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awesome-structural-bioinformatics

Structural Bioinformatics is awesome. Throw your textbook in the garbage, light the garbage can on fire, and blend the ashes into your cold brew almond milk latte and read this.
https://github.com/twoXes/awesome-structural-bioinformatics

Last synced: 3 days ago
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Protein Folding
- Deep Learning Protein Folding
- - - On The Origin of Proteins
  - source
Protein - Ligand Docking
- Deep Learning Protein Folding
  - AutoDock - suite of automated docking tools designed to predict how small molecules bind to a receptor of known 3D structure
  - AutoDock Vina - significantly improves the average accuracy of the binding mode predictions compared to AutoDock
  - source
  - GOMoDo - GPCR online modeling and docking server
- Scoring Function
  - source
Protein Data Sources
- Scoring Function
  - CATH/Gene3D - 151 Million Protein Domains Classified into 5,481 Superfamilies
  - NCBI Conserved Domains Database - resource for the annotation of functional units in proteins
  - Protein Data Bank
  - Scop 2 - Structural Classification of Proteins
  - UniProt - comprehensive, high-quality and freely accessible resource of protein sequence and functional information.
Fusion Proteins
- Software
  - StarFusion - 019-1842-9)
- Groups
  - The 5th Annual Fusion Protein Therapeutics Conference
  - Fusion Oncoproteins in Childhood Cancers (FusOnC2) Consortium
- Articles and References
Genomics Software
- Articles and References
  - NVIDIA Clara Parabricks Pipelines - perform secondary analysis of next generation sequencing (NGS) DNA and RNA data, blazing fast speeds and low cost. Can analyze whole human genomes in about 45 minutes. Includes Deep Variant.
  - AWS Clara Parabrick Pipeline Setup
Genomics Resources
- Articles and References
  - Genome in a Bottle - develop the technical infrastructure (reference standards, reference methods, and reference data) to enable translation of whole human genome sequencing to clinical practice and innovations in technologies.
  - Online Needleman-Wunsch Example - wunsch/index.html) || [Great NW Colab](https://colab.research.google.com/github/zaneveld/full_spectrum_bioinformatics/blob/master/content/08_phylogenetic_trees/needleman_wunsch_alignment.ipynb)
Genomics Learning Online
- Articles and References
  - Rosalind
  - Learn BioInformatics in the Browser - Sandbox Bio
  - Biological Modeling - Free Online Course
  - BioInformatic Algorithms Lecture Videos
Protein/Small Molecule References
- Articles and References
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design - -> :computer: [code](https://github.com/aspuru-guzik-group/JANUS)
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Using Gans With Adaptive Training Data to search for new molecules
  - Machine learning designs non-hemolytic antimicrobial peptides
  - Few-Shot Graph Learning for Molecular Property Prediction - -> :computer: [code](https://github.com/zhichunguo/Meta-MGNN)
  - Assigning Confidence To Molecular Property Prediction
  - Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition
  - A Turing Test For Molecular Generation
  - Mol-CycleGAN: a generative model for molecular optimization - -> :computer: [code](https://github.com/ardigen/mol-cycle-gan)
  - Protein Contact Map Denoising Using Generative Adversarial Networks - -> :computer: [ContactGAN code](https://github.com/kiharalab/ContactGAN)
  - Hierarchical Generation of Molecular Graphs using Structural Motifs - -> :computer: [code](https://github.com/wengong-jin/hgraph2graph/)
  - Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis
  - Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
  - High-Throughput Docking Using Quantum Mechanical Scoring
  - Deep Learning Methods in Protein Structure Prediction
  - GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction - -> :computer: [code](https://github.com/zhichunguo/GraSeq)
  - Revealing cytotoxic substructures in molecules using deep learning
  - ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
  - From Machine Learning to Deep Learning: Advances in scoring functions for protein-ligand docking
  - The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference, Molecular Biology and Evolution
  - Deep Learning Enables Rapid Identification of Potent DDR1 Kinase Inhibitors - -> :computer: [GENTRL code](https://github.com/insilicomedicine/GENTRL)
  - Junction Tree Variational Autoencoder for Molecular Graph Generation - -> :computer: [Code](https://github.com/wengong-jin/icml18-jtnn)
  - SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery - -> [code](https://github.com/DSPsleeporg/smiles-transformer) --> :computer: [code](https://github.com/DSPsleeporg/smiles-transformer)
  - Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective - -> :computer: [code](https://github.com/awslabs/dgl-lifesci/blob/master/python/dgllife/model/model_zoo/mgcn_predictor.py) --> :computer: [more code](https://github.com/tencent-alchemy/Alchemy)
  - SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction
  - eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates - -> :computer: [eToxPred code](https://github.com/pulimeng/etoxpred)
  - Seq3Seq Fingerprint: Towards End-to-end Semi-supervised Deep Drug Discovery - -> :computer: [code](https://github.com/xysteve00/seq3seq-fingerprint-regression)
  - Chemi-Net: A molecular graph convolutional network for accurate drug property prediction
  - Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility
  - DeepFam: deep learning based alignment-free method for protein family modeling and prediction
  - Derivative-free neural network for optimizing the scoring functions associated with dynamic programming of pairwise-profile alignment
  - (MOSES): A Benchmarking Platform for Molecular Generation Models - -> :computer: [code](https://github.com/molecularsets/moses)
  - DeepSMILES: An adaptation of SMILES for use in machine-learning of chemical structures - -> :computer: [code](https://github.com/baoilleach/deepsmiles)
  - Protein-Ligand Scoring with CNN
  - Quantum-chemical insights from deep tensor neural networks
  - Incorporating QM and solvation into docking for applications to GPCR targets
  - MRFalign: Protein Homology Detection through Alignment of Markov Random Fields
  - Molecular Docking: A powerful approach for structure-based drug discovery
  - Molecular Dynamics Simulations of Protein Dynamics and their relevance to drug discovery
  - Amphipol-Assisted in Vitro Folding of G Protein-Coupled Receptors
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Double-mutant cycles: a powerful tool for analyzing protein structure and function
  - A Geometric Approach to MacroMolecule Ligand Interactions
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Revealing cytotoxic substructures in molecules using deep learning
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference, Molecular Biology and Evolution
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design - -> :computer: [code](https://github.com/aspuru-guzik-group/JANUS)
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Hierarchical Generation of Molecular Graphs using Structural Motifs - -> :computer: [code](https://github.com/wengong-jin/hgraph2graph/)
  - Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design
  - Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
  - Revealing cytotoxic substructures in molecules using deep learning
  - ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
  - SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery - -> [code](https://github.com/DSPsleeporg/smiles-transformer) --> :computer: [code](https://github.com/DSPsleeporg/smiles-transformer)
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - The structural basis for agonist and partial agonist action on a β(1)-adrenergic receptor
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
  - Highly accurate protein structure prediction with AlphaFold
  - Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
  - Transformer neural network for protein-specific de novo drug generation as a machine translation problem
  - Revealing cytotoxic substructures in molecules using deep learning
  - Junction Tree Variational Autoencoder for Molecular Graph Generation - -> :computer: [Code](https://github.com/wengong-jin/icml18-jtnn)
  - Quantum-chemical insights from deep tensor neural networks
  - GPCR Folding and Maturation - Coupled Receptors Handbook](https://link.springer.com/book/10.1007/978-1-59259-919-6)
Genomics References
- Articles and References
Distributed Computing in Informatics
- Articles and References
- Distributed Cloud
  - Google Distributed Cloud
Brief Explanation of AlphaFold Jax Architecture
- Distributed Cloud
  - image
  - image
  - Screenshot from 2021-07-28 07-58-02
  - Screenshot from 2021-07-28 07-58-54
  - nn.logsoftmax
  - `lax.stop_gradient` - is the identity function, that is, it returns argument `x` unchanged. However, ``stop_gradient`` prevents the flow of
  - `tree_map`
  - Automatic Differentiation Lecture Slides
Other Free Books You Should Read Instead of This Repo
- Distributed Cloud
  - Chemisty 2E - :atom: Equivalent to 201 & 202 Level Chemistry Book
  - Chemistry: Atoms First 2E
  - Biology 2E
  - Neural Networks and Deep Learning - About Deep Learning - if you are into that sort of thing.
  - Reinforcement Learning
  - Pattern Recognition and Machine Learning

Programming Languages

Python 1

Ecosyste.ms: Awesome

awesome-structural-bioinformatics

Protein Folding

Deep Learning Protein Folding

Protein - Ligand Docking

Deep Learning Protein Folding

Scoring Function

Protein Data Sources

Scoring Function

Fusion Proteins

Software

Groups

Articles and References

Genomics Software

Articles and References

Genomics Resources

Articles and References

Genomics Learning Online

Articles and References

Protein/Small Molecule References

Articles and References

Genomics References

Articles and References

Distributed Computing in Informatics

Articles and References

Distributed Cloud

Brief Explanation of AlphaFold Jax Architecture

Distributed Cloud

Other Free Books You Should Read Instead of This Repo

Distributed Cloud