Brief

Researchers have developed a new framework called Protein Thoughts to improve the discovery of protein-protein interactions (PPIs). This system breaks down binding evidence into four distinct biological signals: sequence similarity, structural complementarity, interface balance, and chemical compatibility. By preserving these individual signals, Protein Thoughts offers a transparent method for ranking and auditing potential interactions, moving beyond opaque scoring systems. The framework utilizes a hypothesis-guided Tree-of-Thoughts search and a fine-tuned language model to efficiently explore candidate spaces and guide the search process. AI

Researchers have developed CoFEH, a novel framework that integrates Large Language Models (LLMs) with Bayesian Hyperparameter Optimization (HPO) for end-to-end automated machine learning. This system uses an LLM with a Tree of Thought approach to generate flexible feature engineering pipelines and a Bayesian optimization module for HPO. CoFEH uniquely interleaves these processes, allowing for informed decision-making between feature engineering and hyperparameter tuning, which has shown superior performance compared to existing methods. AI