GeoCoupling optimizes temporal couplings between modalities in biomolecular generative models and outperforms synchronous baselines on drug design and protein design tasks.
Tagmol: Target-aware gradient-guided molecule generation.arXiv preprint arXiv:2406.01650
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
DEPPA reformulates the denoising process of pocket-aware diffusion models as a multi-step MDP and applies RL fine-tuning with a coarse scheduler to optimize ligands for binding affinity, drug-likeness, synthesizability and diversity on CrossDocked2020.
ToolMol integrates evolutionary algorithms with agentic LLMs and precise RDKit tools to optimize multi-objective drug properties, yielding ligands with over 10% better predicted binding affinity and 35% gains in absolute binding free energy on three protein targets.
FTDiff applies GRPO-style RL fine-tuning and fast sampling to a time-free pretrained diffusion model to generate valid diverse high-quality molecules balancing multiple drug design objectives in SBDD.
citing papers explorer
-
Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling
GeoCoupling optimizes temporal couplings between modalities in biomolecular generative models and outperforms synchronous baselines on drug design and protein design tasks.
-
Fine-tuning Pocket-Aware Diffusion Models via Denoising Policy Optimization
DEPPA reformulates the denoising process of pocket-aware diffusion models as a multi-step MDP and applies RL fine-tuning with a coarse scheduler to optimize ligands for binding affinity, drug-likeness, synthesizability and diversity on CrossDocked2020.
-
ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery
ToolMol integrates evolutionary algorithms with agentic LLMs and precise RDKit tools to optimize multi-objective drug properties, yielding ligands with over 10% better predicted binding affinity and 35% gains in absolute binding free energy on three protein targets.
-
Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling
FTDiff applies GRPO-style RL fine-tuning and fast sampling to a time-free pretrained diffusion model to generate valid diverse high-quality molecules balancing multiple drug design objectives in SBDD.