most citedMolecular graph generation with Graph Neural Networks

179 citations · 224 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ML2022★ 1 cited

A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs

Pietro Bongini, Elisa Messori, Niccolò Pancino +1

Predicting drug side-effects before they occur is a key task in keeping the number of drug-related hospitalizations low and to improve drug discovery processes. Automatic predictor…

cs.CV2022★ 2 cited

Is GPT-3 all you need for Visual Question Answering in Cultural Heritage?

Pietro Bongini, Federico Becattini, Alberto Del Bimbo

The use of Deep Learning and Computer Vision in the Cultural Heritage domain is becoming highly relevant in the last few years with lots of applications about audio smart guides, i…

q-bio.QM2022★ 1 cited

Modular multi-source prediction of drug side-effects with DruGNN

Pietro Bongini, Franco Scarselli, Monica Bianchini +3

Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence,…

stat.ML2020★ 179 cited

Molecular graph generation with Graph Neural Networks

Pietro Bongini, Monica Bianchini, Franco Scarselli

Drug Discovery is a fundamental and ever-evolving field of research. The design of new candidate molecules requires large amounts of time and money, and computational methods are b…

cs.CV2020★ 41 cited

Visual Question Answering for Cultural Heritage

Pietro Bongini, Federico Becattini, Andrew D. Bagdanov +1

Technology and the fruition of cultural heritage are becoming increasingly more entwined, especially with the advent of smart audio guides, virtual and augmented reality, and inter…