Mulaqua: An Interpretable Multimodal Deep Learning Framework for Identifying PMT/vPvM substances in Drinking Water

I was recently interviewed by BRIC (Hanbitsa) about our research on Mulaqua, which was published in the Journal of Hazardous Materials.

In the interview, I discussed how our multimodal deep learning framework integrates molecular strings and images to identify Persistent, Mobile, and Toxic (PMT) and very Persistent and very Mobile (vPvM) substances in drinking water. This approach significantly improves the interpretability of environmental risk assessments and offers a proactive tool for hazard identification.

The full interview is available here.