Cheminformatics as Predictive Science

1. Cheminformatics as Predictive Science Alexander Tropsha Laboratory for Molecular Modeling School of Pharmacy and Carolina Center for Exploratory Cheminformatics Research UNC-Chapel Hill

2. Overview • Cheminformatics revolution • Predictive methods in cheminformatics: QSAR modeling and virtual screening • Extension of cheminformatics approaches to structure based virtual screening • chemical geometricaldescriptors of protein-ligand interface and scoring functions • Virtual docking in multidimensional chemical descriptor space: CoLiBRI. • Final thoughts

3. CHEMINFORMATICS REVOLUTION • Focus on Cheminformatics at recent international meetings (Obernai, EuroQSAR, Pune) • Name change of the QSAR and Modeling Society to CHEMINFORMATICS AND QSAR SOCIETY • NIH RoadMap; specifically, Molecular Libraries Roadmap: emphasis on exploratory and novel methodologies

4. Cheminformatics as an emerging scientific discipline • Factors defining a discipline: • Community of scientists speaking a common language; journals; meetings; educational programs • Unique set of problems • Unique description of the problem • Unique tools

5. Breadth of Cheminformatics Problems and Tools • Call for papers for the Fourth Joint Sheffield Conference on Chemoinformatics (June 2007) • High-Throughput Screening, including: assay quality control; design of screening collections; systems based design; • Virtual Screening including: docking and pharmacophore analysis,  similarity and clustering methods; machine learning; • Computational Methods for Lead Identification and Optimisation including: modelling and structure-activity methods; structure-based design; ADMET prediction • New Algorithms and Technologies including: data mining; searching methods; distributed processing; data handling and visualisation; • Case Histories, incorporating practical experience of any of the above

6. Unique formalization of the problem • Representation of chemical structures: notations (Smiles, InChi); bitstrings; 2D graphs; 3D conformations; molecular surfaces. • Descriptors, descriptors, descriptors… • Similarity metrics

7. Cheminformatics evolution: formalize, evaluate, predict! • Data handling (storage, manipulation, query, etc.) - IT informatics • Data exploration (trend analysis, explanatory modeling, model interpretation) – evaluative cheminformatics • Data exploitation and accurate (!) imputation – predictive cheminformatics

8. Challenges of Cheminformatics in the context of MLI • IT: storage, retrieval, access, LIM (MLSCN and Pubchem) • Data visualization • (Q)SAR analysis to discover activity-specific chemical patterns • Identification or design of bioactive compounds and compound libraries with high expected hit rate • ADMETox property prediction • Definition of the biologically important chemical diversity space as well as biologically inert diversity space.

9. Final Thoughts Nothing that worth knowing can be taught. Oscar Wilde • Best time ever to be a cheminformatics scholar • Growth of databases • Tool development • Collaborations with computational and experimental scientists • Extending cheminformatics approaches to new areas • Structure based virtual screening • “-omics” data analysis • Genotype - phenotype correlations • Focus on Knowledge Discovery (accurate testable predictions!) in Chemical Databases