#chemistry

8 curated events

papersTODAY 04:00 UTC

Study Finds Chemical Chain-of-Thought in Reasoning Models Prone to Hallucination

A new arXiv paper examines how language models trained for chemical reasoning use chain-of-thought steps, and finds that the intermediate reasoning frequently contains fabricated content. Testing four reasoning model families across twelve chemistry tasks, the authors report that hallucination is widespread and largely disconnected from the final answer. The work suggests chain-of-thought traces in this domain act more like an unreliable scratchpad than a faithful record of the model's reasoning.

hallucination/reasoning/chain-of-thought/chemistry

papersTODAY 04:00 UTC

Autoregressive Fragment Model Generates Molecules at Specified Attachment Sites

A new arXiv preprint introduces Fraglingo, a generative approach that builds molecules by mimicking common medicinal-chemistry edits such as extending a scaffold or swapping a substituent. The method generates fragments conditioned on a chosen attachment point, letting users direct where a new piece is added while optimizing desired molecular properties. It falls under fragment-based molecular design research.

molecular-design/generative-models/chemistry/research

papersTODAY 04:00 UTC

Energy-Based Generative Model Proposed for Signal Unmixing and Curve Resolution

A new arXiv preprint introduces EB-gMCR, an energy-based generative approach for separating the individual component signals contained in a single mixed measurement, such as a chemical reaction mixture or tissue sample. The method aims to recover both each component's profile and its concentration from data where only the weighted sum is observed. It is positioned as a tool for signal unmixing and multivariate curve resolution tasks in chemistry and related fields.

research/generative-models/signal-processing/chemistry

papersTODAY 04:00 UTC

Ensemble-Conditioned Molecular Design Accounts for Conformer Distributions

A new arXiv preprint argues that molecular design should not be reduced to finding candidates that lock into one bioactive shape, since real molecules exist across a range of conformations. The authors propose an ensemble-conditioned method that designs molecules against this distribution of shapes rather than a single structure, aiming to better capture the properties that determine whether a candidate succeeds.

molecular-design/arxiv/generative-models/chemistry

papersTODAY 04:00 UTC

Mixed-condition training boosts multimodal spectra model for small-molecule ID

A new arXiv paper describes a multimodal deep learning approach that combines complementary spectroscopic data to identify small-molecule structures. The authors use domain knowledge and mixed-condition training so the model stays reliable when spectra are missing, degraded, or mismatched. This targets a common problem in practical molecular characterization, where real-world measurements rarely arrive in ideal form.

multimodal/spectroscopy/chemistry/robustness

papersSEP 10 04:00 UTC

Small Molecule Optimization with Large Language Models

A research paper on arXiv examines how large language models can be applied to molecular optimization, the task of designing small molecules with targeted properties. The work positions LLM-based approaches as a promising tool for drug discovery pipelines, where candidate compounds must be iteratively refined to meet specific criteria.

drug-discovery/llm/chemistry/research

papersSEP 10 04:00 UTC

uFlowCSP: Mean Flow Generative Models for Crystal Structure Prediction

Researchers introduce uFlowCSP, a generative approach to crystal structure prediction built on mean flow models. The method addresses a key drawback of diffusion- and flow-matching-based systems, which typically require many iterative sampling steps during inference. The paper situates the work next to existing generative CSP models such as CDVAE, DiffCSP, FlowMM, and CrystalFlow.

materials-science/generative-models/flow-matching/chemistry

papersSEP 12 04:00 UTC

ARCHE: Agentic AI System Automates Chemical Mechanism Discovery

Researchers introduced ARCHE, an autonomous agent-based system designed to investigate chemical reaction mechanisms with minimal expert involvement. The work targets the heavy reliance on human intervention in current computational chemistry workflows. Details on validation methods and performance results are presented in the arXiv preprint.

agents/research/chemistry/automation