Medicinal chemistry, structural biology, synthetic chemistry, machine learning, protein engineering, nanomedicine, biomedicine, and chemical design — these are just some of the fields in which we found notable discoveries during 2018.

Let’s start with a trend we noticed from the chemistry, synthetic chemistry and Machine Learning fields.

Throughout 2018, chemists put on display a machine learning algorithm that could predict molecular properties and plan reactions.

As reported by Chemical & Engineering News, Heather Kulik of the Massachusetts Institute of Technology and her colleagues identified inorganic molecules (particularly, spin-crossover complexes) that could prove useful as sensors or electronic switches.

Could we be on the verge of electric senses — like sight, smell, and more? Maybe so…Chemical company Symrise teamed up with IBM to investigate novel fragrances with machine learning.

Kendall N. Houk, a computational chemist at the University of California, Los Angeles, told CEN that Dr. Kulik’s paper “fits into contemporary excitement about the use of machine learning.”

The SLAC National Accelerator Laboratory’s Apurva Mehta, along with collaborators such as Chris Wolverton of Northwestern University and Jason Hattrick-Simpers of NIST, used machine learning to identify new alloys that are metallic glasses.

In addition, Thomas F. Miller and co-workers at the California Institute of Technology demonstrated how machine learning assists chemical modeling by predicting electronic properties of molecules.

As seen in CEN’s 2018 Year in Chemistry summary, Adrian Roitberg of the University of Florida’s machine-learning-based tool that calculates molecular forces and energies — both with high accuracy and low computational cost.

“I think it’s an excellent idea, and looks promising, but it can be harder than it looks to make it a general-purpose tool,” said Kieron Burke, a computational chemist at the University of California, Irvine.

Synthetic chemist Abigail G Doyle of Princeton University and her colleagues worked with collaborators at Merck & Co. to increase the yield of a particular animation reaction, as reported in EurekAlert. Their algorithm was programmed to carry the reagents used.

Bartosz Grzybowski of Ulsan National Institute of Science and Technology and the Polish Academy of Sciences used his synthesis-planning for molecule pathways software, called Chematica, to find that the program charted routes to products similar to those humans have developed.

The University of Toronto’s Alán Aspuru-Guzik, along with a group of chemists, applied machine learning software capable of independently running experiments, using what they found to improve the procedures.

Clearly, machine learning is playing an increasing role in chemistry, synthetic chemistry, and science in general. Indeed, technology is playing an increasing role in scientific inquiry overall.