How AI is Changing Chemical Discovery
While engineering, finance, and commerce have profited immensely from novel algorithms, they are not the only ones. Large-scale computation has been an integral part of the toolkit in the physical sciences for many decades – and some of the recent advances in AI have started to change how scientific discoveries are made.
There has been a lot of excitement about prominent achievements in the physical sciences, like using machine learning to render an image of a black hole or the contribution of AlphaFold towards protein folding. This article will cover some of the more prominent usages of AI in chemistry, the parent discipline of the aforementioned protein folding problem.
One of the chief goals of chemistry is to understand matter, its properties, and the transformations it can undergo. Chemistry is what we turn to when we are looking for a new superconductor, a vaccine, or any other material with the properties we desire.
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.
So how is AI enabling this shift? One particular development is the application of machine learning to material discovery and molecular design – two core problems in chemistry.
In the traditional approach, molecules are designed in roughly four stages, as outlined in the figure below. It is important to note that each stage can take years and many resources with no guarantee of success.
The discovery stage relies on theoretical frameworks that have been developed over centuries to guide molecular design. However, when looking for materials that are “useful” (e.g. vaseline, Teflon, penicillin), we must remember that many of them come from compounds commonly found in nature. Moreover, the utility of these compounds is often discovered after the fact. The opposite of that – targeted search – is an endeavor that would require much more time and resources (and even then, one would likely have to use known “useful” compounds as starting points). To give the reader some perspective, it has been estimated that the pharmacologically active chemical space (i.e. the number of molecules) is 1060! Manual search in such a space would take enormous time and resources, even before the testing and scaling phases.
So how does AI come into all of this, and how is it accelerating chemical discovery?
First, machine learning has improved existing methods of simulating chemical environments. We have already mentioned that computational chemistry allows us to partly bypass lab experiments. However, computational chemistry calculations simulating quantum mechanical processes scale very poorly in both computational cost and accuracy of the chemical simulation. The underlying core problem in computational chemistry is solving the electronic Schrödinger equation for complex molecules – that is, given the positions of a collection of atomic nuclei and the total number of electrons, calculate the properties of interest.
