Kyle Tretina – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-06-13T17:16:32Z http://www.open-lab.net/blog/feed/ Kyle Tretina <![CDATA[Accelerated Sequence Alignment for Protein Science with MMseqs2-GPU and NVIDIA NIM]]> http://www.open-lab.net/blog/?p=97938 2025-06-13T17:16:32Z 2025-06-12T20:57:03Z Protein sequence alignment��comparing protein sequences for similarities��is fundamental to modern biology and medicine. It illuminates gene functions by...]]>

Protein sequence alignment—comparing protein sequences for similarities—is fundamental to modern biology and medicine. It illuminates gene functions by reconstructing evolutionary relationships, technically called homology inference, that can inform drug development. When scientists discover or design a new protein, they can align it with known protein sequences to infer its structure and function.

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Kyle Tretina <![CDATA[Guiding Generative Molecular Design with Experimental Feedback Using Oracles]]> http://www.open-lab.net/blog/?p=96966 2025-03-25T17:23:57Z 2025-03-19T15:00:00Z Generative chemistry with AI has the potential to revolutionize how scientists approach drug discovery and development, health, and materials science and...]]>

Generative chemistry with AI has the potential to revolutionize how scientists approach drug discovery and development, health, and materials science and engineering. Instead of manually designing molecules with “chemical intuition” or screening millions of existing chemicals, researchers can train neural networks to propose novel molecular structures tailored to the desired properties.

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Kyle Tretina <![CDATA[Understanding the Language of Life��s Biomolecules Across Evolution at a New Scale with Evo 2]]> http://www.open-lab.net/blog/?p=95589 2025-04-23T02:44:28Z 2025-02-19T17:14:51Z AI has evolved from an experimental curiosity to a driving force within biological research. The convergence of deep learning algorithms, massive omics...]]>

AI has evolved from an experimental curiosity to a driving force within biological research. The convergence of deep learning algorithms, massive omics datasets, and automated laboratory workflows has allowed scientists to tackle problems once thought intractable—from rapid protein structure prediction to generative drug design, increasing the need for AI literacy among scientists.

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Kyle Tretina <![CDATA[Evaluating GenMol as a Generalist Foundation Model for Molecular Generation]]> http://www.open-lab.net/blog/?p=94836 2025-01-23T19:54:29Z 2025-01-13T14:00:00Z Traditional computational drug discovery relies almost exclusively on highly task-specific computational models for hit identification and lead optimization....]]>

Traditional computational drug discovery relies almost exclusively on highly task-specific computational models for hit identification and lead optimization. Adapting these specialized models to new tasks requires substantial time, computational power, and expertise—challenges that grow when researchers simultaneously work across multiple targets or properties.

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Kyle Tretina <![CDATA[Accelerate Protein Engineering with the NVIDIA BioNeMo Blueprint for Generative Protein Binder Design]]> http://www.open-lab.net/blog/?p=94851 2025-01-23T19:54:28Z 2025-01-13T14:00:00Z Designing a therapeutic protein that specifically binds its target in drug discovery is a staggering challenge. Traditional workflows are often a painstaking...]]>

Designing a therapeutic protein that specifically binds its target in drug discovery is a staggering challenge. Traditional workflows are often a painstaking trial-and-error process—iterating through thousands of candidates, each synthesis and validation round taking months if not years. Considering the average human protein is 430 amino acids long, the number of possible designs translates to…

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Kyle Tretina <![CDATA[Accelerate Drug and Material Discovery with New Math Library NVIDIA cuEquivariance]]> http://www.open-lab.net/blog/?p=91896 2024-11-18T22:58:58Z 2024-11-18T18:30:00Z AI models for science are often trained to make predictions about the workings of nature, such as predicting the structure of a biomolecule or the properties of...]]>

AI models for science are often trained to make predictions about the workings of nature, such as predicting the structure of a biomolecule or the properties of a new solid that can become the next battery material. These tasks require high precision and accuracy. What makes AI for science even more challenging is that highly accurate and precise scientific data is often scarce…

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Kyle Tretina <![CDATA[Boost Alphafold2 Protein Structure Prediction with GPU-Accelerated MMseqs2]]> http://www.open-lab.net/blog/?p=91623 2024-11-14T17:10:35Z 2024-11-13T17:00:00Z The ability to compare the sequences of multiple related proteins is a foundational task for many life science researchers. This is often done in the form of a...]]>

The ability to compare the sequences of multiple related proteins is a foundational task for many life science researchers. This is often done in the form of a multiple sequence alignment (MSA), and the evolutionary information retrieved from these alignments can yield insights into protein structure, function, and evolutionary history. Now, with MMseqs2-GPU, an updated GPU-accelerated…

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Kyle Tretina <![CDATA[Unlock Gene Networks Using Limited Data with AI Model Geneformer]]> http://www.open-lab.net/blog/?p=85400 2024-07-30T23:34:35Z 2024-07-15T12:00:00Z Geneformer is a recently introduced and powerful AI model that learns gene network dynamics and interactions using transfer learning from vast single-cell...]]>

Geneformer is a recently introduced and powerful AI model that learns gene network dynamics and interactions using transfer learning from vast single-cell transcriptome data. This tool enables researchers to make accurate predictions about gene behavior and disease mechanisms even with limited data, accelerating drug target discovery and advancing understanding of complex genetic networks in…

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