Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening.

ABSTRACT: Enhancing the properties of advanced drug candidates is aided by the direct incorporation of specific chemical groups, avoiding the need to construct the entire compound from the ground up. Nevertheless, their chemical intricacy often poses challenges in predicting reactivity for C-H activation reactions and planning their synthesis. We adopted a reaction screening approach that combines high-throughput experimentation (HTE) at a nanomolar scale with computational graph neural networks (GNNs). This approach aims to identify suitable substrates for late-stage C-H alkylation using Minisci-type chemistry. GNNs were trained using experimentally generated reactions derived from in-house HTE and literature data. These trained models were then used to predict, in a forward-looking manner, the cou

SUBMITTER: Nippa DF 

PROVIDER: S-EPMC10661846 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature