Christof Angermüller
@cangermueller
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Software Engineer at Google
Mountain View, CA
Joined November 2012
Excited to announce our latest paper on ML-guided design of single-domain antibodies against CoV-2; A collaboration between @GoogleDeepMind, @AAlphaBio and @LumenBio.
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After only 3 rounds of ML-based design and wet-lab testing, we were able to significantly improve the efficacy against multiple CoV2 strains, including Delta and Omicron. Designed Abs are diverse with up to 15 mutations, providing a therapeutic hedge against future pathogens.
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RT @DdelAlamo: "Sensitive remote homology search by local alignment of small positional embeddings from protein language models". ESM2 embe….
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Contextualizing protein representations using deep learning on protein networks and single-cell data.
biorxiv.org
Understanding protein function and discovering molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein...
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Self-play reinforcement learning guides protein engineering.
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The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures.
biorxiv.org
Antibodies are key proteins of the adaptive immune system, and there exists a large body of academic literature and patents dedicated to their study and concomitant conversion into therapeutics,...
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Sequence-based drug design as a concept in computational drug design.
nature.com
Nature Communications - Conventional structure-based drug design pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, the authors report a...
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RT @DdelAlamo: “Large language models are universal biomedical simulators”. Another use of GPT-4 for biomedical sciences research. https://….
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Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries.
nature.com
Nature Communications - Therapeutic antibody discovery is time and cost-intensive. Here, the authors develop a machine learning-driven method enabling accelerated design of large and diverse...
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Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models.
arxiv.org
Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However, their...
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Contrastive learning in protein language space predicts interactions between drugs and protein targets.
pnas.org
Sequence-based prediction of drug–target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such co...
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PoET: A generative model of protein families as sequences-of-sequences.
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ProteinChat: Towards Achieving ChatGPT-Like Functionalities on Protein 3D Structures.
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ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback.
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A Multimodal Protein Representation Framework for Quantifying Transferability Across Biochemical Downstream Tasks.
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RT @omarsar0: BiomedGPT, a unified biomedical generative pretrained transformer model for vision, language, and multimodal tasks. Achieves….
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RT @DdelAlamo: “Self-driving laboratories to autonomously navigate the protein fitness landscape”. Four independent trajectories “quickly c….
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A general model to predict small molecule substrates of enzymes based on machine and deep learning.
nature.com
Nature Communications - For many enzymes, it is unknown which primary and/or secondary reactions they catalyze. Here, the authors use machine and deep learning to develop a general model for the...
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