Explaining molecular properties with natural language

03 October 2022, Version 3
This content is a preprint and has not undergone peer review at the time of posting.


We present a model-agnostic method that gives natural language explanations of molecular structure property predictions. Machine learning models are now common for molecular property prediction and chemical design. They typically are black boxes -- having no explanation for predictions. We show how to use surrogate models to attribute predictions to chemical descriptors and molecular substructures, independent of the black box model inputs. The method generates explanations consistent with chemical reasoning, like connecting existence of a functional group or molecular polarity. We see in a genuine test like blood brain barrier permeation, our descriptor explanations match biologically observed SARs with mechanistic support. We show these quantitative explanations can be further translated to natural language.



Supplementary weblinks


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.