  The contemporary drug design process demands considerable time and resources
to develop each new compound entering the market. Generating small molecules is
a pivotal aspect of drug discovery, essential for developing innovative
pharmaceuticals. Uniqueness, validity, diversity, druglikeliness,
synthesizability, and solubility molecular pharmacokinetic properties, however,
are yet to be maximized. This work introduces several new generative
adversarial network models based on engineering integration of parametrized
quantum circuits into known molecular generative adversarial networks. The
introduced machine learning models incorporate a new multi-parameter reward
function grounded in reinforcement learning principles. Through extensive
experimentation on benchmark drug design datasets, QM9 and PC9, the introduced
models are shown to outperform scores achieved previously. Most prominently,
the new scores indicate an increase of up to 30% in the druglikeness
quantitative estimation. The new hybrid quantum machine learning algorithms, as
well as the achieved scores of pharmacokinetic properties, contribute to the
development of fast and accurate drug discovery processes.
