Machine
learning approaches promise to accelerate and improve success
rates in medicinal chemistry programs by more effectively leveraging
available data to guide a molecular design. A key step of an automated
computational design algorithm is molecule generation, where the machine
is required to design high-quality, drug-like molecules within the
appropriate chemical space. Many algorithms have been proposed for
molecular generation; however, a challenge is how to assess the validity
of the resulting molecules. Here, we report three Turing-inspired
tests designed to evaluate the performance of molecular generators.
Profound differences were observed between the performance of molecule
generators in these tests, highlighting the importance of selection
of the appropriate design algorithms for specific circumstances. One
molecule generator, based on match molecular pairs, performed excellently
against all tests and thus provides a valuable component for machine-driven
medicinal chemistry design workflows.