advances in high-throughput electron density-derived ：在高吞吐量电子密度衍生的进展

,Molecular Informatics and MQSPR Curt BrenemanEastman Chemical Company, Kingsport, TN April 5, 2011,WISDOM,DATA,INFORMATION,UNDERSTANDING,KNOWLEDGE,The Knowledge Discovery Process,The Evolution of Informatics,4000 BCExperiments, observations, records,The Evolution of Informatics,4000 BCExperiment1700 ADTheory, formalism, publication,The Evolution of Informatics,4000 BCExperiment1700 ADTheory1950+Computation first steps,The Evolution of Informatics,4000 BCExperiment1700 ADTheory1950+Computation1970+Simulation,The Evolution of Informatics & MQSPR,4000 BCExperiment1700 ADTheory1950+Computation1970+Simulation1990+Cheminformatics&Data Mining,Traditional Domain of MQSPR ,STRUCTURE,PROPERTY,MOLECULAR DESCRIPTORREPRESENTATION,Statistical or Pattern Recognition Methods,ComputationalChemistry,X,Predictive Cheminformatics Workflow,Quantitative Structure-Property Relationships (MQSPR),Find hidden information in chemical dataRetrieve structures with a well-defined property range from a databaseIdentify of appropriate descriptor types for each situationAssess model applicability domainsValidate modeling results,QSPR Prediction of Chemical Behavior,Datasets, Information and Descriptors,Modeling and Mining Methods,Validation Methods,STRUCTURE,PROPERTY,MOLECULAR DESCRIPTORREPRESENTATION,Statistical or Pattern Recognition Methods,ComputationalChemistry,X,Predictive Cheminformatics Workflow,Descriptors: Not just for drug design anymore,We need to learn the fundamental language of chemistry“There are just too many descriptors out there” (Anonymous)Feature SelectionMay be hazardous to your modeluse at your own risk!,What is “Molecular Structure”?,AAACCTCATAGGAAGCATACCAGGAATTACATCA,Representing Molecular Structure,Descriptors,Model,Activity,MolecularStructures,Descriptors: Characterizing Molecules,Descriptors: Characterizing Molecules,No particular class of descriptors address all problemsMay be chosen to be problem specificMay be chosen to be method specific,Surface Property Distribution Descriptors,Molecular surface property distributions can be represented as RECON/TAE histogram bin descriptors,(RECON/TAE) Descriptors,PEST (Property-Encoded Surface Translation)Adds shape information that encode the spatial relationships of surface properties,PEST: Molecular Shape/Property Hybrid Descriptors,Curt M. Breneman, C. Matthew Sundling, N. Sukumar, Lingling Shen, William P. Katt and Mark J. Embrechts, “New developments in PEST shape/property hybrid descriptors” J. Computer-Aided Mol. Design, 17, 231240, (2003),Descriptors,Model,Activity,Molecular Structures,What features of a molecule are related to a molecular property of interest? What descriptors can capture that information?,Descriptor Selection,Predictive Cheminformatics: MQSPR,Learning from the past to predict the future,Descriptors,Model,Activity,MolecularStructures,=,Modeling Chemical Properties,Datasets: Where knowledge is stored,Size of the Dataset is important (Gedeck, 2006.)Quality of the dataset is critical (Eva Gottmann, et.al. 2001) Single protocols of data acquisition are more reliable.Be aware of data compilations; different labs, different assays.Interpretation of outliers in identification of mechanism (Cronin, 2003.)Found small and specifically reactive molecules had increased toxicity than reported by QSPRErrors inherent in the datasetExperimental errorDescriptor noiseModeling method should match descriptors and dataset:Data Truncation Analysis & Rank-Order Entropy (ROE),Data Fusion: Leveraging Strengths,FUSED DATA,Domain expert molecularunderstanding,Database #1,Database #n,Knowledge Discovery and Data Fusion,Model Parsimony Rules,Simple models are betterInterpretable models are betterReality: need to balance predictive ability and interpretability,“First there are the known knowns”These are the things that we know we know“Then there are the known unknowns”These are the things that we now know we do not know“Finally there are also the unknown unknowns”These are the things that we do not yet know we do not know“And each day brings us a few more unknown unknowns”Donald Rumsfeld, 2003,Challenges in Predictive Modeling,“Who wants to hear actors talk?” H.M. Warner, 1927“Forget it no civil war picture ever made a nickel” MGM executive, in 1937, advising against production of “Gone with the Wind”“I think there might be a market for maybe five computers” Thomas Watson, IBM, 1943“Computers in the future may weigh no more than 1.5 tons” Popular Mechanics, 1949“There is no reason anyone would want a computer in their home” Ken Olsen, founder of Digital Equipment Corporation, 1977,Famous Prediction Pitfalls,Machine Learning Methods and Statistical Modeling,“If your experiment needs statistics, you ought to have done a better experiment”- Ernest Rutherford,“But what if you havent done the experiment yet?”