Summary form only given. Small molecules with at most a few dozen atoms play a fundamental role in organic chemistry and biology. They can be used as combinatorial building blocks for chemical synthesis, as molecular probes for perturbing and analyzing biological systems, and for the screening/design/discovery of new drugs. As datasets of small molecules become increasingly available, it becomes important to develop computational methods for the classification and analysis of small molecules and in particular for the prediction of their physical, chemical, and biological properties. We describe datasets and machine learning methods, in particular kernel methods, for chemical molecules represented by 1D strings, 2D graphs of bonds, and 3D structures. We demonstrate state-of-the-art results for the prediction of physical, chemical, or biological properties including the prediction of toxicity and anti-cancer activity. More broadly, we will discuss some of the challenges and opportunities for computer science, AI, and machine learning in chemistry.