Through the adoption of strategies in computer-aided drug design, including virtual high throughput screening (VHTS), structure-based drug design (SBDD), fragment-based drug design (FBDD), and quantitative structure-activity relationships (QSAR), we're able to notably curtail the duration of project research and development. This approach amplifies our design and R&D efficiency, subsequently driving down costs.

Leveraging artificial intelligence in the design of new drugs significantly expedites the drug development process and increases the likelihood of successful outcomes. Moreover, it enables effective prediction of drug side effects such as pharmacological activity and safety profiles. The key application areas of integrating artificial intelligence into pharmaceutical research and development span from high-throughput screening for drug target prediction and drug design to forecasting crucial properties of drugs, including their absorption, distribution, metabolism, excretion, and toxicity.