Showing posts from February, 2023

Generative Molecular Design - We Need to Raise the Bar

While it's great that we're now seeing papers describing the experimental validation of generative algorithms for molecular design, we need to consider the significance of these findings and put them into the appropriate context.  Over the last five years, we've seen an explosion in the number of papers describing methods for generative molecular design. The 2018 paper by G√≥mez-Bombarelli, which launched the field, has already been cited more than 2,100 times. For those unfamiliar with the area, generative molecular design algorithms learn the distributions and associations of chemical functionality from a training set, then sample these distributions to generate new molecules. This molecule generation task can be coupled with one or more scoring functions to generate molecules meeting a specific objective, such as a predicted binding affinity. These methods can be considered similar in spirit to techniques for generating photorealistic images , art , or text that have be