Ofori-Atta, Kwabena and Springer, Clayton (2021) The Price is Right: Predicting Reagent Prices. ChemRxiv.

Abstract

We present a model for estimating the price of a reagent from its chemical structure. It is intended to be useful when doing reagent selection for library design. The model is a Random Forest regressor which is trained on the MolPort catalog of 302K reagents and the log of their price. For descriptors we use topological fingerprints from RDKit: chiral Morgan fingerprints, its medicinal chemis-try descriptors, and counts of undetermined chiral centers. The model has an out-of-bag performance of 34% variance explained in log Price. When predicting on known reagents, the model explains 91% of the variance in log Price. We analyzed the model to understand the errors that the model makes. We show that the compounds with the highest errors have only a subtly different structure from similar molecules, but very different in price.

Item Type:	Article
Keywords:	Reagent Pricing. Machine learning. Random forest. Cliff pairs, Matched molecular pairs
Date Deposited:	26 Oct 2021 00:45
Last Modified:	26 Oct 2021 00:45
URI:	https://oak.novartis.com/id/eprint/44400