The Experimentalist's Guide to Machine Learning for Small Molecule Design

Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML algorithms have accelerated scientific developments across a variety of research topics. The field of small molecule design is no exception, and an increasing number of researchers are applying ML techniques in their pursuit of discovering, generating, and optimizing small molecule compounds. The goal of this review is to provide simple, yet descriptive, explanations of some of the most commonly utilized ML algorithms in the field of small molecule design along with those that are highly applicable to an experimentally focused audience. The algorithms discussed here span across three ML paradigms: supervised learning, unsupervised learning, and ensemble methods. Examples from the published literature will be provided for each algorithm. Some common pitfalls of applying ML to biological and chemical data sets will also be explained, alongside a brief summary of a few more advanced paradigms, including reinforcement learning and semi-supervised learning.

A new twist for Stork-Danheiser products enabled by visible light mediated trans-cyclohexene formation; access to acyclic distal enones

Herein, we investigate the use of visible light to indirectly drive ring opening in unstrained 6- and 7-membered ring systems via reaction with a transiently generated trans-cycloalkene. Identification of conditions that capture visible light energy in the form of ring strain was key to success. Under mildly acidic conditions, cycloalkenols were shown to undergo formally endothermic ring-opening isomerization to give acyclic exo-methylene and distal ketones or aldehydes in high yields. Ultimately, this work demonstrates the ability of cycloalkenes to capture visible light energy and its use to drive both kinetically and thermally unfavorable rearrangements.