Structure-based virtual screening is an important tool in the drug discovery process. The use of computational tools has allowed for the screening of large libraries of chemical compounds to identify putative ligand-receptor interactions. The identification of valid targets and therapeutic compounds has long-term importance both to public health and the economic strength of the pharmaceutical industry. Receptor-based virtual screening (VS) is a technique in which computational tools are used dock small molecular weight compounds into a protein receptor or enzyme. This technique is most often used in drug discovery, where a large library of chemical structure can be docked and scored to assess the potential if a compound to bind to a drug target. However, high-throughput virtual screening is computationally intensive, and the cost of building, maintaining, and managing a dedicated computing cluster limits access to these technologies to large universities and commercial enterprises. Internet-based, or “cloud” computing, is a business service model in which computational resources are accessed affordably, scalably, and securely as needed. Our product utilizes this cloud infrastructure to deliver virtual screening to clients who either don’t desire to or cannot maintain their own infrastructure. Our elegant and highly efficient system for managing the job queue and maximizing the efficient use of computational resources allows us to provide reduced-cost access to our tools for academic and government researchers. This confluence of residual processing power and need has given rise to our concept of the “bucket list”; a “free” job queue that unassigned agents can perform during the time between finishing a paid job and their “death” at the end of their provisioned hour. We are working with Chemaxon to expand the capabilities of the current system through the following technical achievements: (1) integration of additional chemical libraries and library filtering tools to focus search space prior to docking; (2) enhancement of end user ability to evaluate results through integration of data analysis and visualization tools; (3) integration of additional licensed, proprietary, and public domain tools for additional functionality. This work is funded by NIH’s National Institute of General Medical Science through SBIR Phase II grant GM097902

1. DEVELOPMENT OF THE
CHEMALYTICS PLATFORM
FOR DRUG DISCOVERY
Gerald J. Wyckoff, UMKC

2. What drives our research?
 The pharmaceutical industry is facing spiraling drug development costs
while R&D productivity remains stalled
 6 of the 10 highest-grossing branded products will or have lost patent
exclusivity this year (2014)
 Reuters notes that the industry spent $65 billion on drug R&D in the U.S. in
2009, but approval rates have sunk 44% over the past 13 years

3. Background
 Importance of identifying valid targets and therapeutic compounds
 Tools currently in use:
 Structure-based virtual screening
 Receptor-based virtual screening
 Other computational tools
 Drawbacks to current implementation of high-throughput virtual screening:
 Computationally intensive
 Limited access due to high cost of infrastructure
 Solution:
 Virtual screening in the cloud
 Provides computational resources scalably and only when needed

4. Maslow’s Hammer

5. Chemalytics Platform
 Utilizes cloud infrastructure to deliver virtual screening to clients
who either don’t desire to or cannot afford to maintain their
own infrastructure
 Highly efficient system for managing job queuing and
maximizing the efficient use of computational resources allows
us to provide reduced-cost access to our tools for academic and
government researchers

6. “Bucket List” Job Queueing Model
 Residual processing power in cloud
 Need for low-to-no cost solutions for
academic researchers
 Solution: Bucket List model for
job queueing allows unassigned
agents to perform lower-priority
jobs after finishing a paid job
and before “death” at the end
of the provisioned hour

7. Our Goals in Working with Chemaxon
 Integration of additional chemical libraries and library filtering
tools to focus search space prior to docking
 Enhancement of end-user ability to evaluate results through
integration of data analysis and visualization tools
 Integration of additional licensed, proprietary, and public
domain tools

8. Outcome:
Products for Three Stages of Drug Discovery
Lead Generation
5 years
Candidate
Identification
Lead
Identification
Target
Validation
Target
Identification
Preclinical
Candidate
Identification
PhI / IIa PhIIb PhIII Register
Lead Optimization
3 years
Product Realization
4.5 years
Fingerprinting
Modeling/Docking
Repurposing

9. Combined Workflow – Chemalytics & Zorilla

10. Combined Workflow - continued

11. Zorilla Research, Re-purposing

12. Zorilla Research, Re-purposing

13. Front-End Requirements
 Spreadsheet-like viewing of compounds
Item Description Source Status
A. Structure identifier. MySQL autogenerated
B. Vina Binding energy for mode 0
structure.
S3 Project
generated
C. Ligand Efficiency -calculated data
from number of heavy atoms in
ligand and (B.) MySQL calculated
D. Physico-chemical properties
calculated by JChem
database precalculated
E. Chemical structure (MarvinView?) MySQL calculated

14. Visualization Requirements
 All numerical fields sortable
 Data export as a spreadsheet
 Mechanism for 3D view of docked structure
 Drill-down for ordering requirements (integration with vendors)

15. 3D Visualization Requirements
 Using Jmol
 Typical Visualizations
contact connect for 1m14, showing hydrogen
bonds between amino acid residues of a single
chain.

16. Why We’re Working With Chemaxon
 Integration of Marvin and search tools in the web front-end
 Consistent nomenclature of all library items
 Automated processing of libraries

17. Future Goals
 Build integrated suite of tools (including Zorilla applications)
 Improve ancestral protein prediction in phylogenetic analysis
 Answer fundamental evolutionary questions relating to
structure/function

18. For Further Information, contact: wyckoffg@umkc.edu
Acknowledgments
 The Wyckoff Lab
 Lee Likins, Scott Foy, Ming Yang
 Ada Solidar (B-tech Consulting)
 HaRo Pharmaceuticals
 Tomasz Skorski (Temple University)
 The Miziorko Lab (UMKC)
 John VanNice
 Andrew Skaff
 Jeff Murphy (Nickel City Software)
 Brian Geisbrecht (K-State)
 And his lab
 John Walker (SLU)
 NIH 1 R41 GM 088922-01A1
 NIH 2 R44 GM097902-02A1
 NIH 1 R21 AI113552-01
 VaSSA Informatics, LLC for major
funding
 Digital Sandbox KC
 Missouri Technology Corporation
 UMKC SBS, UMRB, UMKC FRG,
KCALSI for additional funding