search all vendors and manage vendor updates • On-the-fly application of a large number of chemical and physical property filters • Insert versus retrieval speed tradeoffs

3. Workflow Solution – Visually Identify SAR Gaps • Immediately see where activity (and other interesting properties) are conserved • Zoom allows pattern detection for large results • Visually construct a hypothesis then fill gaps to test

4. Workflow Solution – Simplify access to gap filling sources Literature Compounds SDF SAR Matrix with Cells to fill highlighted and menu options shown VENDOR ASC results with exact Or near matches highlighted

5. Workflow Solution – Track gap filling progress • Share project level matrix overviews so everyone knows the status • Request to fill gaps remembered and filled at later time automatically • Overnight re-caching of results shows the latest results without the wait……… • Any new compounds, in-house synthesized, virtual or vendor? • Some that match existing gaps (or at least are close within some tolerance)? Vendor Available Screening Compounds SEURAT Job Database In House Synthesized Batch Exec of jobs Virtual Compounds

6. Workflow Solution – Plot results to confirm / deny hypothesis • Switch between scaffold specific SAR matrix and project level visualizations to determine whether further gap filling is warranted • Are potency and ADME / Tox getting better in recently synthesized compounds?

7. Technical Solution - Capture and organize chemists follow up ideas • Draw in from scratch, load an existing compound as a starting point or substructure search virtual compound space. • Use JChem Base UpdateHandler to fill database fields for managing / tracking owner, date and status. • Allow a view of synthesized and virtual side by side to aid idea generation

8. Technical Solution - Live test of virtual compounds against model • Hook into MarvinBeans “mol” property change event whenever molecule changes. • Sum each non null contribution from a SMARTS match in the molecule, replace earlier matches with later ones • Use JChem Base MolSearch.findAll to do SMARTS matching • Use published journal articles to glean the appropriate SMARTS, their order and their contribution to each properties calculations. • We are looking into addition of an logS computationamong others

9. Technical Solution - Triage Vendor Compounds • 18 vendors were evaluated based upon library size, cost, availability, purity, analytical data and plating options. • The collections from 8 vendors were further analyzed for duplicates: Test JChem CANSMI JChem CANSMI sufficient to use Drop vendors with lowest unique contribution

10. Technical Solution – Pipeline to ease housekeeping, substructure search all vendors and manage vendor updates • Pipeline to automatically process vendor updates upon SDF file arrival • Keep track of duplicates as aliases but load only a single compound so we have options when ordering Property Rules (10) Vendor SD Files Arrive On average a file of 100,000 compounds takes ~3 hours to be processed Calculate Physical Properties Normalize Names & Structures Multi-vendor DB Load support tables Using Hibernate AND Calculate Chemical Properties and Encode Bitmask Load JChem Base DB Using UpdateHandler Multi-vendor DB Chemistry Rules “REOS” (100)

11. Technical Solution – On-the-fly application of a large number of chemical and physical property filters • 1.7 million compounds from 6 vendors (1.2 non-redundant) • Main reasons for failing “drug-like” filters: • XLogP > 6.0 (213,308) • SSSR > 5 (45,363) • MW > 500 (126,346) • PSA > 160 (8217) • XLogP < -1.0 (4595) • Allows searches on any combination of: • Sub-structure across any vendor combination • 10 drug like property ranges (e.g. MW, XLogP, PSA…) • 100 REOS flag maximums (e.g. Cyanidine Derivatives, alkene, amide, …) • Unique id or vendor name • Allowable elementssoon

12. Technical Solution - On-the-fly application of a large number of chemical and physical property filters • Use positional bitmap for decent balance between insert and retrieval speeds otherwise an index would be needed on upward of 100 columns for decent performance (UTL_RAW for Oracle, PostgreSQL and others have equivalents) • Bitwise OR can then check for a pass or fail of a concatenation of all flags at once 1 = 0001 Cyanidine Derivatives < 3 AND Alkene < 1… 2 = 0011 3 = 0111 0111 || 0001 … 01110001… = .. = …… 16 = …… PASS IF BIT_OR(concatFlagsThisQuery, precomputedInDB) = concatFlagsThisQuery

13. Quick Demo & Contact Details • Matrix SAR as the dashboard to manage gap filling • Available Screening Compounds to show utility of on the fly queries even with a large number of chemical flags • Q & A • Visit out website for more detailed information about SEURAT • www.synapticscience.com • Pricing and sales information send a request to sales@synapticscience.com or call +1 301 915 0274 • SEURAT Components Available Separately • SAR Tool • Visualization plus generic spreadsheet • Available Screening Compounds (ASC) • Docking Viewer • Kinase Viewer and Kinome Tree Viewer • SMPC Tool (Hypothetical Compounds) • Other products • MCS and detached MCS • Patent space visualization SEURAT