  Despite experimental and curation efforts, the extent of enzyme promiscuity
on substrates continues to be largely unexplored and under documented.
Recommender systems (RS), which are currently unexplored for the
enzyme-substrate interaction prediction problem, can be utilized to provide
enzyme recommendations for substrates, and vice versa. The performance of
Collaborative-Filtering (CF) recommender systems however hinges on the quality
of embedding vectors of users and items (enzymes and substrates in our case).
Importantly, enhancing CF embeddings with heterogeneous auxiliary data,
specially relational data (e.g., hierarchical, pairwise, or groupings), remains
a challenge. We propose an innovative general RS framework, termed Boost-RS,
that enhances RS performance by "boosting" embedding vectors through auxiliary
data. Specifically, Boost-RS is trained and dynamically tuned on multiple
relevant auxiliary learning tasks Boost-RS utilizes contrastive learning tasks
to exploit relational data. To show the efficacy of Boost-RS for the
enzyme-substrate prediction interaction problem, we apply the Boost-RS
framework to several baseline CF models. We show that each of our auxiliary
tasks boosts learning of the embedding vectors, and that contrastive learning
using Boost-RS outperforms attribute concatenation and multi-label learning. We
also show that Boost-RS outperforms similarity-based models. Ablation studies
and visualization of learned representations highlight the importance of using
contrastive learning on some of the auxiliary data in boosting the embedding
vectors.
