  Exploring molecular spaces is crucial for advancing our understanding of
chemical properties and reactions, leading to groundbreaking innovations in
materials science, medicine, and energy. This paper explores an approach for
active learning in molecular discovery using Deep Kernel Learning (DKL), a
novel approach surpassing the limits of classical Variational Autoencoders
(VAEs). Employing the QM9 dataset, we contrast DKL with traditional VAEs, which
analyze molecular structures based on similarity, revealing limitations due to
sparse regularities in latent spaces. DKL, however, offers a more holistic
perspective by correlating structure with properties, creating latent spaces
that prioritize molecular functionality. This is achieved by recalculating
embedding vectors iteratively, aligning with the experimental availability of
target properties. The resulting latent spaces are not only better organized
but also exhibit unique characteristics such as concentrated maxima
representing molecular functionalities and a correlation between predictive
uncertainty and error. Additionally, the formation of exclusion regions around
certain compounds indicates unexplored areas with potential for groundbreaking
functionalities. This study underscores DKL's potential in molecular research,
offering new avenues for understanding and discovering molecular
functionalities beyond classical VAE limitations.
