Molecule Design Model Using Pseudo-Matched Pairs for Property Tuning

The patent replaces traditional chemical synthesis methods with a machine learning-based computational system. The molecule design computation model uses iterative training with pseudo-matched molecule pairs to generate optimized molecular structures, substituting wet-lab chemical processes with in-silico computational design. This enables precise control over molecular properties through algorithmic optimization rather than trial-and-error synthesis.

The system employs iterative training that progressively optimizes molecular parameters by generating pseudo-matched molecule pairs with improved binding affinity, specificity, and developability. Each iteration refines the molecular structure by making targeted parameter changes based on learned patterns from training data, enabling systematic enhancement of multiple properties simultaneously.

The training process is segmented into distinct phases: initial model training on matched datasets, generation of pseudo-matched molecule pairs, and iterative refinement cycles. The molecule design computation model is divided into separate components including the encoder for processing input molecules and the decoder for generating optimized structures. This segmentation allows each component to be optimized independently while contributing to overall reliability.

The system employs self-service mechanisms where the model generates its own training data through pseudo-matched molecule pair creation. The iterative process automatically identifies areas for improvement and generates corresponding training examples without external intervention, enabling the model to self-optimize its performance for binding affinity and specificity while managing training complexity through autonomous learning.