End-to-End Protein Surface Learning From Atomic Point Clouds

The patent pre-computes and stores geometric and chemical features of protein surfaces during a preprocessing stage, creating a database of feature vectors that can be quickly retrieved and matched during binding site identification. This preliminary computation of features enables fast accurate matching without requiring complex real-time calculations, thus resolving the contradiction between accuracy and computational time.

The patent creates a simplified representation of protein surfaces using mesh models and extracts key geometric and chemical features as feature vectors. These simplified copies and extracted features replace the need for complex real-time computations, allowing accurate binding site identification through efficient feature matching while significantly reducing computational time and memory requirements.

The patent extracts only the essential geometric and chemical features from protein surface meshes, such as surface area, curvature, and chemical residue information. By taking out and storing only these critical feature vectors rather than the complete mesh data, the system achieves accurate binding site identification while dramatically reducing memory requirements.

The patent creates simplified feature vector representations of protein surfaces instead of storing complete mesh structures. These compressed copies contain the essential information needed for binding site identification but occupy far less memory, thus resolving the contradiction between accuracy and memory requirements.

The patent performs all mesh generation, feature extraction, and connectivity computation during an offline preprocessing stage. Once the feature vectors and mesh connectivities are pre-computed and stored, the actual binding site identification can proceed rapidly through simple feature matching, thus eliminating the computational bottleneck during the main processing task while maintaining full geometric and chemical analysis capability.

The patent segments the protein surface analysis into distinct stages: a preprocessing stage that computes mesh connectivities and extracts features, and a processing stage that performs binding site identification through feature matching. This segmentation separates the computationally intensive tasks from the rapid analysis tasks, improving overall processing efficiency while maintaining comprehensive structural analysis capability.