Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy
This work addresses the challenge of accurate drug screening for pharmaceutical research, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of low accuracy in compound-protein binding affinity (CPA) prediction by developing FeatNN, a novel end-to-end architecture that uses a coevolutionary strategy to represent protein multimodal information, resulting in FeatNN considerably outperforming state-of-the-art baselines in virtual drug screening tasks.
Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug screening tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.