Understanding
the interaction between drug molecules and proteins
is one of the main challenges in drug design. Several tools have been
developed recently to decrease the complexity of the process. Artificial
intelligence and machine learning methods offer promising results
in predicting the binding affinities. It becomes possible to do accurate
predictions by using the known protein–ligand interactions.
In this study, the electrostatic potential values extracted from 3-dimensional
grid cubes of the drug–protein binding sites are used for predicting
binding affinities of related complexes. A new algorithm with a dynamic
feature selection method was implemented, which is derived from Compressed
Images For Affinity Prediction (CIFAP) study, to predict binding affinities
of Checkpoint Kinase 1 and Caspase 3 inhibitors.
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