molecular data mining tool advances in hiv
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artificial neural networks (ANN) to relatestructure to function. The ability to analyzemolecular structure and predict effectivenesshelps Dr. Danter look for existing drugs tobattle diseases like HIV, as well as to developpotential new medications. Analyzingchemical structures, CHEMSAS™ utilizeshybrid ANN systems to predict the in vitroresponse of HIV1 to potential anti-viraldrugs.
The results to date are impressive. In arecent study conducted by Dr. Danter, heanalyzed 311 drugs with known in vitroactivity against the HIV1 virus. The systemcorrectly classified more than 96% of themolecules.
One of the great strengths of a data-mining tool like CART is its ability to pickout the significant variables – even when theyare hidden among hundreds or thousands ofirrelevant variables. It also clearly identifiescomplex interactions among study variables,and permits Dr. Danter to obtain moreaccurate results in minutes – rather than days.
Mining In Other AreasDuring the past several months, Dr.
Danter has also used CART in developingmodels to study central nervous systemreceptors, anti-arthritic medications, andantibiotics, among others. As an artificialintelligence tool, CART’s role in predictingspecific biological activity continues to bevital to his research at Critical Outcome, Inc.To view detailed study results and modelingprocedures, review their research atwww.critical outcome.com.
Richard Burnham can be reached at (651) 773-0619 or [email protected]
Dr. Wayne Danter, MD, FRCPC is an Associate Professor ofMedicine and Director, LRI Neural Computing Lab at theUniversity of Western Ontario London Ontario, Canada andcan be reached at (519) 851-0035 [email protected]
Salford Systems (www.salford-systems.com) can be reachedat (619) 543-8880 or [email protected]
Figure 2: The overtrained maximaltree has a relative error rate of .505(red line); the optimal tree relativeerror is .435 (green line). Thehighlighted nodes on the left of treecontribute least to performance andwill be the first to be pruned away.
Figure 1: The optimal CART tree. Rednodes contain greatest concentrationof the “High Risk” group and bluenodes concentrate the “Low RiskGroup.” Hovering the mouse over anode displays its contents.
Molecular Data Mining Tool:Advances In HIV Research
Pruning Decision TreesUpon creating the structure, the system
prunes back the tree and uses a self-testprocedure to ensure that the model is notover-fitting — that is, finding patterns thatapply only to training data. This produces asmaller, optimal-sized tree. The tree’sterminal nodes become the model used forthe remainder of the research process.
A list of important variables isautomatically produced and is used todevelop the model, ranked by importance.This is crucial because many of the variablesturn out to be relatively unimportant. “Youmay have a couple of hundred inputvariables, but a subgroup of those variablesare the most important ones and the onlyones we really need to use,” says Dr. Danter.Using all the variables throughout theanalysis would make the process needlesslycumbersome — possibly skewing the results.
To satisfy Dr. Danter’s specializedmodeling needs in his HIV research, heinputs the results into another SalfordSystems product, MARS® (MultivariateAdaptive Regression Splines), then into aneural network program from Ward SystemsGroup, NeuroShell® Classifier. MARS is anon-parametric regression procedure thatextends Dr. Danter’s work by improving theaccuracy of predictions. NeuroShell®Classifier then categorizes a molecule’sactivity based on patterns derived fromCART and MARS.
Honing The DataThe results are honed to specific
research needs using a proprietary algorithmDr. Danter developed called CHEMSAS™.This process decomposes complex molecularstructures into key elements, teaching
Pharmaceutical companies may haveas many as a million molecules in theirdatabases. Modeling each molecule andpredicting its effectiveness using standardstatistical methods is virtually impossiblebecause of the enormous number ofvariables. Dr. Danter uses CART®(Classification and Regression Trees), asoftware package from Salford Systems tohelp build models that isolate the mostimportant variables. Working with publicdomain, molecular HIV data, Dantertrains CART and complementary systemsto predict if a given molecular structure isbiologically active against a disease. SaysDr. Danter, “Once we have such a model,we can screen almost any molecule with amolecular weight up to 1700 daltons (anatomic mass unit). It’s an area calledmolecular mining. We’ve developed it as ageneric tool, so that if there is a specifictarget biological activity, we can screen forit.”
To build a model, CART generates abinary decision tree based on yes/noanswers. It generates nodes until it hascreated the largest tree that fits the data.This ensures that the node-generatingprocess is not halted too soon andimportant structures are not overlooked.
Figure 3: Summary reports include avariable importance ranking, gains andlift charts and tables, misclassificationreports, and an overall summary of alltrees grown in a session.
The ability to predict biological activitybased on molecular structure is leadingresearchers to breakthroughs in the most
complex challenges of medicine. Using acombination of artificial intelligence tools,Dr. Wayne Danter of Critical OutcomeTechnologies (London, Ontario, Canada)has developed a method to predict whetherspecific molecular structures are effectiveagainst a disease. Currently under study isthe HIV1 virus.