An effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery.

In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target–cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure–activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.

A proof-of-concept methodology for addressing small amounts of chemical data using transfer learning is presented.

Protein-protein interactions (PPIs) pose challenges for intervention through small molecule drugs, protein drugs, and linear peptides due to inherent limitations such as inappropriate size, poor stability, and limited membrane penetrance. The emergence of stapled α-helical peptides presents a promising avenue as potential competitors for inhibiting PPIs, demonstrating enhanced structural stability and increased tolerance to proteolytic enzymes. This review aims to provide an overview of metal-free stapling strategies involving two identical natural amino acids, two different natural amino acids, non-natural amino acids, and multicomponent reactions. The primary objective is to delineate comprehensive peptide stapling approaches and foster innovative ideation among readers by accentuating methodologies published within the past five years and elucidating evolving trends in stapled peptides.