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Despite the fundamental progress in autonomous molecular and materialsdiscovery, data scarcity throughout chemical compound space still severelyhampers the use of modern ready-made machine learning models as they relyheavily on the paradigm, 'the bigger the data the better'. Presentingsimilarity based machine learning (SML), we show an approach to select data andtrain a model on-the-fly for specific queries, enabling decision making in datascarce scenarios in chemistry. By solely relying on query and training dataproximity to choose training points, only a fraction of data is necessary toconverge to competitive performance. After introducing SML for the harmonicoscillator and the Rosenbrock function, we describe applications to scarce datascenarios in chemistry which include quantum mechanics based molecular designand organic synthesis planning. Finally, we derive a relationship between theintrinsic dimensionality and volume of feature space, governing the overallmodel accuracy.

Improved decision making with similarity based machine learning: Applications in chemistry