Molecular Fingerprint

Molecular fingerprints are numerical representations of molecules, used to predict their properties and facilitate tasks like drug discovery and materials science. Current research focuses on developing more informative and efficient fingerprint generation methods, employing techniques like graph neural networks, persistent homology, and combinations of various fingerprint types, often integrated with machine learning models such as gradient boosting and transformers. These advancements aim to improve the accuracy and interpretability of molecular property predictions, ultimately accelerating the design and development of new molecules with desired characteristics.