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Drug Design

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TitleStatusHype
Reinforcement Learning for Personalized Drug Discovery and Design for Complex Diseases: A Systems Pharmacology Perspective—0
Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design—0
Representation Learning on Graphs: Methods and Applications—0
Research and development of MolAICal for drug design via deep learning and classical programming—0
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion—0
Retro Drug Design: From Target Properties to Molecular Structures—0
RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design—0
Retro-prob: Retrosynthetic Planning Based on a Probabilistic Model—0
Rigidity strengthening is a vital mechanism for protein-ligand binding—0
SAFE setup for generative molecular design—0
Scaffold-constrained molecular generation—0
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning—0
Evaluating Molecule Synthesizability via Retrosynthetic Planning and Reaction Prediction—0
Self-Organizing Maps of Unbiased Ligand-Target Binding Pathways and Kinetics—0
Semi-Supervised GCN for learning Molecular Structure-Activity Relationships—0
Shape Complementarity Analysis for Objects of Arbitrary Shape—0
Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models—0
ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design—0
Spherical Distance Metrics Applied to Protein Structure Classification—0
Stabilizing Policy Gradients for Stochastic Differential Equations via Consistency with Perturbation Process—0
StriderNET: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes—0
STRIDE: Structure-guided Generation for Inverse Design of Molecules—0
Structural insights into characterizing binding sites in EGFR kinase mutants—0
Structure-aware generation of drug-like molecules—0
Structure-Based Drug Design via 3D Molecular Generative Pre-training and Sampling—0
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