engleski

Machine learning in chemical compound space

Machine learning (ML) approaches are extensively used to analyse chemical (synthetical, analytical, physicochemical) and biological (in vitro and in vivo) data and build models for discovery and design of novel small molecular weight molecules with optimized target activities. The basic ML requirement is to have a large and reliable enough dataset which is used for training and testing a predictive model. In the presentation, application of ML approaches on publicly available data sets for biologically active compounds such as drugs and agrochemicals will be demonstrated.

Machine learning ; Chemoinformatics ; QSAR ; biological activities

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