Similarity Search and Predictive Models for Chemicals

Taking advantage of the chemical representations, we investigate computational techniques to explore the chemical space and to identify the real connections between the chemical space and a target property space. In particular, we propose three methods to perform data driven knowledge discovery in chemical space including

  • Similarity search in a kernel space kernel
  • Semi-supervised classification and regression
  • Active learning and one-class classification

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