SupraBench introduces four core tasks and a curated corpus to benchmark LLMs on host-guest chemistry reasoning, showing substantial remaining headroom and task-specific failure modes.
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SciAgentArena is a new interactive benchmark for AI agents on scientific tasks that finds agents handle clear data-analysis workflows but struggle with novel insights, self-directed exploration, and open-ended questions.
Strong absolute accuracy on mixture properties often masks poor recovery of non-ideal behavior, with large drops under strict molecule splits, making transfer to unseen molecules the central challenge.
MolLingo introduces a multi-agent framework with BFE molecular representation and docking-grounded reasoning to outperform frontier LLMs on molecular design benchmarks including fourfold docking score gains.
BioXArena benchmarks LLM agents on generating end-to-end ML pipelines for 76 multi-modal biomedical tasks, with MLEvolve plus Gemini-3.1-Pro scoring highest at 0.666.
Chem-GMNet uses sphere-native embeddings, DualSKA attention, and SH-FFN layers to match or beat ChemBERTa-2 on MoleculeNet tasks with fewer parameters and sometimes no pretraining.
Large language models exhibit distinct creative patterns in molecule generation, including higher constraint satisfaction when more constraints are added, and this is the first work to reframe molecule generation abilities as creativity.
Active-GRPO reaches 0.1773 average SRxSim on TOMG-Bench MOLOPT by adaptively switching between imitation and self-reinforcement while upgrading references, outperforming GRPO and RePO.
Sparse autoencoders on MolFormer reveal position-tracking latents in early layers and atom-in-substructure plus pharmacologically relevant features in later layers, with non-canonical SMILES causing greater representation disruption than invalid ones.
A parallel-tempering evolutionary framework for LLM hypothesis search improves both quality and diversity of candidates in molecular, equation, and algorithm discovery under fixed validation budgets.
Decentralized AI agent teams self-organize around hypotheses, critique proposals, and share knowledge to outperform single-agent baselines on biomedical ML, language-model optimization, and protein fitness tasks.
Benchmark across 78 endpoint-split entries finds classical ML winning 47.4% of best performances over pretrained models, GNNs, and LLMs, with performance depending on model-task-split fit rather than scale.
Tabular foundation models achieve high accuracy in molecular property prediction through in-context learning, with up to 100% win rates on MoleculeACE tasks when paired with CheMeleon embeddings.
Suiren-1.0 is a family of three molecular foundation models (Base, Dimer, ConfAvg) pre-trained on 70M+ DFT samples and distilled to achieve claimed state-of-the-art performance on quantum property prediction tasks from 2D inputs.
GraphPINE is a GNN architecture that initializes node importance from prior knowledge graphs and propagates updates via an importance propagation layer for interpretable drug response prediction on over 5,000 genes and 952 drugs.
Orthonormal Data Collaboration (ODC) enforces orthonormal secret and target bases so that alignment reduces to the Orthogonal Procrustes problem, yielding O(acl^2) complexity, orthogonal concordance, and downstream performance invariant to the choice of target basis.
Reinforcement learning with a quantum-inspired simulated annealing policy neural network is applied to synthesizable molecular optimization and reports competitive results against genetic algorithm baselines on the PMO benchmark with a 10K query budget.
citing papers explorer
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SupraBench: A Benchmark for Supramolecular Chemistry
SupraBench introduces four core tasks and a curated corpus to benchmark LLMs on host-guest chemistry reasoning, showing substantial remaining headroom and task-specific failure modes.
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Benchmarking AI Agents for Addressing Scientific Challenges Across Scales
SciAgentArena is a new interactive benchmark for AI agents on scientific tasks that finds agents handle clear data-analysis workflows but struggle with novel insights, self-directed exploration, and open-ended questions.
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A Systematic Evaluation of Molecular Mixture Behavior Prediction
Strong absolute accuracy on mixture properties often masks poor recovery of non-ideal behavior, with large drops under strict molecule splits, making transfer to unseen molecules the central challenge.
-
MolLingo: Molecule-Native Representations for LLM-Powered Scientific Agents
MolLingo introduces a multi-agent framework with BFE molecular representation and docking-grounded reasoning to outperform frontier LLMs on molecular design benchmarks including fourfold docking score gains.
-
BioXArena: Benchmarking LLM Agents on Multi-Modal Biomedical Machine Learning Tasks
BioXArena benchmarks LLM agents on generating end-to-end ML pipelines for 76 multi-modal biomedical tasks, with MLEvolve plus Gemini-3.1-Pro scoring highest at 0.666.
-
Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction
Chem-GMNet uses sphere-native embeddings, DualSKA attention, and SH-FFN layers to match or beat ChemBERTa-2 on MoleculeNet tasks with fewer parameters and sometimes no pretraining.
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How Creative Are Large Language Models in Generating Molecules?
Large language models exhibit distinct creative patterns in molecule generation, including higher constraint satisfaction when more constraints are added, and this is the first work to reframe molecule generation abilities as creativity.
-
Active-GRPO: Adaptive Imitation and Self-Improving Reasoning for Molecular Optimization
Active-GRPO reaches 0.1773 average SRxSim on TOMG-Bench MOLOPT by adaptively switching between imitation and self-reinforcement while upgrading references, outperforming GRPO and RePO.
-
What Does a Chemical Language Model Know About Molecules?
Sparse autoencoders on MolFormer reveal position-tracking latents in early layers and atom-in-substructure plus pharmacologically relevant features in later layers, with non-canonical SMILES causing greater representation disruption than invalid ones.
-
Towards Diverse Scientific Hypothesis Search with Large Language Models
A parallel-tempering evolutionary framework for LLM hypothesis search improves both quality and diversity of candidates in molecular, equation, and algorithm discovery under fixed validation budgets.
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AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation
Decentralized AI agent teams self-organize around hypotheses, critique proposals, and share knowledge to outperform single-agent baselines on biomedical ML, language-model optimization, and protein fitness tasks.
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Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction
Benchmark across 78 endpoint-split entries finds classical ML winning 47.4% of best performances over pretrained models, GNNs, and LLMs, with performance depending on model-task-split fit rather than scale.
-
Tabular foundation models for in-context prediction of molecular properties
Tabular foundation models achieve high accuracy in molecular property prediction through in-context learning, with up to 100% win rates on MoleculeACE tasks when paired with CheMeleon embeddings.
-
Suiren-1.0 Technical Report: A Family of Molecular Foundation Models
Suiren-1.0 is a family of three molecular foundation models (Base, Dimer, ConfAvg) pre-trained on 70M+ DFT samples and distilled to achieve claimed state-of-the-art performance on quantum property prediction tasks from 2D inputs.
-
GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction
GraphPINE is a GNN architecture that initializes node importance from prior knowledge graphs and propagates updates via an importance propagation layer for interpretable drug response prediction on over 5,000 genes and 952 drugs.
-
Data Collaboration Analysis with Orthonormal Basis Selection and Alignment
Orthonormal Data Collaboration (ODC) enforces orthonormal secret and target bases so that alignment reduces to the Orthogonal Procrustes problem, yielding O(acl^2) complexity, orthogonal concordance, and downstream performance invariant to the choice of target basis.
-
Quantum-inspired Reinforcement Learning for Synthesizable Drug Design
Reinforcement learning with a quantum-inspired simulated annealing policy neural network is applied to synthesizable molecular optimization and reports competitive results against genetic algorithm baselines on the PMO benchmark with a 10K query budget.