Intelligent Molecules: Applications of Machine Learning in Chemistry

Machine learning is an aspect of computing that falls under artificial intelligence that allows information to be collected, digested, and used in the future. There are two types of machine learning: supervised and unsupervised, which differ in if the answer that we will get is known or not already.  Machine learning is used primarily in computers and online systems to save preferences and tailor the experience to the human user. In those application, the “machine” acts as if it has a brain and remembers information so it can regurgitate it later in a situation in which it would make sense, as people do in conversations.

However, not all applications of artificial intelligence must be in computers or online systems. Computer scientists and biological engineers at Harvard have been trying to apply machine learning to molecules that could eventually “that can automatically detect, diagnose, and treat a variety of diseases using a cocktail of chemicals.”

The mathematics and biology necessary to input machine learning into molecules.

Nanotechnology has become a larger focus more recently, so it would be reasonable for the next step in machine learning to move from the macroscopic to the microscopic level, and to biological systems. Currently,  therapeutics do not change in response to the body, they only carry out their predetermined task. If drugs could respond to the body, human error in diagnosis and treatments would be reduced. In research, molecules would be able to relay better data would be able to be collected and characterized better than chemical reactions can be now, since it is difficult to study reactions in situ.

In the future, I believe machine learning implemented in molecules could revolutionize therapeutics for mental illnesses such as bipolar disorder, which changes more frequently than other mood disorders such as depression. Researchers could always use extra help, why not have the molecules and biological systems they are studying do just that?

More biologically and chemically relevant applications of technology can be found at the Wyss Institute which contributed biological engineers to the project of implementing machine learning into molecules.

Sources:

Article: Perry, C. (2013). Programming smart molecules. Harvard.

Picture: Napp, N., Adams, R. P. (2013). Message Passing Inference Networks with Chemical Reaction Networks. NIPS Proceedings, 1-9. https://www.seas.harvard.edu/news/2013/12/programming-smart-molecules