Exploring the potential of compound-protein complex structure-free models in virtual screening using BlendNet.

Abstract

Identifying new compounds that interact with a target is a crucial time-limiting step in the initial phases of drug discovery. Compound-protein complex structure-based affinity prediction models can expedite this process; however, their dependence on high-quality three-dimensional (3D) complex structures limits their practical application. Prediction models that do not require 3D complex structures for binding-affinity estimation offer a theoretically attractive alternative; however, accurately predicting affinity without interaction information presents significant challenges. We introduce BlendNet, a framework that employs a knowledge transfer strategy to improve affinity prediction accuracy by learning the interdependent relationships between compounds and proteins without relying on 3D complex structures. Compared with state-of-the-art models for affinity prediction, BlendNet demonstrated superior performance across various cold-start cases. The ability of BlendNet to interpret compound-protein interactions without utilizing complex structure data highlights its potential to accelerate and streamline drug development.