  Deep learning has significantly advanced and accelerated de novo molecular
generation. Generative networks, namely Variational Autoencoders (VAEs) can not
only randomly generate new molecules, but also alter molecular structures to
optimize specific chemical properties which are pivotal for drug-discovery.
While VAEs have been proposed and researched in the past for pharmaceutical
applications, they possess deficiencies which limit their ability to both
optimize properties and decode syntactically valid molecules. We present a
recurrent, conditional $\beta$-VAE which disentangles the latent space to
enhance post hoc molecule optimization. We create a mutual information driven
training protocol and data augmentations to both increase molecular validity
and promote longer sequence generation. We demonstrate the efficacy of our
framework on the ZINC-250k dataset, achieving SOTA unconstrained optimization
results on the penalized LogP (pLogP) and QED scores, while also matching
current SOTA results for validity, novelty and uniqueness scores for random
generation. We match the current SOTA on QED for top-3 molecules at 0.948,
while setting a new SOTA for pLogP optimization at 104.29, 90.12, 69.68 and
demonstrating improved results on the constrained optimization task.
