Is artificial intelligence the future of drug discovery?

The applications of artificial intelligence (AI) 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.

Increasing investments in AI in drug discovery by big pharma suggest a truth behind the benefits of applying machine learning to identify and screen potential drug candidates. More and more, big pharma is partnering with AI-driven companies in hopes of more accurately predicting drug candidates and cutting R&D costs and time, prompting GlobalData to ask—Is AI the future of drug discovery?

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 Numerate’s computer-based drug design technology to develop novel small molecule drug leads for an undisclosed cardiovascular disease target.

In addition, Merck is working with Atomwise, the creator of AtomNet, which uses deep learning technology for the discovery of novel small molecules. Although the project is confidential, Merck is leveraging Atomwise’s AI-based technology to scan existing medicines that could be redesigned to fight old and upcoming diseases.

Merck is just one of many pharmaceutical companies partnering with AI-focused companies to advance drug discovery. Celgene partnered with GNS Healthcare to utilize its Reverse Engineering and Forward Simulation causal machine learning and simulation platform; GSK entered a $43M drug discovery collaboration with UK-based AI-driven startup Exscientia; Pfizer entered collaboration with IBM Watson for immuno-oncology drug discovery research, and the list goes on.

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 analyse large sets of chemical and biological data to identify drug candidates with high success rates much faster than humans.

However, GlobalData 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. That being said, GlobalData anticipates AI to transform the drug discovery process as we know it.