The Future of Organic Chemistry

Organic chemistry is quite a mature discipline, and it has experienced many changes since the term itself was coined. The typical definition of organic chemistry as the study of carbon compounds falls short, as modern organic chemists must often dig into more than just carbon chemistry. For example, organometallic and coordination chemistry, photochemistry, and electrochemistry to name a few of the knowledge areas that an that aid the creation of design synthetic routes towards known or new molecules.

Furthermore, organic chemistry is a science that finds itself between the limits of art and creativity, needed to solve complex retrosynthetic problems, and industrial applications, to develop molecules in gram to kilogram scales for commercialization. Indeed, if we just stop for a minute to think about how organic chemistry has evolved along the years it can be overwhelming.1

Still, there are challenges to be addressed, and solutions for these are not trivial at all. One of these challenges, and what all synthetic chemists dream about, is synthesis automation.

Although fascinating, lab work can be tedious, with lots of hours needed for setting up reactions, isolating, purifying and characterizing products. In pop culture (or maybe not that popular) there is a movie called Forbidden Planet (1956). A robot called Robby (clever name, right?), which has the ability to synthesise almost anything. Having a machine capable of synthesizing any molecule we want would be a dream come true for all organic chemists.

Currently such systems exist, although only for polymeric molecules such as oligopeptides, carbohydrates and DNA. The reason why these types of molecules are suitable for automation is that the chemistry of such systems requires simple and well-known coupling and deprotection steps. If a problem presents, usually repeating a step solves the problem. Purification is also relatively straightforward. Most of the times it consists of washing a solid support to extract the desired product.2

The case for fine chemicals is far more complicated. An automated system for those would require it to be capable of performing hundreds of chemical reactions, synthetic sequences and being compatible with thousands of building blocks, and at the same time keeping human manipulation as low as possible. There are some research groups around the world working on this problem. One of the systems in development is called AutoSyn, with a design named as Cityscape Architecture, as it resembles the skyline of a city.3

But that is only the surface, as the system consists of a sublevel with a design that looks similar to a subway (or underground if you prefer) map. Generally speaking, the surface part of the system is comprised by the reactors and separators. The subway level consists of a flow chemistry platform, where reactors and separators are connected by flow components that work as pipes. The flow of liquid phases is enabled by HPLC pumps that are able to achieve pressures between 500 to 2500 psi.3

Some pharmaceutically relevant targets have been prepared using this system. Transformations that can be tolerated by this system include amine acylation, N-alkylation of heterocycles and amides, etherification, halogenation, SNAr, imine formation, Michael addition, various SN2 displacement reactions, Grignard addition, hydrolysis, ketone epoxidation, and catalytic hydrogenation being well tolerated. The system was able to deliver from 10 – 15 mg of crude product up to the gram scale. Certainly, it will not be tomorrow when these systems are going to be widespread, but still the future looks promising.

On a different front we have the assistance of artificial intelligence (AI) in the development of programs capable of devising synthetic routes towards complex molecules. The task of retrosynthetic planning involves the best selection of reactions to prepare a complex molecule in the lowest step count possible; it is like solving a puzzle. In 2017, a team at IBM developed a methodology for the forward synthesis of molecules, where the starting materials are known. The program was taught a set of 395,496 reactions! The program, or neural network, used the information to predict the outcomes of reactions with new substrates with an accuracy of up to 80%.4

Further advances have been focused on retrosynthetic analysis of pharmaceutical relevant molecules. In 2018, Segler et. Al. developed a three-layered neural network. The first layer starts by proposing the synthesis of a molecule with the lowest number of steps, the second one evaluates the feasibility of such steps, and the third one tests the probability of each step of being successful.4

This is only a glimpse of what the future has to offer to organic chemistry. New AI algorithms, prediction tools, automated systems and robots are being developed to make the process of finding molecules that could solve today’s problems. Organic chemists are not going to be replaced anytime soon, but we have to keep in mind what the future looks like and be prepared to adapt to it.

1. https://bit.ly/2SYAwsd 2. https://bit.ly/3tTpn8L 3. https://bit.ly/3eSJyPM 4. https://rsc.li/3eQSRQr