Inductive queries for a drug designing robot scientist: Inductive queries for a drug dsigning robot scientist

Ross D. King, Amanda Schierz, Amanda Clare, Jem Rowland, Andrew Sparkes, Siegfried Nijssen, Jan Ramon

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments. © 2010 Springer Science+Business Media, LLC.
    Original languageEnglish
    Title of host publicationInductive Databases and Constraint-Based Data Mining|Inductive Databases and Constraint-Based Data Min.
    PublisherSpringer Nature
    Pages425-451
    Number of pages26
    DOIs
    Publication statusPublished - 2010

    Keywords

    • Robot Scientist

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