Understanding protein function is pivotal in comprehending the intricate mechanisms that underlie many crucial biological activities, with far-reaching implications in the fields of medicine, biotechnology, and drug development. However, more than 200 mi
the tested GearBind variants perform worse than GearBind on all four metrics. The exclusion of edge- and residue-level message passing from GearBind brings a 13% and 3% SpearmanR drop, highlighting the benefits
molecular graphs using sequences and SMILES, subsequently deriving representations through graph neural networks. However, these graph-based approaches are limited by the use of afixed adjacent matrixof protein and drug molecular graphs for graph convolution. This limitation restricts the learning of comp...
An edge is then intro- duced between two atom nodes if they share a bond. This construction method is illus- trated in Fig. 3. Besides, a self-loop is incorporated into the molecular graph, connecting each atom node to itself for better aggregating information of the drug molecule. ...
For example, object of the dependency yields a network of verbs and nouns representing processes that can be performed on entities, while adjective modifier represents the attributes of entities, etc. The association network layer constructed from collocated lexemes in the and/or syntactic dependency,...