Purdue University scientists are using machine learning models to create new options for drug discovery pipelines. One-step multicomponent reaction with interpretable machine learning innovation to develop chemical library for drug discovery. Machine learning has been used widely in the chemical sciences for drug design and other processes. The models that are prospectively tested for new reaction outcomes and used to enhance human understanding to interpret chemical reactivity decisions made by such models are extremely limited. Purdue University innovators have introduced chemical reactivity flowcharts to help chemists interpret reaction outcomes using statistically robust machine learning models trained on a small number of reactions.

'Edge project of the year' award secured for EcoStruxure IT deployment in collaboration with the Wellcome Sanger Institute and Elite Channel Partner …