A Structure-Based Drug Design Framework using Graph Neural Networks and Molecular Dynamics Simulation

Abstract

The process of discovering novel pharmaceutical  compounds is traditionally a capital-intensive and time-consuming  endeavor, often plagued by high attrition rates in late-stage clinical  trials. Structure-based drug design has emerged as a pivotal  methodology to mitigate these risks by leveraging the three dimensional information of target proteins. However, conventional  docking algorithms frequently struggle with the accurate prediction  of binding affinities due to their reliance on static structural  representations and simplified scoring functions. This paper  proposes a novel framework that synergizes the representational  power of Graph Neural Networks with the temporal resolution of  Molecular Dynamics simulations. By treating molecular complexes  as topological graphs, the proposed deep learning architecture  captures intricate spatial dependencies and chemical interactions  that are often overlooked by linear descriptors. Furthermore, the  integration of short-trajectory simulations allows for the  incorporation of entropic contributions and conformational  flexibility into the predictive model. The experimental results  demonstrate that this hybrid approach significantly outperforms  traditional docking baselines and standalone static deep learning  models in predicting binding free energies. The framework offers a  robust solution for virtual screening campaigns, effectively  balancing computational efficiency with physical accuracy.