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8 Startups Using Quantum Theory To Accelerate Drug Discovery

8 Startups Using Quantum Theory To Accelerate Drug Discovery

Molecular mechanics (MM) is a traditional computational approach when it comes to modeling in synthetic organic chemistry, medicinal chem... read more

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"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.

Antibiotic Research and Development - Public vs. Private Funding

Antibiotic Research and Development - Public vs. Private Funding

Over the past decade, pull incentives as a solution to the broken antibiotic market have been proposed to entice companies into antibiotic research and development.  These incentives would essentially provide a market, and therefore a return on investment for pharmaceutical companies. Almost all of today’s inadequate antibiotic pipeline is provided by biotech and small pharma.  All are threatened with loss of investor interest because of the failed marketplace and many are experiencing difficulty in raising funds either from public or private markets.  One alternative to providing money to the “evil” pharmaceutical industry via a substantial pull incentive is to create publicly funded non-profit organizations or public-private ventures that would essentially replace the industry in antibiotic research, development and commercialization. Two proponents of this approach are Lord Jim O’Neill (of the O’Neill Commission or Antimicrobial Resistance Review fame) and Ramanan Laxminarayan of the Center for Disease Dynamics, Economics and Policy and of GARDP. Both, clearly, are key thought leaders in the area. 

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.

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    2767

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    2596

“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    2617

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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