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Artificial Intelligence in Drug Discovery

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. 

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When Not To Use a Graph Database

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.

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Comparing Grakn to Semantic Web Technologies — Part 2/3

This is part two of Comparing Semantic Web Technologies to Grakn. In the first part, we looked at how RDF compares to Grakn. In this part, we look specifically at SPARQL and RDFS.

SPARQL

What Is SPARQL?

SPARQL is a W3C-standardised language to query for information from databases that can be mapped to RDF. Similar to SQL, SPARQL allows to insert and query for data. Unlike SQL, queries aren’t constrained to just one database and can be federated across multiple HTTP endpoints.

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Comparing Grakn to Semantic Web Technologies — Part 1/3

This article explores how Grakn compares to Semantic Web Standards, focusing specifically on RDF, XML, RDFS, OWL, SPARQL and SHACL. There are some key similarities between these two sets of technologies - primarily as they are both rooted in the field of symbolic AI, knowledge representation and automated reasoning. These similarities include:

  1. Both allow developers to represent and query complex and heterogeneous data sets

  3. Both give the ability to add semantics to complex sets of data
  4. Both enable the user to perform automated deductive reasoning over large bodies of data

However, there are core differences between these technologies, as they were designed for different types of applications. Specifically, the Semantic Web is built for the Web, with incomplete data coming from many sources, where anyone can contribute to the definition and mapping between information sources. Grakn, in contrast, wasn't built to share data over the web, but instead to work as a transactional database for closed world organizations. Because of this, comparing the two technologies sometimes feels like comparing apples to oranges.