I guess that if you currently work in an industrial setting (and probably in Academia too) I’m sure that you have come across the concept of unconscious bias, whether it be in recruitment, team selection etc. Well I guess I’m subject to this just as much as anyone else. Probably more so.  In order to redress the balance then, in this newsletter, I thought I should, in the spirit of democracy, let the readers dictate the choice of article. So I chose the 3 most downloaded articles from the last 18 months of Drug Discovery Today in the broad field of “Informatics” purely on the basis of the number of downloads. Such a cathartic exercise. Hope that you enjoy them, clearly many of you already did.

 

The first article is by Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona and Thomas Blaschke from the AstraZeneca and is entitled: “The rise of deep learning in drug discovery”. Given the current interest in the adoption of artificial intelligence into drug discovery, it is perhaps not surprising to see the interest in this, and similar articles. The authors concentrate in this article on deep learning which, they claim, has shown superior performance to alternative algorithms in other fields. Although initially this approach concentrated predominantly on the prediction of compound bioactivity it has shown significant promise in other areas impacting the Pharmaceutical industry and seems likely to be incorporated into many stages of the discovery value chain.

 

Next, we feature the article: “Machine learning in chemoinformatics and drug discovery” by Yu-Chen Lo, Stefano E. Rensi, Wen Torng and Russ B. Altman of The Department of Bioengineering, Stanford University, Stanford, CA, USA. Although the field of chemoinformatics predates, somewhat, the recent explosion in big data available to the chemist has necessitated its incorporation in order to derive information from large compound activity data sets. Moreover, the ability to mine and design drugs has illustrated its potential in modern drug discovery. The authors outline basic principles in the adoption of machine learning techniques in drug discovery and QSAR; they highlight the benefits and limitations of current technology of this approach in drug discovery.

 

The final article from Ola Engkvist, Per-Ola Norrby, Nidhal Selmi, Yu-hong Lam, Zhengwei Peng, Edward C. Sherer, Willi Amberg, Thomas Erhard and Lynette A. Smyth, scientists from AstraZeneca, Merck and AbbVie entitled: “Computational prediction of chemical reactions: current status and outlook”. They outline how the ability to predict chemical reactions is of the utmost importance for the pharmaceutical industry. Recent trends and developments are reviewed for reaction mining, computer-assisted synthesis planning, and QM methods, with an emphasis on collaborative opportunities.

 

Steve Carney was born in Liverpool, England and studied Biochemistry at Liverpool University, obtaining a BSc.(Hons) and then read for a PhD on the Biochemistry and Pathology of Connective Tissue Diseases in Manchester University, in the Departments of Medical Biochemistry and Histopathology. On completion of his PhD he moved to the Kennedy Institute of Rheumatology, London, where he worked with Professor Helen Muir FRS and Professor Tim Hardingham, on the biochemistry of experimental Osteoarthritis. He joined Eli Lilly and Co. and held a number of positions in Biology R&D, initially in the Connective Tissue Department, but latterly in the Neuroscience Department. He left Lilly to take up his present position as Managing Editor, Drug Discovery Today, at Elsevier. Currently, he also holds an honorary lectureship in Drug Discovery at the University of Surrey, UK. He has authored over 50 articles in peer-reviewed journals, written several book chapters and has held a number of patents. On the media front, Dr. Carney has been busy on some hush-hush projects that will be reported on later in the year.