The application of large-scale language models in drug research and development is constantly expanding. In the past, there were technical bottlenecks in applying natural language to molecular optimization, but the emergence of the DrugAssist model provides a new solution to this problem. The DrugAssist model enables real-time interaction between natural language and humans during the drug development process, significantly improving the efficiency and convenience of molecular optimization. Its transferability performance in single-attribute optimization and zero-sample and few-sample scenarios is particularly outstanding, bringing innovative changes to the field of drug discovery.
In recent years, large language models have made significant progress in the field of language processing, but there are challenges in molecular optimization for drug discovery. However, researchers have successfully achieved real-time interaction between natural language and humans during the molecular optimization process through the development and application of the DrugAssist model. The model performs well in single-attribute optimization and has excellent transferability in zero-sample and few-sample scenarios. , providing the possibility of real-time interaction and iterative optimization for drug discovery.
The successful application of the DrugAssist model marks the further deepening of AI technology in the field of drug research and development, providing strong technical support for accelerating the process of new drug research and development and reducing research and development costs. In the future, the continued development of similar technologies will greatly promote the progress of the pharmaceutical industry.