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Big Investments for Human Microbiome Research

Big Investments for Human Microbiome Research

Major companies on the scene include Second Genome, Enterome, and EpiBiome. In addition, several new startups have entered the field. Amo... read more

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Assessing Productivity of Pharmaceutical AI: Any Results Beyond Hype?

Assessing Productivity of Pharmaceutical AI: Any Results Beyond Hype?

A background context -- opportunities and challenges

Current widespread interest towards artificial intelligence (AI) and its numerous research and commercial successes was largely catalyzed by several landmark breakthroughs in 2012, when researchers at the University of Toronto achieved unprecedented improvement in the image classification challenge ImageNet, using their deep neural network “AlexNet” running on graphics processing units (GPUs), and when that same year Google’s deep neural network managed to identify a cat from millions of unlabeled Youtube videos, representing a conceptual step in unsupervised machine learning.

13 Useful Mobile Apps For Life Scientists

13 Useful Mobile Apps For Life Scientists

(Updated: 10.09.2018)

Nowadays mobile devices are ubiquitous with an estimated number of smartphones and tablet PCs to exceed two billion globally.

The availability of internet connection in most public places, powerful processors, and user-friendly touch screen technologies make mobile devices useful not only for spare time activities but also for education and science.

Specialized mobile apps are ubiquitous in the area of healthcare providing value for medical doctors, as well as patients involved in various healthcare programs and therapies. Those include various apps for assisting clinical decision making by doctors, apps for monitoring physiological parameters of patients in real time, apps for managing doctor-patient interactions, apps for self-monitoring various health conditions and physiological parameters (for example, did you know you can identify a dangerous wart on your body using your mobile phone?) etc.  

Pharma R&D Outsourcing Is On The Rise

Pharma R&D Outsourcing Is On The Rise

(Updated: 14.08.2018)

Pharmaceutical companies are increasingly outsourcing research activities to academic and private contract research organizations (CROs) as a strategy to stay competitive and flexible in a world of exponentially growing knowledge, increasingly sophisticated technologies and an unstable economic environment.  

The R&D tasks that firms choose to outsource include a wide spectrum of activities from basic research to late-stage development: genetic engineering, target validation, assay development, hit exploration and lead optimization (hit candidates-as-a-service), safety and efficacy tests in animal models, and clinical trials involving humans.

According to a recent analytical report by Visiongain, drug discovery outsourcing will continue to grow over the next decade and will rise to a $43.7 billion dollar industry by 2026, as compared to an estimated 19.2 billion in 2016 (or $21.2 billion according to Kalorama Information). This is in line with Vantage’s fresh alliance benchmarking study, revealing that over 80% of bio-pharma respondents report increased alliance activity compared to five years ago. Getting ideas and expertise from external sources is a well-established practice in the pharmaceutical industry with about one-third of all drugs in the pipelines of the top ten pharmaceutical companies initially developed elsewhere, according to a 2014 WSJ article by Jonathan D. Rockoff.  

Interviews with experts

[Interview] Adoption of AI-driven Tools By The Life Sciences Professionals: What Is Coming In 2018?

[Interview] Adoption of AI-driven Tools By The Life Sciences Professionals: What Is Coming In 2018?

The previous year was rich in discussions and events one way or another related to potential applications of artificial intelligence (AI) advances for the benefit of drug discovery and development.

(Note: For the sake of simplicity, the term “AI” will be applied herein interchangeably with terms like “machine learning” (ML), “deep learning” (DL), “neural networks” (NN) etc., although conceptually, those terms are quite different in meaning. The term AI describes a field of computer science studying how to make a computer intelligent at doing something, while terms like ”machine learning”, “deep learning”, and “neural networks” relate to algorithms and methods by which it can be achieved.).

[Interview] The Current Status Of AI In Drug Discovery And Looking Forward Into 2018

[Interview] The Current Status Of AI In Drug Discovery And Looking Forward Into 2018

Without a doubt, the area of artificial intelligence (AI) has been a sensation lately -- judging by the amount of hype around this topic. But the hype is not a guarantee of a real breakthrough, which is defined by facts and measurable achievements, not just loud statements.

The fact is, however, AI-driven systems managed to learn chess at a champion level in just 4 hours, and beat human champions in Jeopardy, and Go -- we all know that. And Facebook can recognize faces in a blurry photo where you are hanging out with a bunch of friends -- not worse than you (human) can do. You can try and see it for yourself anytime -- it works!

Editor's Pick: A Video Blog

How AI Is Applied To Metabolomics To Identify New Targets And Drugs

   by Andrii Buvailo    2593

The team at MIT created the most comprehensive database of metabolites, their interactions with proteins, protein-protein interactions, drug-protein interactions, and associations of metabolites with diseases. They then use the obtained interactions map to make inferences about the disease mechanisms and novel targets. With this new technology, the team launched a biotech startup ReviveMed in 2016 having raised $1.5M of funding so far.

Network Driven Drug Discovery Using AI

   by Andrii Buvailo    2489

“One-target-one-disease” paradigm has been around for decades, prompting numerous drug discovery programs focusing on identifying small molecules for targeting only one specific protein (or other targets) believed to be responsible for the disease mechanism.

Evaxion Biotech Uses AI To Develop Anticancer And Antimicrobial Vaccines

   by BiopharmaTrend    2504

This is an interview with Niels Iversen Møller, a CEO, and Co-founder of Evaxion Biotech -- an immuno-informatics biotech from Denmark.

Evaxion Biotech uses its machine learning-based platform to compare DNAs of tumor cells and DNAs healthy cells and identify mutations that are critical for the disease. This data is further used to design anticancer vaccines.

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