The first month of 2019 is not over yet, but there are already four major announcements about new ...
In this Special Perspective, our fourth in an ongoing series, we will be presenting MatchMaker™, a novel deep ...
Updated: 10.01.2019. Newly added content is marked in the text with "Update" sign. The idea of using artificial ...
Antibiotic R&D has had a particularly bad year starting with The Medicines Company who abandoned their antibiotic R&D ...
In The Spotlight
How E-commerce Reshapes Pharmaceutical R&D Market
The first month of 2019 is not over yet, but there are already four major announcements about new research projects between large drug discovery corporations and smaller artificial intelligence (AI) companies -- this is more than the number of all similar announcements in the year 2014 combined -- only three.
Over the last five years the interest of pharmaceutical professionals towards machine learning (ML) and artificial intelligence (AI) has measurably increased -- while only one “AI-related” research collaboration involving “big pharma” appeared in the news in 2013, the number of such events increased up to 21 in 2017 alone, involving some of the top pharma players like GSK, Sanofi, Abbvie, Genentech, etc.
Machine learning and artificial intelligence have become widely discussed topics in the area of life sciences and healthcare over the last several years. While a lot of pharmaceutical companies and healthcare organizations express considerable interest in possible new opportunities, associated with the use of artificial intelligence for early drug discovery, clinical trials optimization, and business intelligence, a considerable gap still exists when it comes to understanding new technologies by pharmaceutical professionals and leaders. The key questions here are these:
What machine learning / AI can and can’t do for pharmaceutical industry
What should be done to harness practical and measurable value out of machine learning / AI?
How it should be done and what are the timelines for getting returns on investments?
Interviews with experts
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.).
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
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.
“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 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.
Small Molecules Artificial Intelligence Machine Learning In Silico Biotech Startup Big Data MedChem Pharmaceutical industry trends R&D Outsourcing Database Antibiotics CROs Thought-provoking Biologics CRISPR Biopharma Policies, Regulations