- Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction
- Protein Secondary Structure Prediction with Long Short Term Memory Networks
- Predicting changes in protein thermostability brought about by single- or multi-site mutations
- NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation
- Engineering proteinase K using machine learning and synthetic genes
- Distributed Representations for Biological Sequence Analysis
- dna2vec: Consistent vector representations of variable-length k-mers
- Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
- Variational auto-encoding of protein sequences
- Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions
- End-to-End Learning on 3D Protein Structure for Interface Prediction
- Design by adaptive sampling
- Distance-based Protein Folding Powered by Deep Learning
- Conditioning by adaptive sampling for robust design
- Learning protein sequence embeddings using information from structure
- How to Hallucinate Functional Proteins
- Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
- Leveraging binding-site structure for drug discovery with point-cloud methods
- Evaluating Protein Transfer Learning with TAPE
- Iterative Peptide Modeling With Active Learning And Meta-Learning
- Protein Sequence Design with a Learned Potential
- Repertoires of G protein-coupled receptors for Ciona-specific neuropeptides
- Generative Modeling for Protein Structures
- Machine learning-assisted directed protein evolution with combinatorial libraries
- Predicting Protein Binding Affinity With Word Embeddings and Recurrent Neural Networks
- Toward machine-guided design of proteins
- Deep Semantic Protein Representation for Annotation, Discovery, and Engineering
- Detecting Protein-Protein Interactions with a Novel Matrix-Based Protein Sequence Representation and Support Vector Machines
- Modeling the Language of Life - Deep Learning Protein Sequences
- Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences
- UDSMProt: Universal Deep Sequence Models for Protein Classification
- DeepPrime2Sec: Deep Learning for Protein Secondary Structure Prediction from the Primary Sequences
- Machine Learning for Prioritization of Thermostabilizing Mutations for G-protein Coupled Receptors
- Improving protein function prediction with synthetic feature samples created by generative adversarial networks
- Augmenting protein network embeddings with sequence information
- DeepCLIP: Predicting the effect of mutations on protein-RNA binding with Deep Learning
- Accelerating Protein Design Using Autoregressive Generative Models
- De Novo Protein Design for Novel Folds using Guided Conditional Wasserstein Generative Adversarial Networks (gcWGAN)
- Structure-Based Function Prediction using Graph Convolutional Networks
- Expanding functional protein sequence space using generative adversarial networks
- USMPep: Universal Sequence Models for Major Histocompatibility Complex Binding Affinity Prediction
- Attention mechanism-based deep learning pan-specific model for interpretable MHC-I peptide binding prediction
- Improved protein structure prediction using predicted inter-residue orientations
- Machine Learning Predicts New Anti-CRISPR Proteins
- Transformer neural network for protein specific de novo drug generation as machine translation problem
- End-to-end multitask learning, from protein language to protein features without alignments
- Interpreting mutational effects predictions, one substitution at a time
- Improved Descriptors for the Quantitative Structure−Activity Relationship Modeling of Peptides and Proteins
- Mismatch string kernels for discriminative protein classification
- AOPs-SVM: A Sequence-Based Classifier of Antioxidant Proteins Using a Support Vector Machine
- A structural alignment kernel for protein structures
- Predicting and understanding transcription factor interactions based on sequence level determinants of combinatorial control
- Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0
- mCSM: predicting the effects of mutations in proteins using graph-based signatures
- ProFET: Feature engineering captures high-level protein functions
- DeepSite: protein-binding site predictor using 3D-convolutional neural networks
- DeepLoc: prediction of protein subcellular localization using deep learning
- DeepSol: a deep learning framework for sequence-based protein solubility prediction
- High precision protein functional site detection using 3D convolutional neural networks
- Navigating the protein fitness landscape with Gaussian processes
- Deep generative models for T cell receptor protein sequences
- Generative Models for Graph-Based Protein Design
- I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure
- AAindex: amino acid index database, progress report 2008
- A deep learning framework for modeling structural features of RNA-binding protein targets
- Exploring sequence-function space of a poplar glutathione transferase using designed information-rich gene variants
- Sequence Motifs in MADS Transcription Factors Responsible for Specificity and Diversification of Protein- Protein Interaction
- PROTS-RF: A Robust Model for Predicting Mutation-Induced Protein Stability Changes
- High Precision Prediction of Functional Sites in Protein Structures
- Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools
- Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics
- The spectrum kernel: a string kernel for SVM protein classification
- Can Machine Learning Revolutionize Directed Evolution of Selective Enzymes?
- ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network
- Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature
- Application of fourier transform and proteochemometrics principles to protein engineering
- P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
- Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints
- Deciphering protein evolution and fitness landscapes with latent space models
- Protein Secondary Structure Prediction Based on Data Partition and Semi-Random Subspace Method
- Design of metalloproteins and novel protein folds using variational autoencoders
- A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes
- DESTINI: A deep-learning approach to contact-driven protein structure prediction
- DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks
- Machine Learning Methods for Protein Structure Prediction
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