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Graph Neural Networks for Binding Affinity Prediction 

Alex Gurbych
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24 авг 2024

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Комментарии : 8   
@insilicokiddo
@insilicokiddo 11 месяцев назад
yow, is there an available google colab for this?
@codewithluq
@codewithluq 6 месяцев назад
Is there code to look at explaining this work?
@StevenNess
@StevenNess 16 дней назад
cool
@tag_of_frank
@tag_of_frank 3 года назад
Is binding affinity between protein and ligand dependent on solvent? If so, that would severely limit the application of this to whatever solvents are used in the existing datasets. If the binding affinity is solvent-dependent, do you know off-hand what solvent is used by PDBBank?
@AlexGurbych
@AlexGurbych 3 года назад
Hi Fahraynk, Good question. Affinity is heavily dependent on the solvent, pH, temperature, dissolved salts, etc. To avoid uncertainty, it is usually measured under normal conditions (Solvent=Water, T=293 K, P=101.3 kPa, pH=7.4). And then there is chemical thermodynamics to recalculate ΔG, Ki, Kd to whatever conditions are needed :)
@tag_of_frank
@tag_of_frank 3 года назад
@@AlexGurbych Thanks for the reply. Yes I think the thermodynamics won't help so much because if water binds to your ligand, but DCM does not, then the binding affinity could be orders of magnitude different in DCM than in water. But this method is probably good for testing binding in blood since blood has a lot of water.
@AlexGurbych
@AlexGurbych 3 года назад
​@@tag_of_frank molecular binding affinity is typically applied to living systems - which is not relevant for your case. I wonder what living being could exist having DCM as a primary solvent :) The major issue here is that ML is built on known data fitting - but there is not enough open-source data for DCM affinities. I would suggest considering methods like coarse-grained (as biomolecules are pretty large) molecular dynamics. You will be able to set solvent, temperature, pressure, and other modeling parameters. Use such forcefields as Amber, CHARMM, GROMOS, and OPLS-AA - they describe protein-ligand interaction under different conditions pretty well.
@tag_of_frank
@tag_of_frank 3 года назад
​@@AlexGurbych Thanks you've been very helpful.
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