EISA-Score: Element Interactive Surface Area Score for Protein–Ligand Binding Affinity Prediction

Molecular surface representations have been advertised
as a great
tool to study protein structure and functions, including protein–ligand
binding affinity modeling. However, the conventional surface-area-based
methods fail to deliver a competitive performance on the energy scoring
tasks. The main reason is the lack of crucial physical and chemical
interactions encoded in the molecular surface generations. We present
novel molecular surface representations embedded in different scales
of the element interactive manifolds featuring the dramatically dimensional
reduction and accurately physical and biological properties encoders.
Those low-dimensional surface-based descriptors are ready to be paired
with any advanced machine learning algorithms to explore the essential
structure–activity relationships that give rise to the element
interactive surface area-based scoring functions (EISA-score). The
newly developed EISA-score has outperformed many state-of-the-art
models, including various well-established surface-related representations,
in standard PDBbind benchmarks.