  Identification and verification of molecular properties such as side effects
is one of the most important and time-consuming steps in the process of
molecule synthesis. For example, failure to identify side effects before
submission to regulatory groups can cost millions of dollars and months of
additional research to the companies. Failure to identify side effects during
the regulatory review can also cost lives. The complexity and expense of this
task have made it a candidate for a machine learning-based solution. Prior
approaches rely on complex model designs and excessive parameter counts for
side effect predictions. We believe reliance on complex models only shifts the
difficulty away from chemists rather than alleviating the issue. Implementing
large models is also expensive without prior access to high-performance
computers. We propose a heuristic approach that allows for the utilization of
simple neural networks, specifically the recurrent neural network, with a 98+%
reduction in the number of required parameters compared to available large
language models while still obtaining near identical results as top-performing
models.
