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A leakage-controlled benchmark for protein-ligand binding affinity prediction. It runs nine models, sequence-based and structure-based, over the same targets under the same metrics, and varies the input structure so that pose quality can be told apart from model quality.
us (code pays fourni par la source)
A leakage-controlled benchmark for protein-ligand binding affinity prediction. It runs nine models, sequence-based and structure-based, over the same targets under the same metrics, and varies the input structure so that pose quality can be told apart from model quality.
us (code pays fourni par la source)
This record is the v1.0.0 source snapshot of PLABench, the code behind the paper of the same name. PLABench is a leakage-controlled benchmark for protein-ligand binding affinity prediction: it runs nine models, sequence-based and structure-based, over the same targets under the same …
us (code pays fourni par la source)
This record is the v1.0.0 source snapshot of PLABench, the code behind the paper of the same name. PLABench is a leakage-controlled benchmark for protein-ligand binding affinity prediction: it runs nine models, sequence-based and structure-based, over the same targets under the same …
us (code pays fourni par la source)
PLABench is a leakage-controlled benchmark for protein-ligand binding affinity prediction. This deposit contains the inputs, predicted structures, trained weights, and scored predictions from the paper “Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction”. The code and the …
us (code pays fourni par la source)
PLABench is a leakage-controlled benchmark for protein-ligand binding affinity prediction. This deposit contains the inputs, predicted structures, trained weights, and scored predictions from the paper “Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction”. The code and the …
us (code pays fourni par la source)
Abstract Accurate genome-wide prediction of transcription factor (TF)–DNA binding remains challenging because many models focus mainly on DNA sequence and overlook chromatin context and TF structure. We previously developed TransBind, a protein-aware model that combines TF and DNA representations through cross-attention. Here, …
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Dataset for TransBind-2, a protein-aware deep learning model for transcription factor (TF) binding site prediction using DNase-seq accessibility, genome uniqueness/mappability, and ProstT5 protein embeddings with bidirectional cross-attention fusion. Dnase_data.zip — DNase-seq chromatin accessibility signal (HDF5): `train_dnase.h5`, `val_dnase.h5`, `test_dnase.h5`. train_val_test_data.zip — One-hot DNA …
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This is the dataset for the paper, Improving protein function prediction by learning and integrating representations of protein sequences and function labels. TransFew: Improving protein function prediction by learning and integrating representations of protein sequences and function labels GitHub Repository: TransFew Journal …
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Frimpong Boadu, Yanli Wang, Jianlin Cheng
This dataset contains the model weights and inference data for FunBind, a unified multimodal AI model for generalizable zero-shot and supervised protein function prediction. FunBind: A Unified Multimodal Model for Generalizable Zero-Shot and Supervised Protein Function Prediction GitHub Repository: FunBind Journal Publication: …
us (code pays fourni par la source)
This is the dataset for the paper, Improving protein function prediction by learning and integrating representations of protein sequences and function labels. TransFew: Improving protein function prediction by learning and integrating representations of protein sequences and function labels GitHub Repository: TransFew Journal …
us (code pays fourni par la source)
Frimpong Boadu, Yanli Wang, Jianlin Cheng
This dataset contains the model weights and inference data for FunBind, a unified multimodal AI model for generalizable zero-shot and supervised protein function prediction. FunBind: A Unified Multimodal Model for Generalizable Zero-Shot and Supervised Protein Function Prediction GitHub Repository: FunBind Journal Publication: …
us (code pays fourni par la source)
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