#chemistry-ai News & Analysis

4 articles tagged with #chemistry-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 236/10

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MMGNN: Multi-level, multi-color graph neural networks for molecular property prediction

Researchers introduce MMGNN (Multi-level, Multi-color Graph Neural Networks), a novel neural network architecture that decomposes molecular graphs into interaction-specific subgraphs to improve molecular property prediction. The framework demonstrates competitive performance across multiple benchmarks, with variants optimized for topological and geometric molecular representations.

AINeutralarXiv – CS AI · Jun 86/10

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RETROSPECT: RETROsynthesis via Sequential Prediction, and Chemically Transformed-ranking

RETROSPECT introduces a modular retrosynthesis system combining a Transformer-based proposal model with LambdaMART reranking to improve chemical synthesis prediction. The system achieves 55% top-1 accuracy on USPTO-50K benchmarks, demonstrating that decomposing retrosynthesis into proposal generation and learned selection improves both ranking quality and candidate diversity.

AIBullisharXiv – CS AI · Jun 26/10

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When Single Answer Is Not Enough: Rethinking Single-Step Retrosynthesis Benchmarks for LLMs

Researchers propose a new benchmarking framework for evaluating large language models in retrosynthesis planning, introducing ChemCensor—a metric prioritizing chemical plausibility over exact-match accuracy—and CREED, a dataset of millions of validated reaction records that improves model performance beyond existing LLM baselines.

AIBullisharXiv – CS AI · May 296/10

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EvoMD-LLM: Learning the Language of Species Evolution in Reactive Molecular Dynamics

Researchers introduce EvoMD-LLM, a framework that adapts large language models to predict molecular dynamics by treating chemical reactions as temporal sequences with duration-aware tokens. The model achieves 66.14% accuracy on prediction tasks and demonstrates the ability to generate explanations for its predictions without explicit supervision, suggesting LLMs can effectively ground themselves in physical simulations through symbolic temporal modeling.