Guest lecture for Jean Claude Bradleys class at Drexel University

1. Molecular Representa/on,
Similarity and Search
Rajarshi Guha
NIH Chemical Genomics Center
December 3rd, 2009

2. Outline
• How can we represent molecules on a
computer?
• How do we decide when molecules are
similar?
• What can we do using similarity?

3. Molecular Representa/ons
• Explicit
– Indicate what the atoms are, what atom is connected
to what other atom(s)
– Differing levels of explicitness
• Do we need to show hydrogens?
• Do we need to indicate actual bonds?
• Implicit
– Usually very compact (e.g., SMILES)
– Need to know the assump/ons involved
• In SMILES, no specific bond symbol implies single bond

4. 2D Representa/ons ‐ Topological
• (Usually) indicates what types of atoms are
present
• Indicates which atoms are connected to which
other atoms
• No indica/on of where these atoms are
located in space
• Very easy to store, manipulate
Cl

5. 3D Representa/ons ‐ Geometric
• Similar to 2D, but now has
explicit 3D coordinates
• More complex – a molecule
can have mul/ple sets of 3D
coordinates (conforma/ons)
– Which is the correct one?
• Takes more space to store,
/me consuming to generate

6. Molecular Similarity
• Many, many ways to determine how similar
two molecules are
• A simple, manual approach is to look at a 2D
depic/on
• But what are we looking at?
Willet, J Chem Inf Comput Sci, 1998, 38, 983-996
Sheridan et al, Drug Discov Today, 2002, 7, 903-911

7. Molecular Similarity
• But 2D can be misleading
• Iden/cal in 2D is not necessarily so in 3D

8. How Do We Quan/fy Similarity?
• 1D similarity can be computed just by using
SMILES, similar to sequence alignment –
LINGO, Holograms
• 2D similarity is commonly measured using
binary fingerprints
– Key based fingerprints
– Hashed fingerprints

9. How Do We Quan/fy Similarity?
• Given 2 fingerprints we can then calculate a
variety of similarity func/ons
• Tanimoto is the most commonly used
– Ranges from 0 to 1
– A measure of the number of bits common to both
fingerprints
– See Daylight for more details
• Can also be extended to 3D similari/es

10. How Do We Quan/fy Similarity?
• 3D similarity is more complex
• Most methods require you to align two 3D
structures
• Then determine the “volume overlap”
– To what extent do the two structures occupy the
same region in space
• Most well known tool for this is ROCS

11. How Do We Quan/fy Similarity?
• Property based similarity will use various
physical proper/es or biological ac/vi/es
– If two molecules exhibit similar ac/vity across
mul/ple cell lines, they are likely similar
– If two molecules have a set of similar physical
proper/es (computed or experimental) they are
likely similar

12. 2D or 3D?
• Fast and easy • More “accurate”
• Not always • Computa/onally
biological relevant more expensive
• But surprisingly • Which
useful conforma/on is the
correct one?
Different representations and similarity
methods will, in general, lead to different
results (hits)

13. What Can We Do With Similarity?
• Searching databases – exact substructure
searching is not always useful
• Using the benzodiazepine substructure would
miss midazolam
• But, the 2D similarity
O N
H
N
between these two
N
structures is rela/vely
N
Cl N
high F
Query Midazolam

14. But 2D Only Goes So Far …
• Using the tradi/onal benzodiazepine core won’t
let you retrieve atypical benzodiazepines
• In this case, the 2D similarity
between this and the
usual core is low
• But in terms of shape they are
quite similar Ambien
• (Ambien occupies the same region of the GABA
receptor as tradi8onal benzodiazepines)

15. Virtual Screening
Sheridan et al, Drug Discov Today, 2002, 7, 903-911
• In many cases the ques/on we’re
asking is
• Find me other ac2ve molecules
• A good star/ng point is to look for
structurally similar molecules
• We assume that molecules with
similar structures will exhibit
similar ac/vites
– J. Med. Chem., 2002, 45, 4350‐4358
– The basis of predic/ve modeling
– But lots and lots of excep/ons!

16. Virtual Screening
• 2D similarity is a cheap, easy and fast way to
perform this type of task
• Can “screen” databases of many millions of
molecules extremely rapidly
• Usually only consider “very similar” (Tc >= 0.85)
hits
• It works …

17. Virtual Screening
• But can be of limited use if used naively
– Similarity is usually supplanted by machine learning
– S/ll, the only way out if there is no receptor and
only a few (or a single) known ac/ves
• Main drawback is that the hits are structurally
similar
– D’oh!
– Not great if you’re trying to find a molecule that
someone else hasn’t already developed

18. Scaffold Hopping
• Ideally, we’d like to find a molecule that is as
ac/ve as our query, but with a different core
structure
• Solving this usually requires us to go to 3D
– Structures can differ in
connec/vity
– But exhibit similar shapes
• Being able to do this in 2D is
an interes/ng research topic
(cf reduced graphs) Bergmann et al, J Chem Inf Model, 2009, 49, 658-669

19. Dissimilarity & Library Design
• Chemical libraries form the basis of high
throughput screening and other discovery
methods
• Sizes can range from a few hundred molecules
to millions (or billions for virtual libraries)
• In most cases, we want to cover as much of
chemical space as possible
– How do we compare coverage?
– So if we want to add new molecules, how do we
choose them?

20. Dissimilarity & Library Design
• Brute force
– Evaluate similarity between
new molecules and the
library and keep those with
low Tc
• Sophis/cated
– Use sta/s/cal techniques to
effec/vely sample different
regions of a chemical space
– Fill in the “holes”

21. Summary
• Similarity (and dissimilarity) are
fundamental concepts
– Simple on the outside, complex on the inside
• A wide variety of methods available
– Need to consider pros/cons in terms of
computa/onal expense, chemical u/lity, …
• Visualizing similarity is useful
• Many problems can be recast in terms of
similarity or dissimilarity