  We address the task of controlled generation of small molecules, which
entails finding novel molecules with desired properties under certain
constraints (e.g., similarity to a reference molecule). Here we introduce
MolMIM, a probabilistic auto-encoder for small molecule drug discovery that
learns an informative and clustered latent space. MolMIM is trained with Mutual
Information Machine (MIM) learning, and provides a fixed length representation
of variable length SMILES strings. Since encoder-decoder models can learn
representations with ``holes'' of invalid samples, here we propose a novel
extension to the training procedure which promotes a dense latent space, and
allows the model to sample valid molecules from random perturbations of latent
codes. We provide a thorough comparison of MolMIM to several variable-size and
fixed-size encoder-decoder models, demonstrating MolMIM's superior generation
as measured in terms of validity, uniqueness, and novelty. We then utilize
CMA-ES, a naive black-box and gradient free search algorithm, over MolMIM's
latent space for the task of property guided molecule optimization. We achieve
state-of-the-art results in several constrained single property optimization
tasks as well as in the challenging task of multi-objective optimization,
improving over previous success rate SOTA by more than 5\% . We attribute the
strong results to MolMIM's latent representation which clusters similar
molecules in the latent space, whereas CMA-ES is often used as a baseline
optimization method. We also demonstrate MolMIM to be favourable in a compute
limited regime, making it an attractive model for such cases.
