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MOLECULAR DESCRIPTORS AND CHEMICAL SPACES - Coggle Diagram
MOLECULAR DESCRIPTORS AND CHEMICAL SPACES
Small molecules can be described with unequivocal representation by
MOLECULAR DESCRIPTORS
that fit them in
CHEMICAL REFERENCE SPACES
to define those spaces we need
molecular and chemical descriptors
that describe the
structure of a molecule and its chemical and physical properties
no generally preferred descriptor spaces
we need to generate a specific reference space for each application and target on a case by case
Molecular Descriptor
➡︎
result of a logic and mathematical procedure in which we have the translation of molecular properties into numbers or visual representation properties
we can have mono/bi/tri-dimensional properties that can be described
FINGERPRINTS
Substructure based fingerprints
Topology-based fingerprints
Circular path fingerprints
Hybrid fingerprints
Molecular Similarity, dissimilarity and diversity
Similarity property principle
for a given chemical reference space
chemical similarity
biological similarity
molecules located together in the reference space → considered to be functionally related
Diversity analysis
dissimilarity algorithm
➔ we can use equations to measure the relative dissimilarity between chemical structures
➔ we want to have as different compounds as we can for screening for the chemical collection
A) Modification and Simplification of chemical spaces
High dimensional chemical spaces might often be too complex to carry out meaningful analyses
Auto scaling or variance scaling
Dimension reduction
B) Compound classification and selection
compounds that populate the same partitions are considered to be similar
Diversity - based selection
Activity - based selection
C) Compound Filtering
attempt to identify compounds with desired properties and discard others
⇝ PAINS
LEPINSKY RULE OF 5
and
VEBER
filter
filters are used at the beginning of a project to select better compounds to put in a library
FRAGMENT BASED DRUG DISCOVERY
screening collections
HTS libraries ⇒ druglike vs leadlike
large ligands have
high potential potency
small ligands have a
higher probability of binding
RULE OF 3 FOR FRAGMENTS
requirements for a good fragment collection
screening of drug fragments
other than screening 1M compounds with the combination of R1, R2, R3 fragment positions we
screen the fragment for the position individually with the other fixed for low activity
→ we will need just 300 screenings (
one at the time approach and then merge all the infos together
)
fragonomics based on central scaffold
screening formats
High concentration screening
NMR screening
X-ray screening concept
SPR screening
ITC
when we have a hit form a fully decorated molecule, we want to modify it to increase its bioavailability and potency. But we cannot add more groups because otherwise it would be too heavy so we need to deconstruct the molecule and substitute some parts to optimize it, this is not a very efficient process though since every time we want to substitute something we have to start from scratch. So instead for selecting molecules from libraries of fully decorated molecules that follow the Lipinski rule we would just
start from fragments (MW<150)