Drug discovery has always been challenging; today, more so than ever. While there has been the success in addressing many diseases, others remain intractable.
The applications of artificial intelligence in healthcare are numerous, with the potential to transform key aspects of the industry, such as drug discovery. For many pharmaceutical companies, machine learning is the most important aspect of AI, with the potential to allow machines to ultimately surpass the intelligence levels of humans.
There is a need and opportunity to explore new drug discovery approaches that harness immense datasets (public and private), which have been built upon the successes and failures of the past to guide in-silico approaches to new therapies. Advances in genetics and molecular biology have revealed potential new targets for developing medicines. Deciding which target to pursue is challenging and an area in which there is an opportunity to increase productivity. We want to move to a highly efficient model that utilizes emerging technologies and harnesses the potential of big data
An enormous figure looms over scientists searching for new drugs. The estimated US$2.6-billion price tag for developing a treatment. A lot of that effectively goes down the drain, because it includes money spent on the nine out of ten candidate therapies that fail somewhere between phase I trials and regulatory approval.
Few people in the field doubt the need to do things differently. The success of AI in drug discovery is largely due to deep learning, a field of machine learning that is built using artificial neural networks that model the way neurons in the human brain talk to each other. This technology can train systems to analyze large sets of chemical and biological data to identify drug candidates with high success rates much faster than humans.
Pharmaceutical giant Merck is one such company taking a lead in implementing AI-based solutions in drug discovery. Merck entered the AI space early, in 2012, striking a partnership with Numerate, an AI-based company leveraging algorithms and cloud computing to transform the drug design process. The collaboration was initially developed for Merck to utilize Numerates computer-based drug design technology to develop novel small molecule drug leads to an undisclosed cardiovascular disease target.
Industry evaluation on Discovery of drug using AI
Industry data shows that more than 1,100 medicines and vaccines to treat cancer alone are currently in trials. While this wealth of development could mean substantial positive change for cancer patient outcomes, it also presents the challenge of finding solutions to make the most of the large volumes of resulting data. That is where biologists and researchers need to closely cooperate with information scientists to explore the potential of enhancing insight, development and learning through AI and machine learning.
Increasing AI investments in drug discovery by big pharma companies suggest the industry has not only woken up to but is actively embracing the benefits of machine learning to identify and screen drugs, more accurately predict drug candidates and cut R&D costs and time.
Conclusion
However, Global Data emphasizes the importance of data scientists and developers monitoring the performance of these systems and taking steps to adjust or train the system to avoid repeating any errors, such as security breaches. Global Data anticipates AI to transform the drug discovery process as we know it.
