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.

AlphaSeq, launched by A-Alpha Bio, has broken the limitations of protein-protein interaction (PPI) prediction, becoming the world's largest PPI dataset with over 750 million measurement results. It contrasts the success of AlphaFold in protein structure prediction and excels particularly in PPI prediction, simplifying the process through innovative methods. The experimental platform of AlphaSeq efficiently measures the binding affinities of millions of PPIs, accelerating the pace of protein research. With expertise in computational biology...

Evo is a genomic foundation model capable of multi-modal and multi-scale learning. Evo can generate genomic-scale DNA sequences. Evo goes beyond single molecules and short sequences, allowing for the design of synthetic multi-component biological systems. Evo can predict essential genes in bacteria or phages at the whole genome scale.

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.

Dutch-Swiss startup Cradle has successfully raised $240 million, aiming to use AI to help scientists design and engineer proteins faster and more cost-effectively. Cradle employs Generative AI technology, leveraging laboratory data and billions of protein sequences to enhance protein design and accelerate R&D. This technology is expected to address protein engineering challenges that traditional tools cannot handle, providing biopharmaceutical companies with quicker and more successful experimental outcomes. Cradle has attracted Index.

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.

Harvard and Columbia have released the open dataset OpenProteinSet, containing 16 million protein multiple sequence alignments and related data to support AI model training. AlphaFold 2 has achieved a milestone in the accuracy of protein structure predictions, but its proprietary data has limited progress for other researchers. OpenProteinSet includes MSAs for all proteins in the PDB, providing ample precomputed MSA resources for the protein machine learning community. This dataset can be used for various tasks in structural biology.