Cheminformatics Task

Cheminformatics leverages computational methods to analyze and predict the properties of chemical compounds, accelerating drug discovery and materials science. Current research emphasizes the development and application of large language models (LLMs), graph neural networks (GNNs), and other deep learning architectures to analyze molecular structures represented as SMILES strings or graphs, often incorporating techniques like contrastive learning and self-supervised pre-training for improved performance. These advancements enable more efficient prediction of molecular properties, such as toxicity and activity, and facilitate tasks like retrosynthesis prediction and virtual screening, ultimately streamlining chemical research and development.