Protein Language Model

Dataset Description: The interaction of 72 kinase inhibitors with 442 kinases covering >80% of the human catalytic protein kinome. Task Description: Regression. Given the target amino acid sequence/compound SMILES string, predict their binding affinity. Dataset Statistics: 0.3.2 Update: 25,772 DTI pairs, 68 drugs, 379 proteins. Before: 27,621 DTI pairs, 68 drugs, 379 proteins. [1] Davis, M., Hunt, J., Herrgard, S. et al. Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol 29, 1046–1051 (2011). [2] Huang, Kexin, et al. “DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction” Bioinformatics.

ProteinGym is a collection of benchmarks aiming at comparing the ability of models to predict the effects of protein mutations. The benchmarks in ProteinGym are divided according to mutation type (substitutions vs. indels), ground truth source (DMS assay vs. clinical annotation), and training regime (zero-shot vs. supervised). Pascal Notin, Aaron Kollasch, Daniel Ritter, Lood Van Niekerk, Steffanie Paul, Han Spinner, Nathan Rollins, Ada Shaw, Rose Orenbuch, Ruben Weitzman, Jonathan Frazer, Mafalda Dias, Dinko Franceschi, Yarin Gal, and Debora Marks https://github.com/OATML-Markslab/ProteinGym

The CATH (Class, Architecture, Topology, Homology) [65] database is a comprehensive resource for protein structure classification that hierarchical group proteins based on their structural features. The database defines classes based on topological similarities, architectures based on the arrangement of secondary structure elements, topologies based on the connectivity of secondary structure elements, and homologous domains based on sequence similarity.

The CATH (Class, Architecture, Topology, Homology) [65] database is a comprehensive resource for protein structure classification that hierarchical group proteins based on their structural features. The database defines classes based on topological similarities, architectures based on the arrangement of secondary structure elements, topologies based on the connectivity of secondary structure elements, and homologous domains based on sequence similarity. This results in a training set of 16,153 structures, a validation set of 1,457 structures, and a test set of 1,797 structures. Note that the curated CATH dataset contains only single-chain structures and does not consider the case of designing multi-chain proteins.

OOD split of the Mol-Instructions Dataset about Protein Annotation. Download link: https://huggingface.co/datasets/TimeRune/Mol-Inst-OOD