Generative Adversarial Network for Drug Screening

The patent replaces traditional simulation software and expert experience (mechanical/manual systems) with an AI-based generative adversarial network system. The GAN automatically generates and optimizes compound structures through machine learning, eliminating the need for time-consuming simulation software operations and expert manual analysis, thereby reducing design time while improving success rate through automated intelligent optimization

The patent changes the optimization parameters by using AI-generated compound structures with optimized molecular properties instead of traditional simulation parameters. The system learns from training data to generate compounds with desired pharmacological properties, transforming the optimization process from manual parameter adjustment to automated parameter optimization based on learned patterns, thus improving both speed and success rate

The patent performs preliminary action by pre-training the generative adversarial network with extensive drug structure and property data before actual drug development. This pre-training phase allows the AI system to learn optimal compound structures and properties in advance, so that during the actual drug development process, the system can quickly generate and evaluate candidate compounds without requiring time-consuming traditional testing and evaluation for each new compound

The patent uses copying by generating virtual copies of drug compounds through the GAN model. Instead of physically synthesizing and testing every possible compound variant, the system creates digital representations and evaluations of candidate compounds, allowing comprehensive evaluation of multiple variants simultaneously in silico before selecting the most promising candidates for physical testing, thereby reducing both time and cost while maintaining evaluation accuracy