  We propose a framework using normalizing-flow based models, SELF-Referencing
Embedded Strings, and multi-objective optimization that efficiently generates
small molecules. With an initial training set of only 100 small molecules,
FastFlows generates thousands of chemically valid molecules in seconds. Because
of the efficient sampling, substructure filters can be applied as desired to
eliminate compounds with unreasonable moieties. Using easily computable and
learned metrics for druglikeness, synthetic accessibility, and synthetic
complexity, we perform a multi-objective optimization to demonstrate how
FastFlows functions in a high-throughput virtual screening context. Our model
is significantly simpler and easier to train than autoregressive molecular
generative models, and enables fast generation and identification of druglike,
synthesizable molecules.
