Google DeepMind has released AlphaProteo, an artificial intelligence system focused on generating novel proteins tailored to specific target molecules, aimed at accelerating drug design, disease understanding, and health application research. AlphaProteo successfully designed binders for the protein VEGF-A, which is associated with tumor growth and various disease complications, outperforming traditional methods. Unlike AlphaFold, the proteins designed by this system can interact with biological processes, providing possibilities for developing targeted therapies against harmful proteins.

Generative AI is rapidly becoming a core tool across industries, with projections indicating it will attract over 100 million users by 2026. Industries such as healthcare, advertising, manufacturing, software development, financial services, and entertainment are extensively utilizing generative AI to significantly enhance work efficiency. In healthcare, AI enhances medical image analysis, designs new drugs, and personalizes treatment plans. The advertising industry efficiently generates content that aligns with brand tone using AI. In manufacturing, AI accelerates design processes and predicts equipment failures for intelligent maintenance. Software development leverages AI to generate and optimize code.

Inceptive achieves the design and generation of new proteins and drugs through large-scale learning of biological software languages. Inceptive applies deep learning to drug design, enabling programmable and scalable new drug development. Inceptive CEO Jakob Uszkoreit highlights that insufficient allocation of LLM computing resources is a key factor in inefficiency.

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.

Inceptive, founded by authors of the Transformer paper, aims to design mRNA molecules using AI. The team consists of pioneers in large models and complementary Stanford biology professors. The core strategy is to use machine learning to quickly design RNA sequences, shortening the drug development cycle. They have partnered with a major European pharmaceutical company to jointly develop new mRNA vaccines. Technological advancements are expected to reach a tipping point within three years, with RNA therapies being at the forefront.