AI and ML are modifying the biotech sector, especially the drug development sector. These processes inspect big data sets quickly and accurately, thus identifying patterns and potential drug candidates more rapidly than conventional techniques. Here is a brief summary:

1. Forecasting Models: Artificial intelligence algorithms anticipate the interactions between various compounds and biological targets, aiding in the early identification of potential drug candidates during the research phase.

2. Analyzing Data: Machine learning analyses large volumes of biological and chemical data, revealing connections and information that human researchers may overlook.

3.  AI conducts virtual screening of chemical libraries, decreasing the time and expenses needed for experimental testing of each compound.

4. Drug Repurposing: Through the analysis of current medications and their impacts on different illnesses, artificial intelligence can discover fresh therapeutic applications, hastening the drug development process.

5. Tailored Treatment: Artificial intelligence-based treatment is designed to suit individual patients through the analysis of genetic, environmental and lifestyle information thereby making it more effective and reducing the risk of side effects.

In conclusion, by generally enhancing the efficacy and accuracy of drug discovery, AI and ML also shorten the period taken before new medicines are introduced into the market at lower costs and higher efficiency.