This paper focuses on the recent development of rule-based methods and their applications to the drug discovery process. For a given target, the path for designing new drugs with a lower attrition rate is based on an effective mining of the huge amount of experimental in vitro and in vivo data which has been collected. These data often come in various formats, from many different areas such as chemistry, biology, pharmacology, toxicity and extraction of the critical information is not an easy task. To guide the multi-objective optimization, we have developed a decision-support system (KEM®), based on the Galois lattices theory and constraint satisfaction programming (CSP). After a brief overview of machine learning applications, we will describe the methodology used in KEM for data mining and prediction. Two examples of applications in the drug discovery area will be discussed.
Keywords: Galois lattices, drug design, multi-objective optimization, structure-activity relationships (SAR), machine learning
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