  Computational drug discovery strategies can be broadly placed in two
categories: ligand-based methods which identify novel molecules by similarity
with known ligands, and structure-based methods which predict molecules with
high-affinity to a given 3D structure (e.g. a protein). However, ligand-based
methods do not leverage information about the binding site, and structure-based
approaches rely on the knowledge of a finite set of ligands binding the target.
In this work, we introduce TarLig, a novel approach that aims to bridge the gap
between ligand and structure-based approaches. We use the 3D structure of the
binding site as input to a model which predicts the ligand preferences of the
binding site. The resulting predictions could then offer promising seeds and
constraints in the chemical space search, based on the binding site structure.
TarLig outperforms standard models by introducing a data-alignment and
augmentation technique. The recent popularity of Volumetric 3DCNN pipelines in
structural bioinformatics suggests that this extra step could help a wide range
of methods to improve their results with minimal modifications.
