PepBAN is introduced, a deep learning framework for modeling PepPI predictions that effectively learns the pattern of pairwise local interactions, enables the identification of key residues participating in the peptide-protein interactions, and offers an intuitive approach to interpret the underlying mechanisms of PepPIs via analyzing attention weights.
Shuaiyan Li, Xiaorui Wang, Yuchen Zhu et al.· Journal of Chemical Informat...· 4 citations
This study proposes SynGFN, which models molecular design as a cascade of simulated chemical reactions, enabling the assembly of molecules from synthesizable building blocks, as a bridge linking molecular design and synthesis, accelerating exploration and producing diverse, synthesizable, high-performance molecules.
RAPiDock is presented, an all-atom diffusion model that predicts peptide–protein binding patterns across 92 amino acid types, enabling high-throughput virtual screening for advancing therapeutic peptide design and serve as a powerful tool for high-throughput virtual screening with structural precision.
The results indicate that this fully automated, open-source system holds potential value for improving the efficiency and sustainability of molecular synthesis, and the integration of organic and enzymatic synthesis enhances molecule construction efficiency.
HiCLR is the first foundation model that can be broadly applied to various synthesis-related tasks, and it achieves state-of-the-art performance in reaction classification, reaction condition recommendation, reaction yield prediction, synthesis planning, and even molecular property prediction.
Jialu Wu, Yiheng Zhu, Xiaorui Wang et al.· JACS Au· 0 citations
RSGPT, a generative model pre-trained on ten billion data points, achieving state-of-the-art performance for synthesis planning, and introduces reinforcement learning to capture the relationships among products, reactants, and templates more accurately.
Yafeng Deng, Xinda Zhao, Hanyu Sun et al.· Nature Communications· 18 citations· ⚡2
ERAM aligns pre-trained molecular representations from Protein Language Model with the knowledge of enzyme catalysis by modeling enzymatic reactions as multi-relational data, and demonstrates its potential as a versatile and effective tool for enzyme catalysis research.
