Artificial Intelligence in Drug Discovery

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Much of the existing hype in biotech has concentrated around the promise of revolutionising drug discovery. After all, the last decade was a so-called golden age in the field. From 2012 to 2021, compared to the prior decade, an increase of 73% new medicines were approved — 25% more than the one before that. These medicines include immunotherapies for cancer, gene therapies, and, of course, Covid vaccines. On the face of it, the pharmaceutical industry is doing well. 

But there are increasingly worrying trends. Drug discovery is becoming prohibitively expensive and risky. As of today, it costs between $1bn-$3bn on average and 12–18 years to bring a new drug to market. Meanwhile, the average price of a new medicine has skyrocketed from $2k in 2007 to $180k in 2021. 

When Not To Use a Graph Database

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The use of graph databases has grown massively in recent years, and they are becoming promising solutions for organizations in any industry. Their increased flexibility makes it easier to leverage relationships and connections in a way that traditional relational databases can't do. But how do you know when to use a graph database? In this article, we explore what to consider if you’re thinking of using a graph database and show how the best approach may be to not use one at all.

What Is a Graph Database?

A graph database is a type of database that uses graph theory as the foundation for its data model. Graph databases consider connectedness as a first-class citizen, making them better suited to represent connected data than more old-school relational databases.