Certain aspects of our daily lives seem to be moving faster and faster. For example, we are not only used to software updates, we actually have come to expect a steady stream of regular improvements showing up on our phones, and in the digital tools we use at work and at home. Against that backdrop, what seems to remain stubbornly resistant to the same pattern of speed and improvement are advances in the treatment of cancer and certain diseases. Treating and curing cancer is not the same as binge-watching online, there is no debate there. But a key enabler of both--computation in the cloud--offers hope that we can crank up the speed with which we are able to deliver earlier and more accurate diagnosis, better therapies, and yes, even cures for cancers.

Increasing investments in AI indicate the benefits of applying machine learning to identify and screen potential drug candidates. 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?

Efficiently and effectively identifying high-quality drug candidates for clinical development is challenging, even for large companies like Takeda that has extensive R&D expertise and therapeutic experience in the areas of oncology, gastroenterology, and diseases of the central nervous system. To combat this challenge, Takeda, not unlike others in the industry, is looking to augment its internal drug discovery abilities with innovative capabilities from outside the company – at pharma and bio companies, as well as academic institutions around the world. The search for partners with unique drug discovery capabilities prompted Takeda to enter into an alliance with Numerate. Numerate is a software company that has developed algorithms that analyze large amounts of data and find promising unexplored chemical space for the human drug targets Takeda wants to pursue. "They [Numerate] are able to leverage publicly available data as well as our proprietary data to computationally envision new chemical space for novel therapies," says David Weitz, Head of Takeda California and Global Research Externalization for Takeda California.

EY announces the appointment of machine learning and AI authority Nigel Duffy as EY Global Innovation Artificial Intelligence Leader. As part of the EY Global Innovation Team, Dr. Duffy will lead the application of AI throughout EY, helping it to be effectively leveraged across the organization. Based in Palo Alto, California, Dr. Duffy will also be responsible for expanding and further strengthening EY's relationships with start-ups and academic and business communities worldwide. "Nigel is considered one of the world's foremost thought leaders in AI, with demonstrated leadership in applying the technology to drive real commercial value. With Nigel joining the EY Global Innovation Team, we look forward to working together to turn ideas leveraging AI into realities that help EY and our clients innovate."