PREDICTION OF MOLECULAR FIELD POINTS USING SE(3)-TRANSFORMER MODEL

Florian Hinz, Amr H Mahmoud, Markus Alexander Lill

Abstract:

Due to their computational efficiency, 2D fingerprints are typically used
in similarity-based high-content screening. The interaction of a ligand
with its target protein, however, relies on its physicochemical interactions
in 3D space. Thus, ligands with different 2D scaffolds can bind to the
same protein if these ligands share similar interaction patterns. Molecular
fields can represent those interaction profiles. For efficiency, the extrema of
those molecular fields, named field points, are used to quantify the ligand
similarity in 3D. The calculation of field points involves the evaluation of the
interaction energy between the ligand and a small probe shifted on a fine grid
representing the molecular surface. These calculations are computationally
prohibitive for large datasets of ligands, making field point representations
of molecules intractable for high-content screening. Here, we overcome this
roadblock by one-shot prediction of field points using generative neural
networks based on the molecular structure alone. Field points are predicted
by training an SE(3)-Transformer, an equivariant, attention-based graph
neural network architecture, on a large set of ligands with field point data.
Initial data demonstrates the feasibility of this approach to precisely generate
negative, positive and hydrophobic field points within 1 Å of the ground
truth for a diverse set of drug-like molecules.