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. 

The Top 3 Challenges Facing Engineering Leaders Today—And How to Overcome Them

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It's no secret that a tech startup is only as good as its engineering team. It's tempting to follow that statement by saying an engineering team is only as good as its talent. That's true, to some extent, but it takes expertise and leadership to get the most out of your talent—and, of course, to identify and recruit the right employees in the first place. To use the most obvious analogy, even an all-star soccer team is unlikely to win a championship without a savvy coach at the helm. Similarly, even the most stacked engineering team (pardon the pun) risks falling short without a smart and persistent leader.

As the engineering lead at a growing up startup, I've experienced success and navigated numerous challenges. While a leader's role is multifaceted, steering the ship through difficult times is one of the most critical Aspects. So let's take a look at the three biggest challenges I've encountered and how they can be overcome.

Data Mesh vs. Data Fabric: A Tale of Two New Data Paradigms

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Data is one of the most critical components of any business, as it allows us to personalize and customize our products for potential consumers. Yet, as important as data is, studies have shown that about 50‑70% of data collected by organizations goes unused and becomes what Gartner calls Dark Data. We can attribute this large amount of unused data to the inefficiencies in the systems that manage them.

This post discusses how methods like Data Meshes and Data Fabrics, which have emerged in the past decade, can help mitigate the problems associated with data management.