TL;DR
The accuracy, generalizability, data scalability, and ease of use of this work demonstrate that FuelProp-LM can serve as a practical and flexible tool for fuel property prediction and play a significant role in future AI-assisted fuel design.
CRISP is introduced, a large language model-assisted framework that treats representation construction as a rule-space exploration and compilation problem: it repeatedly samples target-relevant chemical rules without access to structures, labels or data splits, consolidates related concepts, and compiles each into an e...
This study comprehensively benchmarked these methods for predicting the properties of small organic molecules and showed that TFM consistently outperform XGBoost, CatBoost, multilayer perceptrons, and other descriptor-based methods, even with careful hyperparameter selection for the baselines.
CoMPASS is presented, a retrieval-calibrated framework for small-large model collaboration that retains a graph attention network as the predictive anchor, retrieves locally relevant training molecules, provides attention-grounded evidence to an LLM, and converts its proposal into a bounded correction through an agreem...
The experiments show that existing molecular LLMs and strong proprietary models such as GPT-5.2 and Gemini-3-Flash show limited reliability in substituent contribution prediction, and training on MolSC substantially improves this ability and achieves strong performance across diverse downstream molecular tasks.
Monroe is presented, a new MFM with several innovations over the existing state of the art: increased scale allowing pre-training on over 81 million molecules from the PM6 quantum chemistry dataset; improved graph representation of stereochemistry; improved training losses including conformer denoising and embedding de...
Large language models (LLMs) have significantly revolutionized natural language processing through their strong capabilities in text generation and reasoning. Yet, their applicability to bioinformatics applications remains largely unexplored. Here, we systematically evaluate state‐of‐the‐art LLMs across six represent...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
