Recently, the journal "Nature Machine Intelligence" published a breakthrough study on the structure prediction of protein-ligand complexes. This study proposes a new method called NeuralPLexer, which utilizes deep generative models to directly predict the structure of protein-ligand complexes using only protein sequences and ligand molecular maps as input. This innovation is expected to significantly improve the efficiency of drug research and development and bring revolutionary changes to the field of drug discovery.
The article focuses on:
Recently, scientists published research on the structure prediction of protein-ligand complexes in the journal "Nature Machine Intelligence". The new method NeuralPLexer utilizes deep generative models to directly predict structures with only protein sequence and ligand molecular graph inputs. This method has important application prospects and can play an important role in the field of drug discovery. Through this study, an important step has been taken in predicting the structure of protein-ligand complexes, providing new possibilities for future medical research and bioengineering.
The emergence of the NeuralPLexer method marks a significant progress in protein-ligand complex structure prediction technology, providing a powerful tool for accelerating the drug research and development process and promoting the development of the biomedical industry. In the future, this method is expected to be applied in more fields and make greater contributions to human health and social progress.