Chemical Compound

Chemical compound research focuses on understanding and predicting the properties and behaviors of molecules, primarily driven by applications in drug discovery and materials science. Current research employs machine learning models, including regression techniques (like multiple linear regression), evolutionary algorithms (such as NSGA-II and MOEA/D), and transformer-based architectures, to analyze molecular structures (represented by SMILES strings or graph-theoretic descriptors) and predict properties like aqueous solubility and blood-brain barrier permeability. These advancements improve the efficiency of drug design and materials development by enabling the in silico prediction and generation of compounds with desired characteristics, ultimately accelerating scientific discovery and technological innovation.