Target-based drug discovery

This method typically starts from known disease-related genes or proteins, designing and developing drugs that can bind to them and regulate their function. This requires a deep understanding of the molecular mechanisms of diseases.

Phenotypic-based drug discovery

This method focuses on observing and analyzing the external characteristics (i.e. phenotypes) exhibited by diseases in organisms and then searching for drugs that can improve these phenotypes. This method relies more on high-throughput screening techniques.

Structure-based drug discovery

This method utilizes computer simulation and molecular modeling techniques to predict the interaction between drug molecules and target proteins, thereby designing and optimizing drug structures. This method typically requires advanced computing techniques and specialized software tools.

Drug discovery based on high-throughput screening

This method utilizes automated equipment and technology to quickly screen the activity of a large number of compounds to find potential therapeutic drug candidates.

Drug discovery based on artificial intelligence

In recent years, with the rapid development of artificial intelligence technology, its application in drug discovery has become increasingly widespread. Algorithms based on machine learning and deep learning can help researchers analyze and predict the biological activity of compounds more effectively, accelerating the process of drug discovery.