A cheminformatics method is described for classification, and biophysical examination, of individual molecules. A novel molecular detector is used--one based on current blockade measurements through a nanometer-scale ion channel (alpha-hemolysin). Classification results are described for blockades caused by DNA molecules in the alpha-hemolysin nanopore detector, with signal analysis and pattern recognition performed using a combination of methods from bioinformatics and machine learning. Due to the size of the alpha-hemolysin protein channel, the blockade events report on one DNA molecule at a time, which enables a variety of reproducible, single-molecule biophysical experiments. To capture the full sensitivity of the nanopore detector's blockade signal, Hidden Markov Models (HMMs) were used with Expectation/Maximization for denoising and for associating a feature vector with the ionic current blockade of each captured DNA molecule. Support Vector Machines (SVMs) that employ novel kernel designs were then used as discriminators. With SVM training performed off-line, and economical HMM processing on-line, blockade classification was possible during capture. HMMs were also used in conjunction with a time-domain finite state automaton (off-line) for feature discovery and kinetics analysis. Analysis of the DNA data indicates a variety of binding (DNA-protein), fraying, and conformational shifts that are consistent with data obtained from thermodynamic analyses (melting curves), X-ray crystallography, and NMR studies. The software tools are designed for analysis of generic blockades in ionic channels, including those in other biological pore-forming toxins, other biological channels in general, and semiconductor-based channels.