ESMFold
ESMFold shows how AI can give us new tools to understand the natural world, much like the microscope, which enabled us to see into the world at an infinitesimal scale and opened up a whole new understanding of life. AI can help us understand the immense scope of natural diversity, and see biology in a new way. Much of AI research has focused on helping computers understand the world in a way similar to how humans do. The language of proteins is one that is beyond human comprehension and has eluded even the most powerful computational tools. AI has the potential to open up this language to our understanding. Studying AI in new domains such as biology can also give insight into artificial intelligence more broadly. Our work reveals connections across domains: large language models that are behind advances in machine translation, natural language understanding, speech recognition, and image generation are also able to learn deep information about biology.
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Genedata Biologics
Genedata Biologics® streamlines discovery of biotherapeutics including bispecifics, ADCs, TCRs, CAR-Ts, and AAVs. The most widely adopted platform across the industry, it integrates all discovery workflows so you can focus on true innovation. Accelerate research with a first-in-class platform uniquely designed from the start to digitalize biotherapeutic discovery. The platform facilitates complex R&D processes by designing, tracking, testing, and assessing novel biotherapeutics drugs. It works with any format, from antibodies, bi- or multi-specifics, ADCs, novel scaffolds, and therapeutic proteins, to engineered therapeutic cell lines such as TCRs and CAR-T cells. Acting as a central end-to-end data backbone, Genedata Biologics integrates all R&D processes, from library design and immunizations, selections and panning, molecular biology, screening, protein engineering, expression, purification, and protein analytics, to candidate developability and manufacturability assessments.
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Evo 2
Evo 2 is a genomic foundation model capable of generalist prediction and design tasks across DNA, RNA, and proteins. It utilizes a frontier deep learning architecture to model biological sequences at single-nucleotide resolution, achieving near-linear scaling of compute and memory relative to context length. Trained with 40 billion parameters and a 1 megabase context length, Evo 2 processes over 9 trillion nucleotides from diverse eukaryotic and prokaryotic genomes. This extensive training enables Evo 2 to perform zero-shot function prediction across multiple biological modalities, including DNA, RNA, and proteins, and to generate novel sequences with plausible genomic architecture. The model's capabilities have been demonstrated in tasks such as designing functional CRISPR systems and predicting disease-causing mutations in human genes. Evo 2 is publicly accessible via Arc's GitHub repository and is integrated into the NVIDIA BioNeMo framework.
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3decision
3decision® is a cloud-based protein structure repository designed for comprehensive structural data management and advanced analytics, enabling small molecule and biologics discovery teams to accelerate structure-based drug design.
It centralizes and standardizes experimental and in-silico protein structures from public sources like RCSB PDB and AlphaFoldDB, as well as proprietary data, supporting formats like PDBx/mmCIF and ModelCIF. This ensures easy access to X-Ray, NMR, cryo-EM, and modeled structures, fostering collaboration and enhancing research efforts.
Beyond storage, 3decision® enriches entries with metadata and sequence information, including protein-ligand interactions, antibody annotations, and binding site details. Advanced analytical tools identify druggable sites, assess off-target risks, and enable binding site comparisons, transforming vast structural data into actionable knowledge.
Its cloud-based platform facilitates collaboration among research teams.
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