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AI is revolutionizing life sciences. Biocomputing company ProFluent recently launched ProGen3, a powerful generative protein language model (PLM) poised to drive breakthroughs in antibodies, industrial enzymes, and gene editing. Research shows ProGen3's scale and design optimizations enable the generation of highly functional novel proteins, potentially reshaping our understanding of biology. Proteins are vital molecules within living organisms, responsible for diverse physiological functions, from catalyzing reactions to recognition.

Artificial intelligence is rapidly transforming protein discovery and design in biotechnology. Recently, a collaborative team from UC Berkeley and Caltech developed ProteinDT, a novel multimodal framework leveraging text descriptions to aid in protein design. This innovative approach integrates protein sequence and structural information with a wealth of biological knowledge represented in text, opening a new chapter in protein design.

Recently, a video has sparked widespread attention online, featuring an account impersonating Zhang Wenhong in a live stream selling a type of protein bar. Many netizens began to question, 'Is Zhang Wenhong really selling products?' Even more concerning is that some elderly viewers believed the video to be genuine, with some placing orders and sharing the information in social groups. After investigation, it was confirmed that the Zhang Wenhong in the video was not a real person, but a deepfake generated through AI technology. Zhang Wenhong has responded, indicating that there is more than one such fraudulent account.

Dutch and Swiss protein engineering AI startup Cradle recently announced the successful completion of a $73 million Series B funding round. This round was led by investment firm IVP, with participation from existing investors Index Ventures and Kindred Capital. So far, Cradle has raised over $100 million in total funding. Image source note: Image generated by AI and authorized by service provider Midjourney. Cradle is committed to

In the biotech field, leveraging artificial intelligence to accelerate research has become a new norm. Recently, biotech company Cradle announced it has completed a $73 million funding round, aiming to further expand its laboratories and team. Founded in 2022, Cradle is dedicated to exploring the applications of language models in biotechnology. Founder and CEO Stef van Grieken has vividly referred to the combinations of amino acids and bases as an 'alien programming language', although this language can also be processed by AI models.

Now, scientists can precisely "design" proteins as if they are programming. This is no longer science fiction but a reality being created by the Dutch company Cradle. In the grand tapestry of biotechnology, Cradle's founder, Stefan van Groningen, likens proteins to an "alien programming language." His team is utilizing artificial intelligence to decipher this mysterious language, bringing revolutionary changes to biotechnology. Traditional protein development has been like a long and costly blind exploration. Researchers must go through dozens of experimental rounds, each of which may...

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...

The artificial intelligence research lab EvolutionaryScale, founded by a former Meta engineer, recently launched ESM3, a biological design tool with native multimodality and generative language modeling.

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.