NITI Aayog, the policy think tank of the Government of India, is looking at expanding its "Medicines from the Sky" project, which uses unmanned aerial systems for the delivery of vaccines in remote areas, to the North-Eastern parts of the country. It is also exploring use of emerging technologies including artificial intelligence (AI) in medical diagnostics. NITI Aayog, in collaboration with the Government of Telangana and the World Economic Forum (WEF), launched the'Medicines from the Sky' project on piloting the use of unmanned aerial systems for the delivery of vaccines in remote areas. These drone trials are focused on laying the groundwork for a drone delivery network that will improve access to vital healthcare supplies for remote and vulnerable communities. The scope includes deliveries of MMR (maternal mortality rate), flu and C-19 vaccines.

For several years we have been hearing about the potential of Artificial Intelligence (AI) to improve traditional drug discovery and development. In the last two years, clinical trials have begun. The UK's Exscientia made headlines last April by announcing the start of a Phase 1 clinical trial for a drug it designed using AI for an established protein target. Recursion Pharmaceuticals in Utah uses AI to find new uses for the drugs owned by other companies. Insilico Medicine has now announced the crucial next step: the start of the world's first Phase 1 clinical trial of a drug developed from scratch using AI.

The pharmaceutical industry is not only starting to take notice of artificial intelligence (AI), but many companies have started to future-proof their approaches to keep their competitive edge. Using GlobalData's Thematic Research ecosystem, we look at which companies are leading the field in AI. First, some background: one feature of the ecosystem is the Thematic Scorecard, which evaluates companies on how equipped they are in a certain theme, such as AI, over the next two-to-three years. This is based on their current AI activity and investment. Companies can garner a score between 1–5, with the score of 5 denoting a high AI commitment from the company.

When you ask who is "doing" AI the best, the answer is rarely a Fortune 500 Pharma company, and there are a few reasons for this. Most people think of genomics for healthcare AI applications. Another common one is drug discovery, which is very linear. According to Dr. Adam Jenkins, the linear nature of these applications can hide some of the most interesting ways that pharmaceuticals are using AI after commercialization. Post-launch, the opportunities aren't nearly as direct, but that means there could be some interesting applications.

Although the Covid-19 pandemic persisted throughout the year, the healthcare industry continued to fulfill its duty based on experiences of last year. While going all out to develop Covid-19 treatments and vaccines, the industry tried to graft new technologies, including AI, to promote the sector's development. In the process, the industry revealed problems requiring correction, such as manipulating raw materials and impurities caught in antihypertensive drugs. Still, the sector continued to improve itself in keeping with the changing global healthcare industry amid the Covid-19 crisis. Korea Biomedical Review has compiled the five biggest industry stories in 2021.

So far, the pharmaceutical industry has contributed more to the well-being of humanity than any other industry. But lately its business model has been under significant pressure since the return on R&D investment has dropped to its lowest level in decades (lack of innovation amid digital disruption, rapid technological advances and other issues such as lack of data reproducibility) and its public reputation in US and around the world (anti vaccine movement in Europe) is worse than ever. This worrisome mix of little growth potential and low reputation is the main reason why investors are increasingly worried, not to mention that the current drug development process needs a big dose of digital innovation to deal with its messy data. As a matter of fact, Stefan Oelrich member of the Board Management of Bayer AG, President Pharmaceuticals, wrote in an article -- that the title perfectly summarises the AI pharma situation "Artificial Intelligence - When we Suddenly Know What we Don't Know" -- the following: "As we open the first doors in this unknown land we start to discover how much more is out there for our entire pharmaceutical value chain spanning from research to product supply. I expect AI to help us know what we have not known so far. Artificial Intelligence will become instrumental in our search for new medicines to better serve patients around the world as we leverage Science For A Better Life".

The estimated cost for drug development by U.S. biopharmaceutical companies is nearly $ 1 billion per drug. Instead of throwing darts at the wall and hoping to land on an eventual hit--an expensive and inefficient process--pharmaceutical companies can leverage machine learning techniques to not only cull through literature and journal publications using (again) NLP but also to pre-screen for the most effective potential compounds to prioritize their time. Pfizer's researchers use natural language processing to analyze over a million articles in medical journals, 20 million abstracts of journal articles, and 4 million patents. Computational biochemistry allows drug-makers to cut out a significant portion of the test tube experiments. Instead, a computer simulates the protein and tests all of its atomic interactions.

Also: Peer Reviewing Data Science Projects; Visualizing Decision Trees with Python (Scikit-learn, Graphviz, Matplotlib); Can Java Be Used for Machine Learning and Data Science?; Mathematics for Machine Learning: The Free eBook; 24 Best (and Free) Books To Understand Machine Learning

There's a common refrain among the chronically disappointed, it goes a little something like this: "if this is the future, where is my jetpack?" Juxtaposing this longing for a retro-future against the wonder-world of ubiquitous computing, programmable cells, and renascent space exploration can make the gripe sound out-of-sorts on a cursory examination. For some people this misplaced nostalgic futurism can be remarkably persistent. This causes a tendency to cling to predictions which look quaint in retrospect, ignoring the astounding reality that nobody could have predicted. However, with deep learning for drug discovery we are now able to predict so much more!

There's a common refrain among the chronically disappointed, it goes a little something like this: "if this is the future, where is my jetpack?" Juxtaposing this longing for a retro-future against the wonder-world of ubiquitous computing, programmable cells, and renascent space exploration can make the gripe sound out-of-sorts on a cursory examination. For some people this misplaced nostalgic futurism can be remarkably persistent.