Deep Imitation Learning for Molecular Structure Discovery

The patent replaces traditional mechanical reconstruction methods with deep learning neural networks that have been trained to predict molecular structures from spectroscopic data. The neural network model substitutes the complex iterative inversion process with a direct data-driven prediction approach, achieving faster computation while maintaining accuracy.

The patent uses imitation learning where the neural network is trained to copy the transformations learned from spectroscopic data to molecular structure predictions. The model learns from example data the mapping between spectral patterns and molecular structures, replicating this knowledge to rapidly predict unknown structures without rederiving the complex relationships each time.

The patent segments the molecular structure prediction task into multiple components: graph convolutional layers for processing molecular graphs, attention mechanisms for identifying important structural features, and a final prediction head for structure determination. This segmentation allows each component to be optimized independently and reduces overall computational burden by focusing computations only on relevant parts of the molecule.

The patent applies local quality by using graph convolutional networks that process different parts of the molecular graph with appropriate complexity. The model adapts the computational intensity to the local chemical environment, applying more sophisticated processing to regions with complex bonding patterns while using simpler processing for straightforward structural elements, thereby optimizing the balance between accuracy and computational cost.

The patent incorporates feedback mechanisms where the neural network continuously evaluates generated molecular structures against the input spectroscopic data and adjusts predictions accordingly. The model uses feedback from intermediate structure evaluations to refine subsequent predictions, ensuring that only structurally valid molecules are generated while maintaining flexibility in handling diverse molecular types.

Instead of generating molecules sequentially and hoping for correctness, the patent inverts the approach by using the spectroscopic data as ground truth to directly infer the correct molecular structure. The model works backwards from the spectral information, using the known physics of spectroscopy to constrain and guide the structure prediction, thereby guaranteeing reliability while maintaining adaptability to different molecular classes.