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Is AI actually starting to ‘get smarter’?! The latest research from MIT indicates that the internal structure of large language models (LLMs) astonishingly resembles that of the human brain! This study utilized sparse autoencoder techniques to conduct an in-depth analysis of the activation space of LLMs, revealing three hierarchical structural features that are truly remarkable: first, at the microscopic level, researchers discovered the existence of structures akin to ‘crystals’. The surfaces of these ‘crystals’ are composed of parallelograms or trapezoids, similar to familiar lexical analogies such as ‘man:’

Recently, scientists published a study on protein-ligand complex structure prediction in the journal 'Nature Machine Intelligence'. The new method, NeuralPLexer, predicts structures using deep generative models with only the protein sequence and ligand molecule graph as input. NeuralPLexer leverages a multiscale deep learning system to enhance prediction success rates and is expected to play a significant role in drug discovery. This research provides insights for exploring the field of protein-ligand complex structure prediction.

Google DeepMind has discovered 2.2 million theoretically stable but unrealized crystal structures using AI tools, potentially advancing fields such as renewable energy and advanced computing. The plan is to make the most promising 381,000 structures available to other scientists. The new materials discovered using AI can be utilized in multifunctional layered materials and biomimetic computing applications.

The latest version of AlphaFold can predict the structures of proteins, nucleic acids, and molecules containing PTM, achieving high-accuracy atomic-level predictions for ligands, proteins, and nucleic acids. It significantly surpasses earlier versions in antibody-binding-related issues and is capable of predicting protein-ligand structures more quickly and accurately, aiding in the understanding of complex biological mechanisms and accelerating medical applications.