Subscribe Become an Author Log In

Decisions supported by knowledge

White Papers And Industry Reports

Trends, opportunities and the future of drug discovery

3 Ways Big Data and Machine Learning Revolutionize Drug Discovery

   by Andrii Buvailo    8762

The Internet media is trending now with numerous mentions of “big data”, “machine learning” and “artificial intelligence” all together destined to revolutionize pharmaceutical and biotech industries and the way drugs are discovered. These new technologies are believed to make drug discovery cheaper, faster, and more productive.

But how is “magic” supposed to happen, after all?

The Time for Breakthroughs in Antibiotics: 10 Biotech Startups Fighting Bacterial Resistance

   by Andrii Buvailo    4347

Since a revolutionary discovery of penicillin in 1928 by Scottish bacteriologist and Nobel laureate Alexander Fleming, numerous inventions of new antibiotic classes followed which brought medicine to a new level allowing to provide humans with unprecedented protection against deadly infections, used to kill millions of people in the previous centuries.

Cross-Coupling Hits The Big Time Again

   by Andrii Buvailo    3461

Recently, a series of papers emerged in scientific press significantly expanding capabilities of classical carbon-heteroatom cross-coupling reactions making them simpler, cheaper, and efficient across a much broader range of substrates.

Cross-coupling reactions are widely used by medicinal chemists since they offer a suitable way of creating new carbon-carbon, carbon-oxygen, carbon-sulfur and carbon-nitrogen bonds, which are the staple of modern drug development. Despite tremendous progress in optimizing C-C bond-forming reactions (most notably, Negishi, Suzuki–Miyaura, Stille, Kumada and Hiyama couplings), the use of carbon-heteroatom couplings was rather limited in practice. Fortunately, a major progress has been made recently in this direction.

What Chemicals Are Popular Among Medicinal Chemists?

   by Andrii Buvailo    1946


Increasing the success rate of high-throughput screening (HTS) and the quality of the resulting hits as well as developability of drug candidates is among the key challenges of small molecule drug discovery programs.

The above goals are associated with so called “compound quality” or “drug-likeness” of the starting small molecules. While the majority of considerations in this context is related to  lead-like properties, physicochemical properties, diversity, effective coverage of the chemical space, privileged structures for drugs or structures possessing favourable physical properties or metabolic stability, it was shown in a recent analysis by F.W. Goldberg et al. (2014) that an effective and somewhat overlooked strategy to increase the compound quality is to focus closer on the choice of the building blocks used in the course of drug discovery programs.  The nature of the selected building blocks determines not only the speed of the research but also the quality of the resulting drug candidates and their potential in further trials.

Obviously, some reagent classes are more popular than others due to historically synthetic routes adopted by organic and medicinal chemists.