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Covering emerging technologies, innovations, and companies

Small Molecules


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

   by Andrii Buvailo    537
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. 

"Hot" Research Areas in Drug Discovery - 2019

   by Andrii Buvailo    674
"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.

Confluence of Technologies Can Bring “Virtual Pharmacology” to the Next Level

   by Andrii Buvailo    1213
Confluence of Technologies Can Bring “Virtual Pharmacology” to the Next Level

In 1970-80s, the idea of virtual screening was regarded as a conceptual way to substitute costly and time-consuming experimental “screen-everything-you-have” approaches with a much faster and cheaper predictive modelling to cherry-pick only the best molecules for subsequent synthesis and validation in a lab. A great number of computational tools and approaches emerged, aiming at “pre-screening” new promising molecules, so called “hits”, or augmenting experimental screening programs to optimize efforts.

Presenting a New Paradigm for Drug Discovery: Combining Computational Biophysics and AI through MatchMaker

   by Naheed Kurji    879
Presenting a New Paradigm for Drug Discovery: Combining Computational Biophysics and AI through MatchMaker

In this Special Perspective, our fourth in an ongoing series, we will be presenting MatchMaker™, a novel deep proteome screening technology that we have developed and validated over the past 2 years to identify DTIs. MatchMaker builds on Cyclica’s passions of combining protein, chemistry, and genomic data, and augmenting it with high performance computing and algorithm development supported on the cloud.

ConstruQt - a Reliable Molecular Structure Predictor in the Cloud

   by Peter Jarowski    591
ConstruQt - a Reliable Molecular Structure Predictor in the Cloud

Since August Kekulé’s proposal for the tetrahedral configuration of carbon or his more famous realization that benzene was a cyclic molecule, a snake biting its tale, molecular structure has been the leading consideration for the design of new molecules as drugs or performance materials. For the former, it is said that 70% of drug design is based on molecular shape with the remainder attributed to electrostatic or non-bonded interactions.

Structural chemistry began around the 1860 with these dual assignments by Kekulé but it wasn’t until one hundred years later with Allinger’s initial force field approaches that the first classical molecular mechanics (MM) models became available to make computer-assisted prediction of molecular structure. These models themselves are based on principles derived by Robert Hooke, a contemporary of Isaac Newton, in the mid 17th century with additional layers from van der Waals (19th century) etc.