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Rahmad Akbar on Muck Rack

Rahmad Akbar

As seen in: Nature, BioRxiv, Cell Reports, The Journal of Immunology
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Articles

Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability - Communications Biology

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By Habib Bashour, Eva Smorodina, Jahn Zhong, Rahmad Akbar, Konrad Krawczyk, Geir Kjetil Sandve, Jan Terje Andersen, Jose Gutierrez-Marcos
| Nature Verified
Abstract Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as “developability”, which reflects an antibody’s ability to progress through development stages based on its physicochemical properties.
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Linguistics-based formalization of the antibody language as a basis for antibody language models - Nature Computational Science Original

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By Mai Vu, Philippe Robert, Rahmad Akbar, Bartlomiej Swiatczak, Geir Kjetil Sandve
| Nature Verified
Abstract Apparent parallels between natural language and antibody sequences have led to a surge in deep language models applied to antibody sequences for predicting cognate antigen recognition. However, a linguistic formal definition of antibody language does not exist, and insight into how antibody language models capture antibody-specific binding features remains largely uninterpretable.
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Linguistically inspired roadmap for building biologically reliable protein language models - Nature Machine Intelligence

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By Mai Vu, Rahmad Akbar, Philippe Robert, Geir Kjetil Sandve, Victor Greiff, Bartlomiej Swiatczak
| Nature Verified
Abstract Deep neural-network-based language models (LMs) are increasingly applied to large-scale protein sequence data to predict protein function. However, being largely black-box models and thus challenging to interpret, current protein LM approaches do not contribute to a fundamental understanding of sequence–function mappings, hindering rule-based biotherapeutic drug development.
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