The document discusses a structured approach for traditional drug discovery, focusing on the in silico prediction of ADME properties and the optimization of lead compounds using virtual screening and predictive modeling. It highlights the goal of identifying active compounds through various scoring functions while also addressing challenges in classification accuracy and descriptor selection for improved predictions. Additionally, it mentions the collaborative efforts of multiple experts across chemistry and pharmacology in this research.

1. Donovan N. Chin & R. Aldrin Denny

2.  Traditional Drug Discovery (insert graph)
 In Silico Prediction of ADME (insert graph)
◦ Potency
◦ Absorption
◦ Lead
◦ Drug
◦ Toxicity
◦ Excretion
◦ Metabolism
◦ distribution

3.  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

4.  (insert graph)
◦ Potency
◦ Lead
◦ Drug
◦ Toxicity
◦ Excretion
◦ Metabolism
◦ Distribution
◦ absorption

5.  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

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

7.  (insert graph)
 After filtering by Pharmacophore Feature

8.  (insert graph)

9.  (insert functions for)
◦ F_Score*
◦ D_Score
◦ G_Score
◦ PMF_Score
◦ Chem_Score
◦ ICM_Score*

10.  Cell Adhesion Assay (50% Serum)
◦ (insert graph)
 Biochemical Adhesion Assay
◦ (insert graph)
 Scoring Functions Are Poor More Often Than
Not

11.  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

12.  (insert graph)

13.  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

14.  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)

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

16.  (insert graph)
 Significant Improvement in Finding Hits Using
New SF

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

18.  (insert cluster)
 Able to identify different molecular property
criteria that lead to hits

19.  (insert graph)

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

21.  (insert graph)
 Choose 1 & 2D Descriptors for ease of
interpretation and lower “noise”

22.  Build Model (insert graphs) Apply Model

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

24.  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)

25.  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