Merck Virtual Library (MVL): Deployment, Application, and Future Enhancement

Transcription

1 Merck Virtual Library (MVL): Deployment, Application, and Future Enhancement Zhengwei Peng Informatics, Discovery Chemistry, Merck & Co., Inc., Kenilworth, NJ, USA, and ChemAxon UGM, Boston, MA, USA

2 Contents What is Merck Virtual Library? Learnings from its deployment Some applications Several enhancements Summary 2

3 What is Merck Virtual Library (MVL)? A virtual compound space constructed based on robust Rxns and available reactants Vast, fast, cheap, and dense with near neighbors for SAR exploration A molecular design feature for idea generation and lead hopping A reusable service component which can be called by any other applications Molecular design tools Register Rxn and scope and limitation ChemAxon search engine 3 ACD MBBC Rxn knowledge capturing and mining for suitable reactants MVL ( )

4 MVL Deployed as a Cloud-Based Service Web app Rich GUI app PLP protocols Web services VL upload VL deletion Searches REST-ful API Extended ChemAxon Markush Search Engine AWS private cloud 4

5 HW/SW Used for Cloud Deployment 16 vcpus 64 GB RAM 4 vcpus 16 GB RAM 100 GB table space (SSD) Oracle EE 11.2 Extended Markush Search Engine Web app container (Jetty) EC2 instance (RHEL 64 bit) Amazon Oracle RDS Dedicated RDS for good performance and easy maintenance Snap shots Snap shots Amazon EBS (200G SSD) Persistent file space for easy service recovery Admin tools used Amazon web console Oracle SQL developer PuTTY Monthly maintenance Log file cleanup Jetty restart Monthly charge for cloud deployment: ~$400 Very stable and easy to maintain (<1% FTE) Many thanks to Michael Braden for his great support! 5

6 Some Applications Target discovery Target validation High throughput screening Lead optimization Drug development IP registration and clinical trials Replenish and enrich HTS screening set HTS hit follow-up Fragment hit follow-up Lead OP and lead hopping De novo molecular design Provide MVL neighbors of active project compounds via a Details-on-Demand view in project SpotFire SAR viewer Help to lower the chemistry barriers in fragment hit follow-up Provide synthetic feasible alternatives for de novo targets designed by modelers 6

7 MVL Neighbors of Discovery Project Compounds for LO and Idea Generation MVL Neighbors of Discovery Project Compounds for LO and Idea Generation Cmpds of active projects (~10 5 ) Similarity search (80% cutoff) MVL (>10 10 ) MVL neighbors for active project cmpds (>10 5 ) + ADMET property prediction SpotFire SAR tables with real project cmpds Detail-on-demand Their MVL neighbors Rxn + BBs Predicted ADMET 7 chron job for daily incremental update 10%-20% discovery project cmpds have MVL neighbors This rate will increase as more Rxns are being added into MVL Fast lookup (~instantaneous) Easy for deployment and uptake

8 Support for Fragment Hit Follow-up Key info to help prioritize fragment screening hits: Binding mode ( X-ray or NMR experiments) Chemistry tractability (where MVL can help) Here is a hypothetical example: O N N N N Cl Fragment hit against human CA II enzyme K d ~700 mm Wood, et al. J Med Chem. 2016;59:

9 Multiple Ways Suggested for Hit Follow-up Using Robust Rxns and Available Building Blocks

10 Multiple Ways Suggested for Hit Follow-up Using Robust Rxns and Available Building Blocks N N + O N N + O N N N N sec search + 5 min browsing multiple ideas with Rxns + available reactants Each idea could be further elaborated using library design tools (+ ADMET property prediction, 3D docking/scoring, etc) Lower the chemistry barriers for fragment hit follow-up

11 Support for De Novo Compound Designs Support for De Novo Compound Designs Demonstration of synthetic feasibility improves your chance of convincing chemists to make your de novo designed molecules 11 Liu Q, Masek B, Smith K, Smith J. J Med Chem. 2007;50(22).

12 12 MVL Suggests Synthetic Feasible Analogs of De Novo Designed Target Molecules

13 Future Enhancement: 3D Shape-Based Similarity Search (via fast-rocs) 1) Asymmetric shape similarity scoring ~ sec 3D conformers of MVL basis products (~10 6 ) 2) Monomer focusing and subset enumeration ~ min 10 3 cmpds per subset ~100 individual virtual libraries 3) 3D conformer generation (Omega) 10 5 x 1 sec/molecule 30 hr Returned MVL hits similar in 3D shape to the query molecules 3D conformers of focused subsets (~10 5 ) Dynamically generated based on the query molecule 13 Current bottlenecks and possible solutions: 4 CPU: 8 hour turn-around time GPU-enabled Omega 100x speed-up 20 GPUs in parallel 20x speed-up The full workflow could be completed in minutes

14 Future Enhancement: Automated Molecular Design Within MVL Starting compound set Repeat Ending compound set GA-based molecule generator New compound set MPO scoring Select the top compounds MVL QSAR models for activity, selectivity, ADMET 3D info such as shape fit, pharmacophores, and even protein-ligand docking/scoring Nightly run, daily review and adjustment of the MPO scoring function used Stay within the synthetic feasible virtual space Schneider, et al. De novo design at the edge of chaos. J Med Chem. 2016;59: Besnard, et al. Automated design of ligands to polypharmacological profiles. Nature. 2012;492: Hartenfeller, et al. DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comp Bio. 2012;2:e

15 Summary Summary 15 MVL has been deployed in the AWS private cloud for a year Very stable with good performance Low maintenance (<1% FTE + $400/mo cloud charge) Applications in discovery projects are ramping up MVL Similarity Search tool MVL neighbors of project compounds in SpotFire SAR viewer Fragment hit follow-up lower the chemistry barriers Support for de novo compound designs by project modelers lower the chemistry barriers A few major enhancements were requested by project teams Enable shape-based similarity for more aggressive lead hopping Enable automated compound design within MVL Continue to add more Rxns into MVL

16 Acknowledgement Acknowledgements s ChemAxon collaborator: Michael Braden and Jon Patterson Merck colleagues The Rxn registration and tool enhancement: Sobhana Babu Boga, Derun Li, Rajan Anand Chris Culberson, Brad Sherborne, and Scott A. Johnson Discovery project teams: Xiao Li, John Sanders, Andrew Rusinko, Sookhee Ha, Zhuyan Guo, James Fells, Hongwu Wang, Deping Wang, Shu-Wei Yang, Mike Seganish, Zhi-Cai Shi IT support in cloud deployment: Steve Toback and Richard Matz Management support and funding: Meir Glick, Milana Maletic, Farida Kopti, Chris Waller, and Frank Brown 16

17 17 BACKUP SLIDES Backup Slides

18 Previous Work on Vast Virtual Chemical Spaces and Related Similarity Search Methods 18 Peng Z. Very large virtual compound spaces: construction, storage, and utility in drug discovery. Drug Discov Today Technol