QPred: A Quantum Mechanical Property Predictor for Small Molecules

Predicting the physicochemical properties of molecules is a cornerstone of computational chemistry and drug discovery. While quantum mechanical methods provide high accuracy, their computational expense limits their use in high-throughput screening. To bridge this gap, we introduce a novel, disentangled deep learning architecture that adaptively predicts molecular properties by leveraging either 2D topological graphs or 3D geometric information. Our framework consists of two specialized models: a Message Passing Neural Network (MPNN) enhanced with cycle-based semimaster nodes for robust 2D feature extraction, and an equivariant network with a disentangled update mechanism for high-fidelity 3D-aware predictions. The architecture's built-in hierarchical attention mechanism provides interpretability by highlighting salient atomic and substructure features. This adaptive, high-performance, and interpretable framework offers a versatile solution for accelerating molecular discovery, irrespective of the data dimensionality.