Abhinav Kumar 1
, Jurgen Swienty-Busch 1
, Thomas Bottjer 1
, Ivan Krstic 1 

Synthetic organic chemistry is one of the cornerstones of the drug discovery process and is crucial
for rapid navigation of increasingly complex druggable chemical space. It is also critical in
determining discovery cycle times and development speeds associated with drug candidates
(Lowering et al. J. Med. Chem., 2009 & Nadin et al. Angew. Chem., Int. Ed., 2012). The knowledge
and experience of researchers has always been the key to combining chemical reactions into
successful synthetic schemes. 

However, chemical synthesis and route design is still a significant challenge. Merck quotes that 55%
of the time, a benchmarked reaction fails (Science 02, 2015). Therefore, a radical and innovative step
change in synthesis is needed. Computer-aided retrosynthesis would be a valuable tool but at
present it is slow and provides results of unsatisfactory quality. Further to this software’s that devise
effective schemes for synthetic chemistry have depended on the input of rules from researchers. 

Waller and Segler et al. (Nature, March 2018) in collaboration with Elsevier have developed a ‘deep
learning’ computer program that produces blueprints for the sequences of reactions needed to
create small organic molecules, such as drug compounds. This artificial-intelligence tool has digested
nearly every reaction ever performed (> 15 million) and has the potential transform the way how
synthetic chemists work in the future. Segler & Waller et al. tested the synthetic routes that the
program threw up in a double-blind trial with 45 organic chemists from two institutes in China and
Germany and the routes have proven scientifically sound and robust. 

Increasing the success rate in synthetic chemistry would have a huge benefit in terms of speed and
efficiency on drug-discovery projects, as well as cost reduction. Reaxys-PAI Predictive Retrosynthesis
solution developed in collaboration between Elsevier and Waller and Segler et al. is a first-in-class &
best-in-class solution combining unparalleled Reaxys content with AI & ML technologies. It deploys
next generation technologies to augment chemical synthesis and chemist knowledge helping drive
innovation, time and cost savings. The Reaxys predictive retrosynthesis tool will be an invaluable
assistant for the chemist who wants to make molecules as quickly and as confidently as possible. 

Affiliations: 1 Elsevier Information Systems GmbH, Frankfurt, Germany 

*Reaxys is a trademark of Elsevier Limited and PAI is Pending AI Pvt. Ltd.