Protein-Complex Prediction with Recursive Transformers for Drug Design

The patent replaces traditional mechanical and experimental drug discovery methods with an AI-based recursive transformer system. The neural network automatically determines protein complex structures from constituent proteins, eliminating the need for time-consuming wet lab experiments and manual structure determination, thereby dramatically reducing both time and cost while improving success rates.

The recursive transformer neural network serves as an intermediary between known constituent proteins and unknown protein complex structures. Instead of direct experimental determination, the AI model mediates the structure prediction process by learning from training data and generating accurate complex structures, which can then be used for drug design without extensive clinical testing.

The patent segments the complex drug discovery process into distinct computational stages: input preparation (encoding constituent proteins), recursive transformation (predicting complex structures iteratively), and output generation (producing complex structure predictions). This segmentation allows each module to be optimized independently while maintaining overall system productivity.

The patent transitions from traditional 3D structural biology methods to a high-dimensional computational space using neural network embeddings and latent representations. By operating in this abstract dimensional space, the system can process and integrate multiple protein features simultaneously, achieving high productivity despite the underlying computational complexity.

The patent performs preliminary action by using the recursive transformer to accurately predict protein complex structures before clinical testing. This pre-verification of structural accuracy and drug-target compatibility filters out ineffective candidates early, reducing the number of compounds that need to proceed to expensive clinical trials, thereby verifying safety and effectiveness with reduced resource investment.

The patent creates computational copies and simulations of protein complexes and drug interactions in silico. Instead of physically synthesizing and testing every drug candidate, the AI model generates virtual models that predict binding affinity and structural compatibility, allowing virtual screening of thousands of candidates before committing resources to physical testing, thus reducing quantity of resources while maintaining reliability verification.