Robert Chang, a Stanford ophthalmologist, normally stays busy prescribing drops and performing eye surgery. But a few years ago, he decided to jump on a hot new trend in his field: artificial intelligence. Doctors like Chang often rely on eye imaging to track the development of conditions like glaucoma. With enough scans, he reasoned, he might find patterns that could help him better interpret test results. That is, if he could get his hands on enough data.
Science has always hinged on the idea that researchers must be able to prove and reproduce the results of their research. Simply put, that is what makes science...science. Yet in recent years, as computing power has increased, the cloud has taken shape, and data sets have grown, a problem has appeared: it has becoming increasingly difficult to generate the same results consistently--even when researchers include the same dataset. "One basic requirement of scientific results is reproducibility: shake an apple tree, and apples will fall downwards each and every time," observes Kai Zhang, an associate professor in the department of statistics and operations research at The University of North Carolina, Chapel Hill. "The problem today is that in many cases, researchers cannot replicate existing findings in the literature and they cannot produce the same conclusions. This is undermining the credibility of scientists and science. It is producing a crisis."
The global drug discovery market is estimated to be worth at least $35 billion, a figure that could rise to $71 billion by 2025. But taking a drug from research and development to market is a long and resource-intensive process. A large part of this work involves rigorous testing to ensure a drug is not only effective, but safe -- and this unfortunately entails animal testing, whether on monkeys, rats, mice, dogs, or rabbits. Contrary to what some may think, animal testing is not only a pivotal facet of drug development, in most countries it's actually a legal requirement that must be completed before clinical trials on humans can commence. However, animal testing is slow and expensive, with a low success rate -- it's estimated that fewer than 10% of drug candidates tested on animals make it through the pipeline.
The fountain of eternal youth could come in technological form thanks to $100 million investment in a life sciences company. Juvenescence is working with drug developers and AI experts to create treatments and technologies to treat age-related diseases and to increase human longevity. The firm, set up by London City of London entrepreneur Michael Spencer, announced a total investment of $10 million from its founders. A further $10 million each will come from four cornerstone investors, including Grok Ventures, Mike Cannon-Brookes and Mr Spencer's private investment company. This brings the total to $165 Million that Juvenescence has raised in 18 months.
High-throughput phenotypic screening, based on high content imaging, is increasingly often used as a tool in the context of drug discovery. Compound screens are used to find hits that produce the desired phenotypes in relevant cellular assays. Genomic screens are used to elucidate the underlying molecular pathways and identify suitable drug targets. Since a wealth of data is produced in the process of high- content screening, data science approaches such as statistics, machine learning and neural networks can play an important role in making the most of the collected data. Much like virtual screening can be performed in more classical chemoinformatic settings by, e.g., learning predictive models for QSAR (quantitative structure-activity relations) from data obtained through compound screens, similar approaches can be taken in the context of high-throughput phenotypic screening.
Proteins are biological high-performance machines. They can be found in every cell and play an important role in human blood coagulation or as main constituents of hairs or muscles. The function of these molecular tools is obvious from their structure. Researchers of Karlsruhe Institute of Technology (KIT) have now developed a new method to predict this protein structure with the help of artificial intelligence. This is very difficult to detect, the experiments needed for this purpose are expensive and complex.
Imagine a couple of caffeine-addled biochemistry majors late at night in their dorm kitchen cooking up a new medicine that proves remarkably effective at soothing colds but inadvertently causes permanent behavioral changes. Those who ingest it become radically politicized and shout uncontrollably in casual conversation. Still, the concoction sells to billions of people. This sounds preposterous, because the FDA would never let such a drug reach the market. Olaf J. Groth is founding CEO of Cambrian Labs and a professor at Hult Business School.
A particularly exciting subject is machine learning. The idea of cognitive artificial intelligence sounds simple yet revolutionary. However, its application at Bayer is proving to be more difficult than expected, and is not fully implemented at the present stage. Do you have an idea for a new use of machine learning in everyday work? Could machine learning perhaps improve data quality, automatic processes, or even flexible pricing strategies?
By 2021, consultant firm Frost & Sullivan expects that artificial intelligence (AI) systems will generate $6.7 billion in revenue from healthcare globally. One area that machine learning is significantly evolving is genomics--the study of the complete set of genes within an organism. While much attention has been paid to the implications for human health, genetic sequencing and analysis could also be ground-breaking for agriculture and animal husbandry. When researchers can sequence and analyze DNA, something that artificial intelligence systems make faster, cheaper and more accurate, they gain perspective on the particular genetic blueprint that orchestrates all activities of that organism. With this insight, they can make decisions about care, what an organism might be susceptible to in the future, what mutations might cause different diseases and how to prepare for the future.