Structure‐based ligand design requires an exact description of the topology of molecular entities under scrutiny. IChem is a software package that reflects the many contributions of our research group in this area over the last decade. It facilitates and automates many tasks (e.g., ligand/cofactor atom typing, identification of key water molecules) usually left to the modeler's choice. It therefore permits the detection of molecular interactions between two molecules in a very precise and flexible manner. Moreover, IChem enables the conversion of intricate three‐dimensional (3D) molecular objects into simple representations (fingerprints, graphs) that facilitate knowledge acquisition at very high throughput. The toolkit is an ideal companion for setting up and performing many structure‐based design computations.
Discovering the very
first ligands of pharmacologically important targets in a fast and
cost-efficient manner is an important issue in drug discovery. In
the absence of structural information on either endogenous or synthetic
ligands, computational chemists classically identify the very first
hits by docking compound libraries to a binding site of interest,
with well-known biases arising from the usage of scoring functions.
We herewith propose a novel computational method tailored to ligand-free
protein structures and consisting in the generation of simple cavity-based
pharmacophores to which potential ligands could be aligned by the
use of a smooth Gaussian function. The method, embedded in the IChem
toolkit, automatically detects ligand-binding cavities, then predicts
their structural druggability, and last creates a structure-based
pharmacophore for predicted druggable binding sites. A companion tool
(Shaper2) was designed to align ligands to cavity-derived pharmacophoric
features. The proposed method is as efficient as state-of-the-art
virtual screening methods (ROCS, Surflex-Dock) in both posing and
virtual screening challenges. Interestingly, IChem-Shaper2 is clearly
orthogonal to these latter methods in retrieving unique chemotypes
from high-throughput virtual screening data.
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