As mentioned in our post, ‘13 Useful Mobile Apps For Life Scientists’, mobile devices have become widespread, and ...
Artificial intelligence (machine learning and deep learning, to be more specific) has become widely discussed topics in the ...
Variational AI is a newly formed artificial intelligence (AI)-driven molecule discovery & drug design startup out of Vancouver, ...
Artificial intelligence continues to expand into more aspects of human life on a weekly basis. The field is ...
SMi group presents the launch of the inaugural Disruptive Technologies in Pharma conference taking place in London on 20th-21st January, 2020.
As new technologies are being incorporated into pharma it is crucial, we reflect on trends in cutting-edge innovation and explore new tech-enabled opportunities to cut costs and save time.
Discover the AI methods & tools set to revolutionize healthcare, pharmaceuticals, medicine & diagnostics, as well as industrial applications and key insights. Hear from industry-leading experts on the latest technological advancements and business use cases for 2020!
The Future is Now
The UK’s most forward thinking event, dedicated to exhibiting technology which is really just beginning to be introduced across hospital services, this truly is a pioneering event that will shape the future of healthcare.
Co-located with the Medical Imaging Convention and the Oncology Convention, join over 4000 medical professionals at the UK’s leading medical event for AI & Machine Learning.
Pioneered by expensive and cumbersome legacy electronic data interchange (EDI) systems, the B2B e-commerce market has been evolving, showing a staggering growth rate with a projected volume of $1.1 trillion in the U.S. alone and $6.7 trillion globally by 2020.
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.
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.
![[Interview] This Vancouver-based Startup Plans To Boost Drug Design With AI](/files/blog/20191106_154805.5679_Handol_Kim_1200x886_2.png)
Variational AI is a newly formed artificial intelligence (AI)-driven molecule discovery & drug design startup out of Vancouver, British Columbia, Canada. The company has developed Enki, an AI-powered small molecule discovery service.
The founders of Variational AI are planning to build on top of their state-of-the-art expertise in machine learning, reflected in more than 40 research publications, including those presented at NIPS/NeurIPS, ICML, ICLR, CVPR, ICCV, and other top events in the area of artificial intelligence research.
![[Interview] The Rise of Quantum Physics in Drug Discovery](/files/blog/20190715_222113.7097_Enric_Pharmacelera.png)
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?](/files/blog/20180619_125427.7395_ed-addison.jpg)
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.).
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.
In this interview, Dr. Jackie Hunter, CEO at BenevolentAI, provides insights into how the company’s AI-driven platform helps scientists create and validate complex research hypotheses faster and more efficiently.
“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.
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.
Artificial Intelligence Small Molecules Machine Learning In Silico Biopharma news Biotech Startup Big Data Pharmaceutical industry trends MedChem Database Antibiotics R&D Outsourcing Thought-provoking CROs Microbiome Drug Targets
