Earlier, global big pharmacies and even big biotech companies took millions of years, and a huge amount was required in order to discover a new drug or develop new vaccines. Due to the occurrence of artificial intelligence and its generative form, this process is easier and has significantly made drastic changes while developing a vaccine.
During the COVID-19 pandemic era, the world was in a fix to find a vaccine in order to save people from such a disastrous situation. Some big as well as small life-sciences companies use AI to speed up the development process. Artificial intelligence has made rapid transitions from the manufacturing of drugs to marketing processes. The contract research, development, and manufacturing department of Dr. Reddy’s laboratories, in collaboration with Aurigene Pharmaceutical Services, has invented its own aided AI and machine learning drug platform, commonly known as ‘Aurigene AI’. AI-aided drug development often uses generative and predictive AI and even models like Computer Aided Drug Development (CADD).
In an interview, Akhil Ravi (CEO of Aurigene) said that the scientists are using big historical datasets in order to improve the probability of the success of a particular molecule. With the help of machine learning processes, molecules could be prioritized based on their synthetic tractability and processes like absorption, metabolism, distribution, and excretion. Now, the AI patterns are identifying patient needs by zeroing down molecules, which could easily fulfill those unmet needs by testing drug molecules on animals.
Artificial intelligence is giving humans the appropriate tools to make faster decisions through smart automation. The algorithms could easily depict protein genes in order to form knowledge graphs with the help of clinical datasets. Over 150 AI initiatives were involved in Merck’s life-science business. Although Karen Madden (Chief Technology Officer, Life- Sciences) often added that their teams have innovated software that combines AI with ML and CADD processes to increase the success rate of drug discovery and its development.
The predictive AI models permit scientists to identify key drug properties such as non-toxicity, solubility, and stability. However, most of the molecules were successfully developed under phase 1 trials, but it will take decades to notice new transformations under AI.