The immense Potential of AI in Drug Discovery
Artificial Intelligence (AI) has become a hot topic in the pharmaceutical industry, and nearly every pharma company in the world is embracing the technology.
Artificial Intelligence (AI) has become a hot topic in the pharmaceutical industry, and nearly every pharma company in the world is embracing the technology. The hope is that AI will play a significant role in speeding up drug discovery, reducing R&D costs and avoiding failure.
- Pharma companies are looking at AI technologies such as machine learning and deep learning to accelerate drug discovery and reduce costs.
- AI can identify patterns from data and help scientists determine which drugs are most effective in the treatment of specific diseases.
- Though still in infancy, AI is giving pharma companies and investors great promise.
According to published reports, the market opportunity for AI-driven solutions to accelerate drug discovery will likely reach $10B by 2024. The drug discovery process is quite lengthy, and it may take years to for a new medicine to hit the market. According to a survey, it takes about a decade of research and $2.6 billion to drive an experimental drug from lab to market. In addition, because of concerns over healthcare safety, only about 5 percent of experimental drugs make it to market. The desire for faster product-to-market is driving pharma companies to invest full throttle in AI technologies.
Successful drug discovery relies on the interaction of a molecule and the target disease. Small teams of scientists test each case of target disease with a molecule in the hope of finding a successful interaction. This is a time-consuming process with a high failure rate. AI technology can solve these problems by researching rates much faster than humans. Successful interactions between a target disease and molecules can be identified in a fraction of the time, thus saving costs and improving the success rate of drug trial.
The success of AI in drug discovery is chiefly due to Deep Learning, which is a part of Machine Learning that is built using artificial neural networks. These artificial neural networks model the way neurons in the human brain talk to each other. Deep learning technology can train systems to analyze large sets of raw data to identify potential drugs with high success rates and much faster than humans. With this huge volume of data, AI can help scientists generate a large number of possible hypotheses.
The potential for AI to reduce medical R&D spend and improve the quality of healthcare is attracting not only pharma companies and investors, but also tech giants. As a result, a growing number of AI vendors are expected to pitch in, conduct extensive research and offer innovative solutions for use cases and collaboration models. This means the R&D outsourcing market for AI, cloud and big data technologies will grow even faster in 2018.