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Cheminformatics

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Cheminformatics

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Research Frontiers in Generative Models for De Novo Drug Design

Creation of generative adversarial networks and variational autoencoders for novel bioactive molecule generation.

Latent Space Geometry and Chemical Validity in Generative Models
Multi-Objective Optimization Through Conditional Generation Architectures
Transferability of Learned Chemical Grammars Across Scaffolds
Fragment-Based Generative Priors for Synthesizable Drug Candidates
Uncertainty Quantification in Generative Chemical Space Exploration
Equivariant Graph Networks for 3D Ligand Generation
Generative Models Constrained by Pharmacophoric Topology
Adversarial Robustness in De Novo Molecular Design Systems
Reinforcement Learning for Iterative Chemical Property Steering
Interpretability of Learned Chemical Transformations in Generative Models

All Cheminformatics PhD categories