$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning
Quantitative Structure-Activity Relationship ($\mathtt{QSAR}$) modeling is a foundational computational methodology in early-stage drug discovery, heavily relied upon for predicting compound toxicity, bioavailability, and therapeutic potential. However, classical methods often struggle to effectively map the highly complex, non-linear, and high-dimensional interactions inherent in molecular data, leading to reduced predictive accuracy and costly late-stage clinical failures. In this paper, we present a Quantum Multiple Kernel Learning ($\mathtt{QMKL}$) framework, dubbed Next-Gen $\mathtt{Q^2SA
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 52%rdk/p2rank →
- PossiblePossibly related (embedding) · 51%netket/netket →
- PossiblePossibly related (embedding) · 49%JMIR report: machine learning accelerates radiopharmaceutical drug discovery, optimizes personalized dosimetry - EurekAlert! →
- PossiblePossibly related (embedding) · 46%R-D-BioTech-Alaska/Qelm →
- PossiblePossibly related (embedding) · 45%Scientists’ Side Hustle? Using AI and Quantum Computing to Generate New Peptides →
- LinkedLinked via arxiv author · 85%Mariano Caruso →
“$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning”
- LinkedLinked via arxiv author · 85%Daniel Ruiz →
“$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning”
- LinkedLinked via arxiv author · 85%Alejandro Giraldo →
“$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning”