By leveraging these core technological advantages and innovations, Genion Quantum Technologies has achieved technological breakthroughs in AI-driven drug discovery, providing efficient and precise solutions for drug discovery and optimization.
AI-Driven Candidate Molecule Generation and Screening
Utilizing Transformer, Diffusion Model, and VAE+RL (Variational Autoencoder + Reinforcement Learning) for De Novo drug design, significantly enhancing drug discovery innovation while reducing R&D costs. Testing has shown that the trained model can reproduce experimentally active molecules that were not in the training set, with a reproduction rate exceeding 60%, surpassing industry competitors such as Exscientia, Insilico Medicine, and XtalPi.
AI + Quantum Simulation Optimization
Integrating Molecular Dynamics (MD) and Free Energy Perturbation (FEP) calculations to enhance drug binding stability and developability predictions. Additionally, the Genion team has pioneered an AI + Quantum Mechanics algorithm framework for precise drug molecular structure identification. Quantum mechanics-based optimization compensates for the limitations of data-driven algorithms in large models, enabling highly accurate molecular structure optimization. This technology is proprietary to Genion, has been successfully published, and is unique in the industry (Reference: Gao P, Zhang J, Peng Q, et al., 2020).
Automated AI-Experimental Closed-Loop Optimization
By incorporating experimental data feedback, AI predictive models are continuously optimized, improving the accuracy of drug clinical applications. Genion's large language model text-data algorithm enables drug combination optimization to support therapeutic decision-making and facilitate foundational clinical research. This technology significantly outperforms traditional machine learning and deep learning-based competitors, such as Tempus Labs and IQVIA, in algorithmic optimization.
LLM-Powered Molecular Generation and Chemical Knowledge Reasoning
Utilizing ChemGPT (Chemical Language Model) and ProteinBERT (Biological Language Model) for molecular optimization and multi-objective identification, while providing an interactive module for platform users. This functionality is not available in competing platforms, further enhancing the intelligence and user experience of the platform.