Graph representation learning in biomedicine and healthcare (Nature Biomedical Engineering 2022.10) [Paper]
Learning functional properties of proteins with language models (Nature Machine Intelligence 2022.04) [Paper] [Code]
[CTR] Clustered tree regression to learn protein energy change with mutated amino acid (Briefings in Bioinformatics 2022.11) [Paper] [Code]
[MIF-ST] Masked inverse folding with sequence transfer for protein representation learning (BioRxiv 2022.11) [Paper]
[CARP] Convolutions are competitive with transformers for protein sequence pretraining (BioRxiv 2022.11) [Paper] [Code]
[PLMs] Exploring evolution-aware & -free protein language models as protein function predictors (NeurIPS 2022.10) [Paper] [Code]
[ESM-2] Language models of protein sequences at the scale of evolution enable accurate structure prediction (BioRxiv 2022.10) [Paper] [Code]
[I-TASSER-MTD] I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction (Nature Protocols 2022.08) [paper] [Code]
[ATOMRefine] Atomic protein structure refinement using all-atom graph representations and SE(3)-equivariant graph neural networks (BioRxiv 2022.08) [paper]
[Cytoself] Self-supervised deep learning encodes high-resolution features of protein subcellular localization (Nature Methods 2022.07) [Paper] [Code]
[ProtGPT2] ProtGPT2 is a deep unsupervised language model for protein design (Nature Communications 2022.07) [Paper][Code]
[OmegaFold] High-resolution de novo structure prediction from primary sequence (BioRxiv 2022.07) [Paper]
[generative_IDPs] Artificial Intelligence Guided Conformational Mining of Intrinsically Disordered Proteins (Communications Biology 2022.06) [Paper] [Code]
[Effector-GAN] Effector-GAN: prediction of fungal effector proteins based on pretrained deep representation learning methods and generative adversarial networks (Bioinformatics 2022.05) [Paper] [Code] [Web server]
Learning meaningful representations of protein sequences (Nature Communications 2022.04) [Paper] [Code]
[Evolocity] Evolutionary velocity with protein language models predicts evolutionary dynamics of diverse proteins (Cell Systems 2022.04) [Paper] [Code] [Chinese blog]
[ProteinBERT] ProteinBERT: a universal deep-learning model of protein sequence and function (Bioinformatics 2022.04) [Paper] [Code]
Using deep learning to annotate the protein universe (Nature Biotechnolog 2022.02) [Paper] [Code]
[trRosetta] The trRosetta server for fast and accurate protein structure prediction (Nature protocols 2021.11) [Web server]
[PMLM] Pre-training co-evolutionary protein representation via a pairwise masked language model (Arxiv 2021.10) [Paper]
[ESM-1v] Language models enable zero-shot prediction of the effects of mutations on protein function (NeurIPS 2021.09) [Paper] [Code]
[NeuroSEED] Neural Distance Embeddings for Biological Sequences (NeurIPS 2021.09) [Paper] [Code]
[MSA Transformer] MSA Transformer (ICML 2021.09) [Paper] [Code]
[RoseTTAFold] Accurate prediction of protein structures and interactions using a three-track neural network (Science 2021.08) [Paper] [Code] [pdf]
[ProteinLM] Modeling Protein Using Large-scale Pretrain Language Model (KDD: the International Workshop on Pretraining: Algorithms, Architectures, and Applications 2021.08) [Paper] [Code]
[AlphaAFold] Highly accurate protein structure prediction with AlphaFold (Nature 2021.07) [Paper] [Code]
[pLMs] ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Learning (TPAMI 2021.07) [Paper] [Code]
Learning the protein language: Evolution, structure, and function (Cell Systems 2021.06) [Paper] [Code]
Combining evolutionary and assay-labelled data for protein fitness prediction (bioRxiv 2021.03) [Paper]
[TAPE] Evaluating Protein Transfer Learning with TAPE (NeurIPS 2019.09) [Paper] [Code] [Chinese blog]
[DeepRank-GNN] DeepRank-GNN: A Graph Neural Network Framework to Learn Patterns in Protein-Protein Interfaces (Bioinformatics 2022.11) [Paper] [Code]
