Macrocycles
target proteins that are otherwise considered undruggable
because of a lack of hydrophobic cavities and the presence of extended
featureless surfaces. Increasing efforts by computational chemists
have developed effective software to overcome the restrictions of
torsional and conformational freedom that arise as a consequence of
macrocyclization. Moloc is an efficient algorithm, with an emphasis
on high interactivity, and has been constantly updated since 1986
by drug designers and crystallographers of the Roche biostructural
community. In this work, we have benchmarked the shape-guided algorithm
using a dataset of 208 macrocycles, carefully selected on the basis
of structural complexity. We have quantified the accuracy, diversity,
speed, exhaustiveness, and sampling efficiency in an automated fashion
and we compared them with four commercial (Prime, MacroModel, molecular
operating environment, and molecular dynamics) and four open-access
(experimental-torsion distance geometry with additional “basic
knowledge” alone and with Merck molecular force field minimization
or universal force field minimization, Cambridge Crystallographic
Data Centre conformer generator, and conformator) packages. With three-quarters
of the database processed below the threshold of high ring accuracy,
Moloc was identified as having the highest sampling efficiency and
exhaustiveness without producing thousands of conformations, random
ring splitting into two half-loops, and possibility to interactively
produce globular or flat conformations with diversity similar to Prime,
MacroModel, and molecular dynamics. The algorithm and the Python scripts
for full automatization of these parameters are freely available for
academic use.