- Curt Breneman,MQSPR Model Building and Validation,Training Set,Dataset,Training Set(70%),Testing Set(30%),Training Set,90%,Subset,Testing Set(30%),Testing Set(30%),Training Set,Training Set,Training Set,Training Set,Training Set,Training Set,Training Set,Dataset Truncation Analysis,Testing MQSPR Models by Dataset Truncation,BP,Arteminsin,ACE,AChE,Testing Descriptors by Dataset Truncation,Data Truncation Analysis provides:A measure of regression model stabilityA way of easily determining the applicability of descriptors and modeling methods for a given problem domainRank Order EntropyA means for evaluating the rank-order stability of a model,Dataset Truncation and ROE,Model Applicability Domains,Toxicity Risk Assessment,Chemocentric view of biological data,Slide courtesy of Dr. Ann Richard (EPA),Database,Alerts,MQSPR models,Wrong Properties,Non-toxic,Molecular prioritization using an ensemble of MQSPR models, 760 kNN QSAR models,10 Best models,4334 hits,50 consensus (common) hits,9 compounds selected based on synthetic considerations,Acceptancecriteria,SimilarityCutoff,Mining DBs using Probes,Predictions with10 QSAR models using applicability domain,NIH testing,Predictive MQSPR Workflow example,48 anticonvulsants *,Ca. 255,000 chemicals in DBs,22 compounds submitted to chemists,7 compounds active,*Shen, M., et al. J. Med Chem., 2002, 45, 2811-2823; Shen, M., et al 2004, 47, 2356-2364.,RECCR Modeling Applications,Descriptor Calculation, Model Building, Validation and Analysis,ROMS Online Method Analysis Tool,ROMS Online Prediction Tool,ROMS Predictions,Rank Order Entropy Analysis,Some Features of Predictive Models,All descriptors that are used in the model are significantNone of the descriptors account for single peculiarities No leverage or outlier compounds in the training set(Gisbert, 2006.)Cross-validation performance should show:Significantly better performance than that of randomized tests Training set and external test set homogeneity. Models are stable to changes in dataset and model parametersLow Rank Order EntropyFavorable Dataset Truncation Analysis results,Best Practices in MQSPR,Data Sets Problems: Inadequate compilation of data, outliers, size of samples Solutions: Well-standardized assays, clear and unambiguous endpointsDescriptors Problems: Collinearity, Interpretability, error in data, too many variables Solutions: Domain knowledge, combined descriptors, feature selectionStatistical MethodsProblems: Overfitting of data, non-linearity, low interpretabilitySolutions: Simple models using validation,Development of MQSPRs is more of an art than a science,- Mark T.D. Cronin and T. Wayne Schultz,The Eight Commandments of QSPR/QSAR Modeling,There should be a PLAUSIBLE (not necessarily known or well understood) mechanism or connection between the descriptors and response. Otherwise we could be doing numerology,Robustness: you cannot keep tweaking parameters until you find one that works just right for a particular problem or dataset and then apply it to another. A generalizable model should be applicable across a broad range of parameter space.,Know the domain of applicability of the model and stay within it. What is sauce for the goose is sauce for the gander, but not necessarily for the alligator.,Likewise, know the error bars of your data.,The Eight Commandments of QSPR/QSAR Modeling,No cheating. no looking at the answer. This is the minimum requirement for developing a predictive model or hypothesis,Not all datasets contain a useful QSAR/QSPR “signal”. Dont look too hard for something that isnt there,Consider the use of “filters” to scale and then remove correlated, invariant and “noise” descriptors from the data, and to remove outliers from consideration.,Use your head and try to understand the chemistry of the problem that you are working on modeling is meant to assist human intelligence not to replace it,RECCR Cheminformatics Center,Descriptor CoreHTS Descriptors*Structure-based designLigand-based designMolecular SimilarityDescriptor benchmarks,Software Engineering and DisseminationAlgorithm Implementation *Computing VisualizationDatabase DevelopmentUser InterfaceSupport and Documentation,Modeling CoreAlternate Model Fusion*Task-targeted modelingMulti-objective LearningApplicability DomainsModel benchmarks,Data Management CoreCuration and Standardization *Data CollectionDatabase ManagementModel Implementation,Mfold (Mike Zuker)RNA, DNA secondary structure predictionAnalyze (Mark Embrechts)Fast KPLS test set mode with low memory footprintRECON Transferable Atom Equivalent descriptorsRECON for MOEDrop-in interactive for MOE 2007PROTEIN RECON for protein characterizationProperty moment descriptorsCOLIBRI (with Alex Tropsha)Binding site/ligand scoring using Universal Descriptor SpaceDIXELDNA Characterization and bioinformaticsPES