[GearNet] Protein representation learning by geometric structure pretraining (Arxiv 2022.09) [Paper] [Code]
[DW-GNN] Directed Weight Neural Networks for Protein Structure Representation Learning (Arxiv 2022.09) [Paper]
[GBPNet] GBPNet: Universal Geometric Representation Learning on Protein Structures (KDD 2022.08) [Paper] [Code] [pdf]
[STEPS] Structure-aware Protein Self-supervised Learning (Arxiv 2022.06) [Paper]
[GNN_F, GNN_P] Decoding the protein–ligand interactions using parallel graph neural networks (Scientific Reports 2022.05) [Paper] [Code]
[CRL3DProt] Contrastive Representation Learning for 3D Protein Structures (Arxiv 2022.05) [Paper]
[BACPI] BACPI: a bi-directional attention neural network for compound–protein interaction and binding affinity prediction (Bioinformatics 2022.04) [Paper] [Code]
[STAMP-DPI] Structure-Aware Multimodal Deep Learning for Drug–Protein Interaction Prediction (JCIM 2022.04) [Paper] [Code]
[GraphSite] Fast AlphaFold2-aware protein–DNA binding site prediction using graph transformer (Briefings in Bioinformatics 2022.03) [Paper] [Code] [Web server]
[GraSR] Fast protein structure comparison through effective representation learning with contrastive graph neural networks (PLOS Computational Biology 2022.03) [Paper] [Code]
[InteractionGraphNet] InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein−Ligand Interaction Predictions (JMC 2021.12) [Paper] [Code]
[nn4dms] Neural networks to learn protein sequence–function relationships from deep mutational scanning data (PNAS 2021.11) [Paper] [Code]
[PG-GNN] Geometric Graph Representation Learning on Protein Structure Prediction (KDD 2021.08) [Paper]
Distillation of MSA Embeddings to Folded Protein Structures with Graph Transformers (bioRxiv 2021.06) [Paper]
[DeepFRI] Structure-based protein function prediction using graph convolutional networks (Nature Communications 2021.05) [Paper] [Code]
[PtsRep] Self-Supervised Representation Learning of Protein Tertiary Structures (PtsRep) and Its Implications for Protein Engineering (bioRxiv 2021.03) [Paper]
[GraphQA] GraphQA: protein model quality assessment using graph convolutional networks (Bioinformatics 2021.02) [Paper] [Code]
[GVP] Learning from Protein Structure with Geometric Vector Perceptrons (ICLR 2021.01) [Paper] [Code]
[IEConv_proteins] Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures (ICLR 2021.01) [Paper] [Code]
[masif-seed] De novo design of site-specific protein interactions with learned surface fingerprints (bioArxiv 2022.06) [Paper] [Code]
[PointSite] PointSite: A Point Cloud Segmentation Tool for Identification of Protein Ligand Binding Atoms (JCIM 2022.05) [Paper] [Code]
A point cloud-based deep learning strategy for protein–ligand binding affinity prediction (Briefings in Bioinformatics 2022.01) [Paper]
[DeepRank] DeepRank: a deep learning framework for data mining 3D protein-protein interfaces (Nature Communications 2021.12) [Paper] [Code]
[ProtConv] Convolutional neural networks with image representation of amino acid sequences for protein function prediction (Computational Biology and Chemistry 2021.06) [Paper] [Code]
[dMaSIF] Fast End-to-End Learning on Protein Surfaces (CVPR 2021.03) [Paper] [Code]
[CPAC] Cross-Modality and Self-Supervised Protein Embedding for Compound–Protein Affinity and Contact Prediction (Bioinformatics 2022.09) [Paper][Code]
[OntoProtein] OntoProtein: Protein Pretraining With Gene Ontology Embedding (ICLR 2022.01) [Paper][Code]
[LM-GVP] NeuralLM-GVP: A Generalizable Deep Learning Framework for Protein Property Prediction from Sequence and Structure (bioRxiv 2021.09) [Paper] [Code]
[HOLOPROT] Multi-Scale Representation Learning on Proteins (NeurIPS 2021.09) [Paper] [Code]
Toward More General Embeddings for Protein Design: Harnessing Joint Representations of Sequence and Structure (bioRxiv 2021.09) [Paper]
Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function (Bioinformatics 2021.01) [Paper] [Code]