  In order to design a more potent and effective chemical entity, it is
essential to identify molecular structures with the desired chemical
properties. Recent advances in generative models using neural networks and
machine learning are being widely used by many emerging startups and
researchers in this domain to design virtual libraries of drug-like compounds.
Although these models can help a scientist to produce novel molecular
structures rapidly, the challenge still exists in the intelligent exploration
of the latent spaces of generative models, thereby reducing the randomness in
the generative procedure. In this work we present a manifold traversal with
heuristic search to explore the latent chemical space. Different heuristics and
scores such as the Tanimoto coefficient, synthetic accessibility, binding
activity, and QED drug-likeness can be incorporated to increase the validity
and proximity for desired molecular properties of the generated molecules. For
evaluating the manifold traversal exploration, we produce the latent chemical
space using various generative models such as grammar variational autoencoders
(with and without attention) as they deal with the randomized generation and
validity of compounds. With this novel traversal method, we are able to find
more unseen compounds and more specific regions to mine in the latent space.
Finally, these components are brought together in a simple platform allowing
users to perform search, visualization and selection of novel generated
compounds.
