Utilizing Transformer-based and deep learning algorithms to accurately predict molecular activity, optimize pharmacokinetic properties, and refine drug structures through quantum chemistry calculations, enhancing target binding affinity and drug-likeness.
Leveraging protein-ligand interaction prediction models to optimize peptide sequences, improving target affinity and stability. AI-driven peptide design is combined with synthetic process validation to accelerate peptide drug development.
Applying deep learning models trained on large-scale antibody databases to intelligently optimize antibody variable region sequences, enhancing affinity, stability, and druggability. This is integrated with experimental screening to accelerate antibody drug development.
Using AI to optimize nanobody structures and predict affinity for specific disease targets. Experimental screening further refines candidate molecules for preclinical research, providing innovative solutions for nanobody drug development.
Combining Transformer-based deep learning models with quantum chemistry calculations (such as DFT and FEP) to achieve high-precision molecular optimization, improving target binding affinity, selectivity, and druggability, providing a scientific basis for drug design.
Continuously optimizing AI drug design algorithms, incorporating experimental feedback to improve models, and independently advancing innovative drug pipelines. This accelerates clinical translation, providing efficient and reliable technical support for drug discovery.
Utilizing Transformer-based and deep learning algorithms to accurately predict molecular activity, optimize pharmacokinetic properties, and refine drug structures through quantum chemistry calculations, enhancing target binding affinity and drug-likeness.
Leveraging protein-ligand interaction prediction models to optimize peptide sequences, improving target affinity and stability. AI-driven peptide design is combined with synthetic process validation to accelerate peptide drug development.
Applying deep learning models trained on large-scale antibody databases to intelligently optimize antibody variable region sequences, enhancing affinity, stability, and druggability. This is integrated with experimental screening to accelerate antibody drug development.
Using AI to optimize nanobody structures and predict affinity for specific disease targets. Experimental screening further refines candidate molecules for preclinical research, providing innovative solutions for nanobody drug development.
Combining Transformer-based deep learning models with quantum chemistry calculations (such as DFT and FEP) to achieve high-precision molecular optimization, improving target binding affinity, selectivity, and druggability, providing a scientific basis for drug design.
Continuously optimizing AI drug design algorithms, incorporating experimental feedback to improve models, and independently advancing innovative drug pipelines. This accelerates clinical translation, providing efficient and reliable technical support for drug discovery.