Structure-Based Generation of 3D Small-Molecule Drugs: Are We There Yet?

Structure-based drug design (SBDD) plays a crucial role
in preclinical
discovery. Recently, structure-based generative algorithms have been
developed to streamline the SBDD process, by generating novel, drug-like
molecule designs based on the binding pocket structure of target protein.
However, there is no effective metric to evaluate the chemical plausibility
of molecules designed by these algorithms, which can limit further
applications. In this study, we introduce two new metrics for assessing
the chemical plausibility of generated molecules and show that these
algorithms can generate chemically implausible structures with certain
property distributions that differ from those of known drug-like molecules.
We further compare results with high-throughput virtual screening
hits for three targets: c-SRC kinase, Smoothened receptor, and dopamine
D1 receptor. These metrics and analysis methods described here offer
valuable tools for assessing the chemical plausibility and drug-likeness
of generated molecules, ultimately enhancing the use of structure-based
generation in drug discovery.