Predictive Minisci late stage functionalization with transfer learning.

ABSTRACT: Structural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates to deliver numerous diverse products in a single reaction. Predicting the regioselectivity of LSF is still an open challenge in the field. Numerous efforts from chemoinformatics and machine learning (ML) groups have made strides in this area. However, it is arduous to isolate and characterize the multitude of LSF products generated, limiting available data and hindering pure ML approaches. We report the development of an approach that combines a message passing neural network and ¹³C NMR-based transfer learning to predict the atom-wis

SUBMITTER: King-Smith E 

PROVIDER: S-EPMC10789750 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature