Drug Discovery Flashcards
1
Q
Overview of Computer Aided Drug Design
A
- drug design based on ligand shape and x-ray structure
- virtual screening
- pharmacophores
- rigid or flexible docking
- lead compounds identified
- assess compounds by assays or x-ray structures
2
Q
CADD Steps
A
- medical need identified
- relevant mechanism determined using structure function studies
- mechanism or high throughput based screens of libraries
- hits and lead compounds found
- drug candidates identified
- preclinical and clinical testing
3
Q
Identify New Hits
A
- high throughput screening
- virtual screening
- or a combination of both
4
Q
High Throughput Screening Limitations
A
- mostly used in pharma but is expensive and yields false positives (hit appears to inhibit the protein when it actually doesn’t)
- even largest libraries of compounds are miniscule in comparison with the possible chemical diversity space (many missed options)
5
Q
HTS Method
A
- HTS dispenser (computer/automatic)
- biological activity meter (assays)
- interaction analysis (isothermal titration/SPR/yeast genetic interaction
- X-ray crystal structure analysis/docking simulation
6
Q
Computer Aided Drug Discovery
A
- in silico screening
- ligand based or structure based drug design
- structure based methods require 3D information of target
- ligand based methods used when target structure is not known : uses information about the known binding molecules
7
Q
LBDD Steps
A
- virtual combinatorial library design (select molecular scaffold)
- pre processing library
- filtering
- virtual HTS
- LBDD virtual screen
- similarity searching : 2D/3D fingerprint, volume, pharmacophore
- hit selection and biological evaluation
8
Q
SBDD Steps
A
- non combinatorial library
- pre processing library
- filtering
- virtual HTS
- SBDD screen
- preparation of targets
- docking
- hit selection and biological evaluation
9
Q
Ligand Based drug discovery
A
- takes into account different kinds of molecular information like 3D molecule shape, molecular and electronic properties, and 3D pharmacophore
- similarity search of a drug database and ranking of hits
10
Q
LBDD Strategies
A
2D fingerprint:
- identify sequence and the molecular and electronic properties
- classify library compounds as active/inactive
3D fingerprint:
- molecular shape and pharmacophore
- ranking library compounds
11
Q
LBDD Methods
A
- similarity searching
- pharmacophore mapping
- machine learning
12
Q
Similarity searching
A
- structurally similar molecules tend to have similar properties
- given active reference structure rank compounds based on similarities
- top ranking compounds tested for activity
- need a way to measure similarity between compounds quantitatively
eg. B-lactam ring in compounds
13
Q
Similarity measure
A
- molecular descriptors like 2D properties and physicochemical properties
- maximum common substructures like 3D properties
- similarity coefficient measures the similarity between two sets of molecular descriptors
14
Q
2D Fingerpting
A
- fingerprint is a part of the molecule similar throughout the search
- each bit in the bit string represents one molecular fragment
- the bit string for a molecule records presence as 1 and absence as 0 for each fragment in the molecule
- similarity assessed by comparing number of bits that are common between the two structures
- no information about arrangement of the groups or their 3D structure though
15
Q
Hashed Fingerprint
A
- chemical hashed fingerprint of the molecule is a bit string containing information on topology/structure
- stereochemistry not considered
- score of a compound is based on which groups are near what/set parameters for topology
- careful of bit collisions: pattern for describing the same group many times (ie. same bit set by many patterns)
16
Q
Similarity Coefficient
A
- Tanimoto coefficient
- set the coefficient to be what you want for similarity between original and hit compounds
= number of c bits set in common in reference + database structure / (number of a bits in reference structure + number of b bits in hit structure - number of c bits)
17
Q
Scaffold hopping
A
- find molecule as active as query one but with different core structure
- requires 3D searches
- rather than being based on chemistry the similarity is based on shape
- ie. molecules giving the same effect with different core structures
- need to identify the pharmacophores
18
Q
3D Fingerprinting
A
- identify groups of interest and find similar groups giving the same properties
- search how functional groups link in 3D space
- identify triangles of topology: triplets of atoms and their associated distances
- many triangles are identified with different properties like H bonding, hydrophobic/philic, etc
- pharmacophore pairs and triplets
- take into account valence and torsion angles
19
Q
Pharmacophore
A
- substructure of molecule responsible for its pharmacological activity
20
Q
3D Shape Search
A
- molecules aligned in 3D
- similarity score based on common volume
- conformational plasticity of compound taken into account
21
Q
Cons of 3D Search
A
- lots of databases use crystal structures
- might not reflect conformation in the solvent
22
Q
Types of Similar Compounds
A
- similar in 3D not in 2D
- similar in 2D but not in 3D
- identical in 2D but not 3D
23
Q
Scoring
A
- for a reference with known activity search it against a database containing other actives and decoy compounds
- good similarity measure clusters the known actives at the top of the ranking
- not ideal for scaffold hopping