Abstract
Detection of protein-ligand binding sites is essential not only for protein function investigation but also in fields such as drug discovery or bioengineering. In this paper, we show that the recently-developed pre-trained language models can be used for protein-ligand binding site prediction.
Specifically, we present a neural network architecture where inputs correspond to amino acids embeddings obtained from a protein language model. We show that increasing complexity of the language model improves the predictive performance of the method, eventually leading to results comparable to or surpassing state-of-the-art approaches.
Unlike the existing methods, the presented approach does not require time-consuming computation of evolutionary information, resulting in faster running times.