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1 Schedule 8:30-9:30 Introduction 9:40- 10:45 Analysis Methods 10:55-12:00 Design and Analysis 12:00 Lunch 1:00-2:05 Design and Analysis I (Will and Stan)

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Presentation on theme: "1 Schedule 8:30-9:30 Introduction 9:40- 10:45 Analysis Methods 10:55-12:00 Design and Analysis 12:00 Lunch 1:00-2:05 Design and Analysis I (Will and Stan)"— Presentation transcript:

1 1 Schedule 8:30-9:30 Introduction 9:40- 10:45 Analysis Methods 10:55-12:00 Design and Analysis 12:00 Lunch 1:00-2:05 Design and Analysis I (Will and Stan) 2:15-3:15 Pooling 3:25-4:00 Pooling Discussion 4:00-4:30 General Discussion

2 2 HTS Directions NISS Affiliates Workshop 25 October 2002

3 3 Statistical Analysis Corporate Collection Commercial Compounds Virtual Compounds Statistical Model Selected Compounds Initial Compounds Active Compounds Filters Screen Compounds Stat Methods Chem Descriptors Model Data Sets Sequential Screening Paradigm

4 4 Data / Descriptors / Stat Method Data : Protein binding Cell-based Whole Animal, e.g. Toxicology Descriptors : BCUT, Atom Type Counts, Topological, 3D, etc. Analysis : LR, PLS, RP, NN, Latent Class, SVM, etc. Combined (e.g. RP and LR)

5 5 Data Sets – Need for benchmarking data sets Binding : NCI Cell-based: none Tox : Mutagenicity, others Pooled : none

6 6 Software Needs Compound viewer/editor: MolViewer : JMP/smiles Descriptor Calculations: Dragon? Environment to code new algorithms: R, MatLab, MOE Standard stat methods: SAS, S, R, JMP Special Data Mining Code : ChemTree, CompChem vendors LeadScope, BioReason, etc.

7 7 Statistical Analysis Corporate Collection Commercial Compounds Virtual Compounds Statistical Model Selected Compounds Initial Compounds Active Compounds Filters Screen Compounds Stat Methods Chem Descriptors Model Data Sets Sequential Screening Paradigm

8 8 Initial Sample 1. Focused, knowledge-based 2. No knowledge => Diverse 3. Redundant to support analysis 4. Large enough to start process 5. Random is generally OK!

9 9 Rational Screening Goals 1. Optimize bioactivity, (increase selectivity) 2. Reduce the cost of experimental data 3. Increase the number of “active” classes 4. Increase speed of knowledge acquisition

10 10 Irrational Screening Goals 1. Find all active compounds. 2. Cost is no object. 3. Time is no object. 4. Find every active class.

11 11 Find Multiple Chemical Classes

12 12 Literature DM Hawkins, SS Young and A Rusinko. “Analysis of a Large Structure-Activity Data Set Using Recursive Partitioning” QSAR 16: (1997). MJ Valler and D Green. “Diversity screening versus focussed screening in drug discovery” Drug Discovery Today 5: (2001). MFM Engels and P Venkatarangan. “Smart screening: Approaches to efficient HTS” Current Opinions in Drug Discovery&Development 4: (2001)


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