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Will Biologics Surpass Small Molecules In The Pharma Race?

   by Andrii Buvailo    10044
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 new era in pharmaceutical industry. Modern advances in biotechnology permit large-scale syntheses of biologics in a more or less cost-effective manner. Having once started with large peptides and recombinant proteins, biologics nowadays include a wide range of other entities, such as antibodies, monoclonal antibodies, and more recently, nanobodies and related objects, soluble receptors, recombinant DNA, antibody-drug conjugates (ADCs), fusion proteins, immunotherapeutics, and synthetic vaccines.  

The “Why”, “How” and “When” of AI in Pharmaceutical Innovation

   by Andrii Buvailo    2542
The “Why”, “How” and “When” of AI in Pharmaceutical Innovation

(Edited version of this post originally appeared in Forbes)

“It is not the strongest of the species that survives, nor the most intelligent, but the one most adaptable to change” -- Leon C. Megginson

Last year brought about new hope and even more hype around the idea of applying artificial intelligence (AI) for “revolutionizing” drug discovery research -- via machines being able to “learn” chemistry and biology from vast amounts of experimental data to propose potent drug candidates, accurately predict their properties and possible toxicity risks. It is supposed to dramatically minimize failures in clinical trials -- saving R&D budgets, time, and most importantly, lives of patients.

Top 7 Trends In Pharmaceutical Research In 2018

   by Andrii Buvailo, Alfred Ajami    26132
Top 7 Trends In Pharmaceutical Research In 2018

Being under ever-increasing pressure to compete in a challenging economic and technological environment, pharmaceutical and biotech companies must continually innovate in their R&D programmes to stay ahead of the game.

External innovations come in different forms and originate in different places -- from university labs, to privately held venture capital-backed startups and contract research organizations (CROs). Let’s get to reviewing some of the most influential research trends which will be “hot” in 2018 and beyond, and summarize some of the key players driving innovations.

2018 Brings A Surge Of Activity In The “AI For Drug Discovery” Space

   by Andrii Buvailo    6937
2018 Brings A Surge Of Activity In The “AI For Drug Discovery” Space

(Last updated: 15.03.2018)

The idea of using artificial intelligence (AI) to accelerate drug discovery process and boost a success rate of pharmaceutical research programs has inspired a notable amount of activity over the last several years with a considerable number of initiated research collaborations between AI-driven R&D vendors and top pharmaceutical companies in 2016-2017.

(For a detailed review of the topic, read Biopharma’s Hunt For Artificial Intelligence: Who Does What?).

A busy beginning of 2018 shows that the area is getting even “hotter” and things start unfolding faster in the emerging “AI for drug discovery” space. Below is a brief summary of some of the most notable events of this year so far:  

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

   by Andrii Buvailo    2289
[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.).