Hybrid Molecular Nano Frameworks for Disease Modeling Using Neuro Symbolic Machine Learning

Disease modelling has advanced significantly when computational methods and molecular data are used. For prediction accuracy and interpretability, this initiative proposes to create a hybrid molecular nano framework for disease modelling employing neuro-symbolic machine learning, with Graph Attention Networks (GAT) as the central method. The GAT depicts molecules as graphs, and the technique interactively assigns focus to atom-bond interactions to extract structural and relational properties. These traits' anticipated disease-relevant relationships and biological objectives are paired with symbolic reasoning, which uses molecular similarity metrics (such as Tanimoto coefficients) and 3D structure data. One of the main discoveries is the successful use of GAT to capture necessary chemical substructures and achieve better prediction performance than conventional models. The hybrid framework showed excellent interpretability, effectively connecting biological targets like CYP2D6 and HERG to molecular patterns and offering insights into disease processes. Finally, combining GAT with symbolic reasoning reveals a viable strategy for molecular-based illness modelling by striking a balance between interpretability and prediction accuracy.