Donovan N. Chin & R. Aldrin Denny Traditional Drug Discovery - - PowerPoint PPT Presentation

Donovan N. Chin & R. Aldrin Denny

 Traditional Drug Discovery (insert graph)  In Silico Prediction of ADME (insert graph)

• Potency
• Absorption
• Lead
• Drug
• Toxicity
• Excretion
• Metabolism
• distribution

 Target IVY(Brute force virtual screening of

very large compound libraries) Lead Discovery IVY(Utilize predictive models from Biogen data for more efficient virtual screening) Lead Optimization candidate

 (insert graph)

• Potency
• Lead
• Drug
• Toxicity
• Excretion
• Metabolism
• Distribution
• absorption

 Goal: Identify crystallographic binding mode,

Rank order ligands wrt binding with protein

 (insert graph)  Receptor Docking  Ligand Shape  Generate plausible trial binding modes using

docking function then Re-rank modes with scoring function

 (insert graph)  341 Active  47 Non-Active

 (insert graph)  After filtering by Pharmacophore Feature

 (insert graph)

 (insert functions for)

• F_Score*
• D_Score
• G_Score
• PMF_Score
• Chem_Score
• ICM_Score*

 Cell Adhesion Assay (50% Serum)

• (insert graph)

 Biochemical Adhesion Assay

• (insert graph)

 Scoring Functions Are Poor More Often Than

Not

 Receptor Site View Library Design FlexX

Score Consensus Score>=3 e.g. Contact Map, CLogP MW, HBOND Rotatable bonds Consensus=5? if yes, substructure exists? if yes, Pharmacophore<4.2Å? if yes, Publish Hit Report

 (insert graph)

 Goal: Predict hit/miss class based on presence of features

(fingerprints)

 Method

• Given a set of N samples
• Given that some subset A of them are good („active‟)

 Then we estimate for a new compound: P(good)~ A/N

• Given a set of binary features F

 For a given feature F:

 It appears in N samples  It appears in A good samples

 Can we estimate: P(good l F)~A/N

 (Problem: Error gets worse as Nsmall)

• P‟(good l F)= (A+P(good)k)/(n+k)

 P‟(good l F)p(good)as N0  P‟(good l F) A/N as N large

• (If K=1/P(good) this is the Laplacian correction)

 Descriptors (insert)  Advantages

• Can describe huge number of features (up to 4 billion; MDL 1024; Lead

scope 27,000)

• Contains tertiary and stereochemistry information
• Fast

 Classification Analysis

• Developing Non-Linear Scoring Functions to classify

actives and non-actives

• (insert graphs)
• Cost Function to Minimize: Gini Impurity N= 1-

ΣP^2(ω)

 Training Set Prediction Success  (insert table)  10-fold cross validation  Randomly split training and test sets  Significant Improvement in Separating Actives

from Non-Actives

 (insert graph)  Significant Improvement in Finding Hits Using

New SF

 Optimal tree identified (insert graph)  No random effects (insert graph)

 (insert cluster)  Able to identify different molecular property

criteria that lead to hits

 (insert graph)

 (insert graph)  Size= magnitude of OBA  OBA values cover range of descriptor space

 (insert graph)  Choose 1 & 2D Descriptors for ease of

interpretation and lower “noise”

 Build Model (insert graphs) Apply Model

 Features found in high OBA  Features found in low OBA  Would be nice if CART did similar view

 Improved scoring functions for separating

hits from non-hits in structure-based drug design developed with CART and Bayesian models

 Identified key differences in molecular

physical properties that led to hits

 Built reasonably predictive OBA model

(cannot expect method to extend to other systems given complexity of OBA, however)

 Biogen IDEC  Modeling

• Rajiah Denny
• Claudio Chuaqui
• Juswinder Singh
• Herman van Vlijmen
• Norman Wang
• Anuj Patel
• Zhan Deng

 Chemistry

• Kevin Guckian
• Dan Scott
• Thomas Durand-Reville
• Pat Conlon
• Charlie Hammond
• Chuck Jewell

 Pharmacology

• Tonika Bonhert