Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
원문 초록
The nexus of quantum computing and machine learning - quantum machine learning - offers the potential for significant advancements in chemistry. This review specifically explores the potential of quantum neural networks on gate-based quantum computers within the context of drug discovery. We discuss the theoretical foundations of quantum machine learning, including data encoding, variational quantum circuits, and hybrid quantum-classical approaches. Applications to drug discovery are highlighted, including molecular property prediction and molecular generation. We provide a balanced perspective, emphasizing both the potential benefits and the challenges that must be addressed.
함께 살펴볼 논문
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Robust Molecular Property Prediction via Densifying Scarce Labeled Data
Learning Hierarchical Interaction for Accurate Molecular Property Prediction
AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
Graph Neural Networks in Modern AI-aided Drug Discovery
Recent Developments in GNNs for Drug Discovery
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery
토론
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