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High Throughput Experimentation: Computational Requirements

High Throughput Experimentation: Computational Requirements. John M. Newsam Molecular Simulations Inc. (A Pharmacopeia subsidiary). “Workshop on Combinatorial Methods for Materials Discovery” ATP Fall National Meeting Atlanta, GA Wednesday November 18th 1998. Potential Hindrances?.

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High Throughput Experimentation: Computational Requirements

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  1. High Throughput Experimentation: Computational Requirements John M. Newsam Molecular Simulations Inc. (A Pharmacopeia subsidiary) “Workshop on Combinatorial Methods for Materials Discovery” ATP Fall National Meeting Atlanta, GA Wednesday November 18th 1998

  2. Potential Hindrances? • Patent profusion • vigilance • Unmet expectations • set reasonably • Infrastructure cost • hindrance for academics • Lack of standards • premature for hardware • Inertia • resistance to change, short-term delivery focus

  3. Synthesis Analytical Characterization of composition, purity, phases, structure QSAR# Lead compounds for resynthesis and secondary testing High-throughput Experimentation Library Design Primary Testing Pooled, parallel or discrete Processing Physical, mechanical etc. processing Performance in specific application Testing requirements drive synthesis format #Quantitative Structure-Activity relationships

  4. Infrastructure Needs • Vertical and horizontal integration • Adaptable • Modular • Geared for huge throughput • Broadly deployable

  5. Engineering Solution New 1536 well HTS Format • 1536 wells, 2 l well volume • Corning Science Products joint design • Automated 961536 reformatter • l-level fluids dispensing • Oxidative and evaporative loss reduced

  6. User Input & Workstation Interfaces Data Base Engines Analysis, Display and Data Access Chemistry & Materials Input Server-based Processing Oracle Molecular Simulation Workstation & Oracle Forms Display Materials Specific Tables Materials Algorithms Statistics Process and Data Management

  7. Luminescence data for a library of mixed metal oxides under 254nm UV irradiation Data from E.Danielson et al., Science 279 (1998) 831

  8. Some Specific Technology Needs • Hits vs misses; improvement criteria • Descriptors • Experiment decision support • Abstracted feature models (AFMs) • Process optimization • Simulation for scale-up • Sensor data (unravelling response of arrays)

  9. Which experiments should be done ? Computation Solution ‘Soft materials’ ‘Hard materials’ R1 R4 M1 + Temp M2 R3 X Scaffold R2 Making it practical: computation • 100 R1, 100 R2, 100 R3, 100 R4108 • 50,000 compounds/week40 years • How do we manage the process ? • What knowledge do the experiments yield ?

  10. Computation Solution Compound library design • Library Specification • Molecular: Product or Reaction-based • Polymers, Heterogeneous catalysts ? • Library Design • Diversity and similarity metrics • Similarity Selection • Array and mixture design • Library Comparison • Library Focussing • Active site model (atomic or abstracted) • QSAR Model World Drug Index of 35,873 compounds in a space of principal components C2.Diversity C2.LibCompare C2.LibSelect

  11. Abstracted Feature Models • Abstraction of key features • Based on activity data • Interesting ‘active’ definition R.C.Willson

  12. Computation Solution Descriptors Descriptor Families Descriptors - calculable molecular attributes that govern particular macroscopic properties Topological Fragments Receptor surface Structural Information-content Spatial Electronic Thermodynamic Conformational Quantum mechanical Products C2.Descriptor+ C2.MFA C2.QSAR+ C2.Synthia Plus Molecular and Quantum Methods

  13. Available, occupiable volume & framework density descriptors (104 zeolite and zeolite-related framework types) Correlative methods in catalyst design: Expert systems, neural networks and structure-activity relationships, in “Advances in Catalyst Design” Catalyst Advance Program (CAP) Report, The Catalyst Group, PA; in press (1998)

  14. Computation Solution Structure-Activity Relationships Properties Descriptors Correlative Methods Statistical Models Linear regression Stepwise & multiple linear regression Principal components analysis Partial least squares Genetic algorithm Genetic function approximation Products C2.QSAR+ C2.GA E.g. K.F. Moschner and A. Cece, “Development of a General QSAR for Predicting Octanol-Water Partition Coefficients and its Application to Surfactants,” ASTM STP 1218 (1995); MSI C2 QSAR manual April 1997.

  15. Organics Oil Field Corrosion Inhibitors • Benzimidazolines function at cathodic sites • Library studied by Kuron et al. (1985) • Key descriptors • Terminal N charge • 3-substituted N charge • Octanol-water logP • Moment of inertia H. Gråfen et al., Werkstoff und Korrosion, Vol. 36, 407 (1985) M.Doyle

  16. Conclusion • Computational infrastructure needs • Specific technology needs • Role of computation • process management system • experiment decision support • data visualization and analysis • knowledge from the experimental data • Integration

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