The paper critiques the current evaluation practices in generative molecular dynamics (GenMD), arguing that they allow models to appear successful while potentially being incorrect on key claims. It attributes this issue to the high computational cost of obtaining exhaustive ground truth, which forces the community to rely on convenient proxies that become entrenched. The authors propose new observables, reporting standards, and benchmark designs—both computational and experimental—to align evaluation with GenMD’s scientific goals.
arXiv:2607. 03513v1 Announce Type: cross Abstract: We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution at a fraction of the cost of molecular dynamics (MD).
arXiv:2608. 02642v1 Announce Type: cross Abstract: Accelerating scientific discovery is among the most consequential applications of AI, and computational biomolecular simulation stands out as a particularly promising target within this broader effort.
R-GroundBench is a new diagnostic benchmark for evaluating AI models on R‑group grounding in Markush molecular editing, derived from real pharmaceutical patents. It includes a Multiple‑Choice VQA track with varying difficulty and modality splits, as well as an open‑ended Generation track. Experiments show a large performance gap: models score over 90% on easy VQA but drop to 56–66% on hard VQA, and generation exact match stays below 20% (and under 8% with visual input).
MolDesignBench is a new benchmark for evaluating large language model (LLM)-based agents in scenario‑grounded molecular design. It contains 2,000 generation and optimization tasks that blend implicit narrative requirements with explicit property and functional‑group constraints, including infeasible cases, and require the use of 17 specialized chemistry tools. Experiments with leading LLMs show low success rates (best ~43%) and highlight failures in implicit‑constraint reasoning, infeasibility detection, and tool usage, underscoring the benchmark’s role in identifying key bottlenecks for future research.
arXiv:2606. 09520v1 Announce Type: cross Abstract: Can a general-purpose large language model design molecules with the precision of a seasoned chemist?
The paper introduces LibStructBench, a document‑grounded benchmark designed to assess whether AI agents can accurately reconstruct sequencing‑library structures and molecular workflows from protocols. Experiments reveal that current state‑of‑the‑art agents often generate roughly correct reconstructions but struggle with exact molecular‑state representation and workflow connectivity. The study also finds that changing the underlying language model yields larger performance differences than changing agent harnesses, and that additional knowledge only modestly improves oligo‑family recovery without affecting overall workflow accuracy.
arXiv:2605. 26540v2 Announce Type: replace-cross Abstract: Energetic materials power mining, demolition, propulsion and airbags, yet today's compounds were designed decades ago.
arXiv:2608. 06961v1 Announce Type: new Abstract: Early-stage molecular design is an iterative process, not just a task of generating molecules.
arXiv:2606. 19624v1 Announce Type: new Abstract: Reliable benchmarking is critical for developing machine learning models for tandem mass spectrometry (MS/MS) based molecule discovery.
The paper identifies a phenomenon called principle-level search collapse in feedback-driven large language model (LLM) optimizers, where the search narrows to a limited set of design principles despite ongoing candidate generation and minor improvements. Using a diagnostic benchmark across molecular optimization, network sentinel-node selection, and GPU-kernel optimization, the authors find that 78.3% of runs exhibit collapse, with 55.3% showing strong support. They evaluate common remedies such as diversity injection and reference-principle interventions, noting limited and inconsistent benefits, and emphasize the need for online collapse detection and improved mechanisms for generating and maintaining alternative principles.
arXiv:2606. 03660v1 Announce Type: new Abstract: Large language models are increasingly used as chemistry assistants, yet most chemistry benchmarks still score only final answers.
arXiv:2609.36683v1 Announce Type: new Abstract: Molecular optimization is inherently iterative: a candidate is proposed, evaluated against several objectives, and revised while preserving a relations...