How Computers “See” Molecules

Translating real-world molecules into a format computers can process is a fundamental challenge in data science and generative molecular design. The quality of this representation is critical, as it establishes the upper performance limit for any machine learning model built upon it. Common methods for this translation include simple chemical formulas and linear string encodings like SMILES and InChI, which capture atomic connectivity. Another powerful approach involves using molecular descriptors to convert a molecule's properties into a standardized numerical feature vector suitable for computational analysis. This process is essential for enabling computers to infer chemical properties, predict biological functions, and design new molecules.