How AI is Changing Chemical Discovery
22nd March 2022
Traditionally, we think of chemistry being done in a lab with test tubes, flasks, and gas burners. But it has also benefited from developments in computing and quantum mechanics, both of which rose to prominence in the early-mid 20th century. Early applications included using computers to help solve physics-based calculations; by blending theoretical chemistry with computer programming, we were able to simulate (albeit far from perfect) chemical systems. Eventually, this vein of work grew into a subfield now called computational chemistry. The subfield started to gain momentum in the 1970s and was featured in the Nobel Prizes of 1998 and 2013. Even so, while computational chemistry has gained more and more recognition over the past few decades, its importance has been largely overshadowed by that of lab experiments – the cornerstone of chemical discovery.
However, with current advancements in AI, data-centric techniques, and ever-growing amounts of data, we might be witnessing a change where computational approaches are used not just to assist lab experiments but to guide them.
The number of possible chemical compounds is as vast as the number of possible English sentences.