MolSafeEval introduces a safety benchmark for AI molecular generators that combines heterogeneous safety data into a knowledge graph for LLM-based detection of unsafe molecules.
Decompdiff: Diffusion models with decomposed priors for structure-based drug design
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 5representative citing papers
GeoCoupling optimizes temporal couplings between modalities in biomolecular generative models and outperforms synchronous baselines on drug design and protein design tasks.
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
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MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules
MolSafeEval introduces a safety benchmark for AI molecular generators that combines heterogeneous safety data into a knowledge graph for LLM-based detection of unsafe molecules.
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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.
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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.
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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.
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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.