A multi-target drug design method based on target feature fusion

Targeted drugs are medications designed to treat diseases by targeting specific sites on cancerous or diseased cells. Multi-target drugs can target multiple protein sites to treat diseases, improving therapeutic efficiency, but are more challenging to design. Computer-aided targeted drug design can reduce costs and shorten development time, with most drugs being single-target. Recent research on multi-target drug design has focused on optimizing single-target drugs into multi-target drugs, but this approach has limitations. This study proposes a multi-target drug design method based on protein feature fusion, which encodes and integrates features based on the target's sequence characteristics, enabling the design of multi-target drugs without prior knowledge of the targeted drug. The target protein sequences are embedded to extract features. Each target's features are independently encoded into latent vectors, while the features of multiple targets are encoded into similarity latent vectors. By leveraging both individual target features and the similarity features among targets, multi-target drugs can be efficiently designed.

We validated the proposed multi-target drug design method on three groups of targets: the 3CLpro and PLpro targets for COVID-19, the TAAR1 and DRD2 targets for schizophrenia, and the MEK1 and mTOR targets for tumors. The designed multi-target drugs can be docked with target proteins possessing unique molecular structures, tailored to the specific requirements of different target pocket structures. The excellent fit between the molecular structures of the multi-target drugs and the protein structures of multiple targets validates the performance of the proposed method.

The proposed method can efficiently design multi-target drugs with stronger predicted binding affinities than those reported in previous studies. These drugs are capable of adapting to multiple targets based on the features of the target proteins. Additionally, the model demonstrates excellent generalization ability for untrained multiple targets.