2 citations · 2 across the 4 of their papers we have counts for
4 papers
: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning
Mariano Caruso, Daniel Ruiz, Alejandro Giraldo +1
Quantitative Structure-Activity Relationship () modeling is a foundational computational methodology in early-stage drug discovery, heavily relied upon for predictin…
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises
Afifa Khaled, Mohammed Sabir, Rizwan Qureshi +4
The Medical Information Mart for Intensive Care (MIMIC) datasets have become the Kernel of Digital Health Research by providing freely accessible, deidentified records from tens of…
Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery
Alejandro Giraldo, Daniel Ruiz, Mariano Caruso +2
Quantitative Structure-Activity Relationship (QSAR) modeling is a cornerstone of computational drug discovery. This research demonstrates the successful application of a Quantum Mu…
Quantum QSAR for drug discovery
Alejandro Giraldo, Daniel Ruiz, Mariano Caruso +1
Quantitative Structure-Activity Relationship (QSAR) modeling is key in drug discovery, but classical methods face limitations when handling high-dimensional data and capturing comp…