Fuzzy Logic Interval Clustering for Drug Discovery PREDICTION - - PowerPoint PPT Presentation

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Jan 05, 2023 490 likes •558 views

Fuzzy Logic Interval Clustering for Drug Discovery PREDICTION ACCURACY FOR DRUG DISCOVERY PROBLEM Hundreds of Test EACH thousands of compoundon High throughput screening compoundsare the desired (HTS) is expensive and time-

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Fuzzy Logic Interval Clustering for Drug Discovery

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PREDICTION ACCURACY FOR DRUG DISCOVERY

PROBLEM

– High throughput screening (HTS) is expensive and time- consuming – Artificial Neural Network (ANN) can detect patterns, but prediction accuracy is low

SOLUTION

– KU invention integrates Fuzzy Logic and Interval Clustering (FLIC) to the existing ANN operation – Higher prediction accuracy of Active Compounds = cheaper, faster process

Hundreds of thousands of compoundsare available Test EACH compoundon the desired protein Does the compound bind with the protein Non-Active Active

No Yes

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HOW IT WORKS

ANNs simulate the structure and function of a brain

using interconnected nodes – Trained using known (labeled) data

Fuzzy Logic applies confidence levels, rather than

a binary true/false classification

Interval Clustering groups items based on

“membership values”

KU innovation: FLIC is used to train and validate

many ANNs and the best performing ones are chosen to increase efficiency and accuracy – Clustering is used during training – The most accurate ANNs are chosen during validation to process the unknown data – During testing, each ANN calculates a probability value that indicates how “active” or how “inactive” each compound is

The higher the number of ANNs that

identify a compound as “active”, the higher the chances it is actually active. Clustering Basic ANN Structure

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FEATURES AND BENEFITS

FEATURES

– FLIC utilizes “fuzzy logic and interval clustering”

Improves overall performance and efficiency in predicting active

compounds

Identifies existence of misclassification in training data

– Easily integrated into available drug discovery process – Patent pending (2015) – Proof of concept completed using real datasets from KU HTS lab

50x improvement over ANNs alone
BENEFITS

– Identifies active compounds at a low number of misclassifications – Moves the process from the wet lab to a dry lab – Reduces cost and provides faster results

Method could be applied to other types of large datasets for pattern

recognition (facial or speech recognition, medical diagnosis)

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CONTACT INFORMATION David Richart, JD

Licensing Associate KU Center for Technology Commercialization University of Kansas drichart@ku.edu (785) 864-0124

Read More: http://kuic.ku.edu/available-technologies/high-throughput-screening-HTS-accurate-lower-cost-faster-results