Institution: IC
Departement: DSCTM
Typology: Software
Location: Via Amendola 122/O, 70125 Bari
Contacts:
- Giuseppe Felice Mangiatordi (giuseppefelice.mangiatordi@cnr.it)
- Domenico Alberga (domenico.alberga@cnr.it)
Description:
This software enables the use of a machine learning-based classifier for predicting sigma-1 receptor (S1R) affinity. The model was developed using the Support Vector Machine (SVM) algorithm with Morgan fingerprints as molecular descriptors. To enhance transparency and trustworthiness, the tool incorporates Explainable Artificial Intelligence (XAI) techniques, providing users with interpretable insights into the predictions. With its intuitive interface, robust predictive capabilities, and integrated XAI features, SIGMAP offers a powerful solution for the rational design of novel S1R modulators.
Team:
- Dr Nicola Corriero (nicola.corriero@cnr.it)
- Dr Michele Saviano (michele.saviano@cnr.it)
- Dr Domenico Alberga (domenico.alberga@cnr.it)
https://www.ba.ic.cnr.it/softwareic/sigmap/
SIGMAP