Computation is revolutionizing drug discovery. Advances in big chemical data, massive computing power, artificial intelligence, and molecular dynamics simulation are changing the way we develop new drugs. At 1910 Genetics, we put computation at the heart of drug discovery, blending expertise in computational chemistry, structural biology, pharmacology, genetics, data science, and software engineering to develop drugs for previously undruggable targets.

The AI-powered protein-folding model AlphaFold has predicted more than 200 million proteins, nearly all such structures known to science, DeepMind said on Thursday. Proteins are complex biological molecules produced in living organisms from instructions stored in DNA. Made from as many as 20 types of amino acids, these nano-scale chains perform vital cellular tasks to carry out all sorts of bodily functions. Knowing the three-dimensional form of proteins is important since its physical structure provides hints at how it behaves, and what purpose it serves, which helps us do things like develop drugs, and create copycat proteins for those lacking them. Some proteins are helpful, such as those involved in digesting food while others can be harmful, such as those involved in the growth of tumors. Figuring out their complicated wriggly shapes, however, is difficult.

AZoRobotics speaks with Alice Ruixue Ai from the University of Oslo about her efforts to create an artificial intelligence (AI)-based virtual screening algorithm and a cross-species Alzheimer's disease (AD) drug verification system. This system could help provide a fast, cost-effective and highly accurate method for the identification of potent mitophagy inducers to maintain brain health. Alzheimer's Disease (AD) is the most common form of dementia, seen mainly in the elderly. Around 50 million people in the world suffer from dementia, and about 70% of those people have AD, so this is a huge problem for society. It is estimated that managing the health and social costs for people with AD will cost about 2 trillion dollars by the year 2030.

A new drug created with artificial intelligence (AI) to treat patients with obsessive-compulsive disorder (OCD) will be entering human clinical trials this March – a first for AI. It has cut the drug development time from four and a half years to just 12 months, accelerating the time it typically takes to develop drugs for clinical trials. Exscientia, an Oxford-based AI start-up, collaborated with the Japanese pharmaceutical firm Sumitomo Dainippon Pharma to develop the OCD drug. It has been difficult to invent new drugs with AI that are safe and effective for humans to use. But, it has been successful with machine learning algorithms to look through data and identify which patients can benefit the most from existing medicines. AI has also been used beyond medical approaches, and has also been used to track the coronavirus outbreak and to tackle America's opioid crisis.

A lack of proper data is hurting the use of machine learning to develop drugs, which could put U.S. drugmakers at a competitive disadvantage compared to other countries, according to a report from the U.S. Government Accountability Office and the National Academy of Medicine. Machine learning is a type of artificial intelligence that involves using data to train computers to make decisions and learn from experiences, according to Pharmaphorum. It has the potential to cut costs of research and development for drugmakers by helping researchers to predict what will and won't work in clinical trials. However, the report says a lot of the data being used in drug development is not suitable for machine learning purposes. There is a phenomenon known as "garbage in, garbage out," where a machine learning system can't produce credible results because of poor data, according to Pharmaphorum.

Novartis and Microsoft plan to use artificial intelligence to develop new drugs faster, and with greater precision. Novartis and Microsoft have agreed to a five-year partnership to use artificial intelligence (AI) to develop new drugs faster, and with greater precision. Novartis CEO Vas Narasimhan said AI's potential for personalized medicine is particularly promising, as it will help classify subgroups of patients that new drugs will most likely benefit, using clinical and preclinical datasets. Narasimhan added that these datasets were consolidated from Novartis' internal information, surmounting integration obstacles. The ultimate goal of the alliance is to use AI to fuel research, development, manufacturing, finance, sales, marketing, and acquisitions.

Novartis and Microsoft announced they are joining forces to apply artificial intelligence to some of the most intractable problems in healthcare, in one of the most expansive tie-ups so far between big pharma and big tech. Under one part of the five-year agreement, which will be reviewed annually, Microsoft will work on new tools intended to make it easier to apply AI to all areas of the Swiss pharmaceutical company's business, from finance to manufacturing. A second part of the work will focus on using deep learning -- the technique that has brought the biggest recent advances in AI -- to improve the speed and precision with which it develops new medicines. Vas Narasimhan, Novartis' chief executive, suggested Tuesday that AI could hold particular promise in the field of personalized medicine, helping to identify subgroups of patients most likely to benefit from new treatments. The pharmaceutical industry, initially slow to recognize the potential of digital technologies, has been picking up the pace in recent years, with companies including GlaxoSmithKline and Sanofi exploring how big data can accelerate research and development.

Between the fake news potential of deepfakes, the fear of robots stealing jobs, and the occasional call for automated systems to have control of the nuclear button, A.I.'s public image could do with a PR makeover here in 2019. Could saving a few million lives help? That's something a new biotech pharmaceutical startup called Insilico Medicine may be able to help with. Combining genomics, big data analysis, and deep learning, the company -- which is based in Rockville in Johns Hopkins University's Emerging Technology Centers -- has been using artificial intelligence algorithms to potentially discover the next world-changing drug. Using two of the most exciting and popular A.I. techniques of the moment, it's found a way of discovering drug molecules not only far more cheaply than usual, but also much, much, much faster.

Sensyne Health, a London-based healthcare artificial intelligence (AI) company founded by biotech entrepreneur Paul Drayson, recently launched an initial public offering (IPO) on the London AIM market. Drayson's plan is to raise $78 million. It's just one of many companies that are exploiting advances in computing, data science and AI to help identify and develop potential new drugs. Drayson was formerly the UK's Minister of Science in the Department for Business, Innovation and Skills until May 2010, and before that, Minister of State for Strategic Defence Acquisition Reform at the Ministry of Defence. Drayson co-founded vaccine maker PowderJect Pharmaceuticals in 1993 and sold it in 2003 to Chiron for 524 million pounds.

Alice Zhang appeared on Forbes' 2017 list of the 30 Under 30 in Science.Jamel Toppin for Forbes Alice Zhang, 29, was a graduate student at UCLA when she decided there was a tremendous opportunity to speed up the process of drug development. "I started becoming frustrated with how drug discovery was done. I viewed it as a guessing game and largely focused on single targets. In our lab, we were finding that literally hundreds of targets were causing disease. We could start taking the guesswork out of drug discovery." This morning, Zhang's company, Verge Genomics, is announcing that it is securing $32 million in funding led by DFJ, formerly known as Draper Fisher Jurvetson.