This file contains the MATLAB data to reproduce the QSAR multi-task models described in the following paper: Valsecchi C., Collarile M., Grisoni F., Todeschini R., Ballabio D., Consonni V. Predicting molecular activity on nuclear receptors by multi-task neural networks. Submitted to Journal of Chemometrics [link]. The file contains molecular descriptors (Dragon ECFP) and experimental binary activity on 30 tasks (i.e. binding, agonism and antagonism for 11 nuclear receptors) for training and test molecules.
Conditions and warranty
The dataset is freeware and may be used if proper reference is given to the authors. Please, refer to the following paper:
Valsecchi C., Collarile M., Grisoni F., Todeschini R., Ballabio D., Consonni V. Predicting molecular activity on nuclear receptors by multi-task neural networks. Journal of Chemometrics, in press [link]
Download
Fill in the following form. Your personal data will be used only for notification via email of new releases of the dataset and will not be communicated to external third parties. Once the form has been submitted, open the rar file and extract the files. Have a look to the readme.txt file for further details. If you experience any problem to downlaod the toolbox, write to davide.ballabio@unimib.it.