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The intend is to solve the forward-reaction prediction problem, where the reactants are known and the interest is in generating the reaction products using Deep learning. This Graphical User Interface takes simplified molecular-input line-entry system (SMILES) as an input and generates the product SMILE & molecule. Beam search is used in Version 2, to generate top 5 predictions. Maximum input length for the model is 15 (excluding spaces).

RASP uses structure-based chemical shift predictions to solve the backbone resonance assignment problem in protein NMR spectroscopy. This enables rapid determination of highly accurate assignments on the basis of minimal experimental datasets, even for spectroscopically challenging proteins. RASP takes as input spin systems assembled on the basis of an arbitrary set of conventional triple-resonance NMR experiments. Uniquely, RASP is capable of extensive assignments even in the abscence of Cbeta chemical shift information: over a test set of 154 proteins RASP assigns 88 % of residues with an accuracy of 99.7 %, using only information available from HNCO and HNCA spectra. ...