6 research outputs found
From chemical documentation to chemoinformatics: fifty years of chemical information science
This paper summarises the historical development of the discipline that is now called ‘chemoinformatics’. It shows how this has evolved, principally as a result of technological developments in chemistry and biology during the past decade, from long-established techniques for the modelling and searching of chemical molecules. A total of 30 papers, the earliest dating back to 1957, are briefly summarised to highlight some of the key publications and to show the development of the discipline
Incorporating Molecule's Stereisomerism within the Machine Learning Framework
International audienceAn important field of chemoinformatics consists in the prediction of molecule's properties, and within this field, graph kernels constitute a powerful framework thanks to their ability to combine a natural encoding of molecules by graphs, with classical statistical tools. Unfortunately some molecules encoded by a same graph and differing only by the three dimensional orientation of their atoms in space have different properties. Such molecules are called stereoisomers. These latter properties can not be predicted by usual graph methods which do not encode stereoisomerism. In this paper we propose to encode the stereoisomerism property of each atom of a molecule by a local subgraph. A kernel between bags of such subgraphs provides a similarity measure incorporating stereoisomerism properties. We then propose two extensions of this kernel incorporating in each sub graph information about its surroundings