Predicting Side Effect of Drug Molecules Using Recurrent Neural Networks

Identification of molecular properties, like side effects, is one of the most important and time-consuming steps in the process of molecule synthesis. 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. 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 GRU recurrent neural network, with a 98+% reduction of required parameters compared to available large language models while obtaining near identical results as top-performing models.