MolDesignBench: Evaluating LLM-based Agent for Scenario-grounded Molecular Design

arXiv:2609.27349 · cs.AI, cs.LG · Submitted 2026-09-23 · Read on arXiv

cs.AI, cs.LG

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/KU-AGI/MolDesignBench

Terminology

• Benchmark — 69× in this paper · explained in 2 episodes
• Core — 13× in this paper · explained in 2 episodes
• Consistency — 10× in this paper · explained in 3 episodes
• Verifier — 10× in this paper · explained in 2 episodes
• Blood-Brain Barrier — 5× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Test-time Scaling — 4× in this paper · explained in 2 episodes
• Structural Constraints — 3× in this paper · explained in 2 episodes
• AUC — 2× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Blood-Brain Barrier Penetration — 2× in this paper
• Failure Modes — 2× in this paper · explained in 3 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Human Intestinal Absorption — 2× in this paper
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Quantitative Estimate of Drug-likeness — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Topological Polar Surface Area — 2× in this paper
• AI Agents — 1× in this paper · explained in 2 episodes
• AI Foundation Model — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Agentic Evaluation — 1× in this paper · explained in 1 episode
• Decode — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Thinking mode — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• gpt-oss-120b & gpt-oss-20b Model Card
• Intern-S1: A Scientific Multimodal Foundation Model
• ACEBench: Who Wins the Match Point in Tool Usage?
• Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization
• ToolTalk: Evaluating Tool-Usage in a Conversational Setting
• Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation
• ChemAmp: Amplified Chemistry Tools via Composable Agents
• Training a Scientific Reasoning Model for Chemistry
• Gorilla: Large Language Model Connected with Massive APIs
• AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments
• Evaluating Large Language Models in Scientific Discovery
• Qwen3 Technical Report
• SGLang: Efficient Execution of Structured Language Model Programs

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection