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# Protein Inverse Folding
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Protein inverse folding represents a computational technique aimed at generating protein sequences that will fold into specific three-dimensional structures. The central challenge in protein inverse folding involves identifying sequences capable of reliably adopting the intended structure. In our research, we concentrate on designing sequences based on the known backbone structure of a protein, represented with 3D coordinates of the atoms of the backbone (without any information about what the individual amino-acids are). Specifically. we finetune the [AIDO.Protein-16B](https://huggingface.co/genbio-ai/AIDO.Protein-16B) model with LoRA on the [CATH 4.2](https://pubmed.ncbi.nlm.nih.gov/9309224/) benchmark dataset. We use the same train, validation, and test splits used by the previous studies, such as [LM-Design](https://arxiv.org/abs/2302.01649), and [DPLM](https://arxiv.org/abs/2402.18567). Current version of ModelGenerator contains the inference pipeline for protein inverse folding. Experimental pipeline on other datasets (both training and testing) will be included in the future.
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##### Experimental Results
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# Protein Inverse Folding
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[](https://github.com/genbio-ai/ModelGenerator/blob/main/LICENSE)
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Protein inverse folding represents a computational technique aimed at generating protein sequences that will fold into specific three-dimensional structures. The central challenge in protein inverse folding involves identifying sequences capable of reliably adopting the intended structure. In our research, we concentrate on designing sequences based on the known backbone structure of a protein, represented with 3D coordinates of the atoms of the backbone (without any information about what the individual amino-acids are). Specifically. we finetune the [AIDO.Protein-16B](https://huggingface.co/genbio-ai/AIDO.Protein-16B) model with LoRA on the [CATH 4.2](https://pubmed.ncbi.nlm.nih.gov/9309224/) benchmark dataset. We use the same train, validation, and test splits used by the previous studies, such as [LM-Design](https://arxiv.org/abs/2302.01649), and [DPLM](https://arxiv.org/abs/2402.18567). Current version of ModelGenerator contains the inference pipeline for protein inverse folding. Experimental pipeline on other datasets (both training and testing) will be included in the future.
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##### Experimental Results
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