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Will Biologics Surpass Small Molecules In The Pharma Race?

Will Biologics Surpass Small Molecules In The Pharma Race?

The first biologics drug, humanized insulin (5.8 kDa), became available in 1982 following the advent of biotechnology, and it marked a ne... read more

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Pharma Companies Join Forces to Train AI for Drug Discovery Using Blockchain

Pharma Companies Join Forces to Train AI for Drug Discovery Using Blockchain

The newly organized research project “MELLODDY” (Machine Learning Ledger Orchestration for Drug Discovery), involving ten large pharma companies and seven technology providers, is that kind of deals which can catalyze a transition of the pharmaceutical industry to a new level -- a “paradigm shift”, as one might refer to it in terms of Thomas Kuhn’s “The Structure of Scientific Revolutions”.

The project aims at developing a state-of-the-art platform for collaboration, based on Owkin’s blockchain architecture technology, which would allow collective training of artificial intelligence (AI) algorithms using data from multiple direct pharmaceutical competitors, without exposing their internal know-hows and compromising their intellectual property -- for the collective benefit of everyone involved. 

Toronto AI Startups Aim High in Drug Discovery Space

Toronto AI Startups Aim High in Drug Discovery Space

According to a report by the Advisory Council on Artificial Intelligence of Canada, this country is home to more than eight hundred artificial intelligence (AI) companies, and the number of AI startups is growing by about 28% each year.

This is due to quite favorable conditions, that Canada offers to local and foreign AI-focused talent and organizations. Not only the country is a strong global leader in artificial intelligence research, with some of the most cited academics working here (Yoshua Bengio and Geoffrey Hinton -- pioneers of modern AI), but also the government is quite active in pushing Pan-Canadian Artificial Intelligence Strategy, aiming at supporting AI research and talent attraction and retention in Canada.

"Hot" Research Areas in Drug Discovery - 2019

"Hot" Research Areas in Drug Discovery - 2019

Things like gene editing, stem cells, immunotherapies and new types of biologics are now mega-trends in the pharmaceutical industry, widely covered in media, and I guess there is little doubt that biology is the next big thing in medicine. However, in this post I would like to outline several hot areas in small molecule drug discovery, suggesting a lot of untapped potential and investment prospects in this more “traditional” pharmaceutical research space.

Interviews with experts

[Interview] The Rise of Quantum Physics in Drug Discovery

[Interview] The Rise of Quantum Physics in Drug Discovery

Computer-aided drug design (CADD) is a central part of so-called “rational drug design”, pioneered in the last century by companies like Vertex. Over the last decades, CADD had great influence on the way new therapeutics are discovered, however, it also showed limitations due to modest accuracy of computational tools.  

The majority of software tools used for computational chemistry and biology rely on molecular mechanics -- a simplified representation of molecules, essentially reducing them down to “balls and sticks”: atoms and bonds between them. In this way it is easier to compute, but accuracy suffers greatly.

In order to gain adequate accuracy, one has to account for the electronic behavior of atoms and molecules, i.e. consider subatomic particles -- electrons and protons. This is what quantum mechanical (QM) methods are all about -- and the theory is not new, dating back to the early decades of the 20th century.  

[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    2869

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    2658

“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    2673

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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