Inspired by Kevin Kaichuang Yang's Machine-learning-for-proteins. In terms of the fast development of protein design in DL(some ML models are also included), we started making this dynamic repository as a record of latest papers/projects in this field for the newcomers like us:
- Mini protein, binders, metalloprotein, antibody, peptide & molecule designs are included.
- More de novo protein design paper list at Wangchentong's GitHub repo: paper_for_denovo_protein_design.
- Our notes of these papers are shared in a Zhihu Column (simplified Chinese/English), more suggested notes at RosettAI.
Contributions are welcome!
Heading [2] follows a "generator-predictor-optimizer" paradigm, Heading [3], [4]&[6] follow "Inside-out" paradigm(function-scaffold-sequence) from RosettaCommons, Heading [5]&[7] follow other ML/DL strategies.
- List of papers about Proteins Design using Deep Learning
FLIP: Benchmark tasks in fitness landscape inference for proteins
Christian Dallago, Jody Mou, Kadina E Johnston, Bruce Wittmann, Nick Bhattacharya, Samuel Goldman, Ali Madani, Kevin K Yang
NeurIPS 2021 Datasets and Benchmarks Track/bioRxiv 2021 || website || code || supplementary
A Benchmark Framework for Evaluating Structure-to-Sequence Models for Protein Design
Jeffrey Chan, Seyone Chithrananda, David Brookes, Sam Sinai
Paper unavailable at Machine Learning in Structural Biology Workshop 2022
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB
Zhangyang Gao, Cheng Tan, Stan Z. Li
arxiv (2022)
SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning
Jonathan E. King, David Ryan Koes
arxiv || github::sidechainnet
TDC maintains a resource list that currently contains 22 tasks (and its datasets) related to small molecules and macromolecules, including PPI, DDI and so on. MoleculeNet published a small molecule related benchmark four years ago.
In terms of datasets and benchmarks, protein design is far less mature than drug discovery (paperwithcode drug discovery benchmarks). (Maybe should add the evaluation of protein design for deep learning method (especially deep generative model))
Difficulties and opportunities always coexist. Happy to see the work of Christian Dallago, Jody Mou, Kadina E. Johnston, Bruce J. Wittmann, Nicholas Bhattacharya, Samuel Goldman, Ali Madani, Kevin K. Yang and Zhangyang Gao, Cheng Tan, Stan Z. Li.
Sampling of structure and sequence space of small protein folds
Linsky, T.W., Noble, K., Tobin, A.R. et al.
Nat Commun 13, 7151 (2022) || code || Supplementary
A list of suggested protein databases, more lists at CNCB.
Some similar GitHub lists that include papers about protein design using deep learning:
Deep learning in protein structural modeling and design
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J. Gray
Patterns 1.9 || 2020
100th anniversary of macromolecular science viewpoint: Data-driven protein design
Ferguson, Andrew L., and Rama Ranganathan.
ACS Macro Letters 10.3 (2021)
Protein sequence design with deep generative models
Zachary Wu, Kadina E. Johnston, Frances H. Arnold, Kevin K. Yang
Current Opinion in Chemical Biology 65 || note || 2021
Structure-based protein design with deep learning
Ovchinnikov, Sergey, and Po-Ssu Huang.
Current opinion in chemical biology 65 || note || 2021
Deep learning techniques have significantly impacted protein structure prediction and protein design
Pearce, Robin, and Yang Zhang.
Current opinion in structural biology 68 (2021)
Protein design via deep learning
Wenze Ding, Kenta Nakai, Haipeng Gong
Briefings in Bioinformatics || 25 March 2022
Deep generative modeling for protein design
Strokach, Alexey, and Philip M. Kim.
Current Opinion in Structural Biology || 2022
Deep learning approaches for conformational flexibility and switching properties in protein design
Rudden, Lucas SP, Mahdi Hijazi, and Patrick Barth
Frontiers in Molecular Biosciences
From sequence to function through structure: deep learning for protein design
Noelia Ferruz, Michael Heinzinger, Mehmet Akdel, Alexander Goncearenco, Luca Naef, Christian Dallago
bioRxiv 2022.08.31.505981/Computational and Structural Biotechnology Journal
Volume 21, 2023 || Supplementary || accompanying list
Computational protein design with data-driven approaches: Recent developments and perspectives
Liu, H, Chen, Q.
WIREs Comput Mol Sci. 2022. e1646
A review of deep learning methods for antibodies
Graves, Jordan, et al.
Antibodies 9.2 (2020)
Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies
Akbar, Rahmad, et al.
Mabs. Vol. 14. No. 1. Taylor & Francis, 2022
Advances in computational structure-based antibody design
Hummer, Alissa M., Brennan Abanades, and Charlotte M. Deane.
Current Opinion in Structural Biology 74 (2022)
Deep generative models for peptide design
Wan, Fangping, Daphne Kontogiorgos-Heintz, and Cesar de la Fuente-Nunez
Digital Discovery (2022)
Improving de novo Protein Binder Design with Deep Learning
Nathaniel Bennett, Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne Vafeados, Ying Po Peng, Justas Dauparas, Minkyung Baek, Lance Stewart, Frank DiMaio, Steven De Munck, Savvas Savvides, David Baker
bioRxiv 2022.06.15.495993
Invert trained models with optimize algorithms through iterations for sequence design. Inverted structure prediction models are known as Hallucination.
Design of proteins presenting discontinuous functional sites using deep learning
Doug Tischer, Sidney Lisanza, Jue Wang, Runze Dong, View ORCID ProfileIvan Anishchenko, Lukas F. Milles, Sergey Ovchinnikov, David Baker
bioRxiv (2020)
Fast differentiable DNA and protein sequence optimization for molecular design
Linder, Johannes, and Georg Seelig.
arXiv preprint arXiv:2005.11275 (2020)
De novo protein design by deep network hallucination
Ivan Anishchenko, Samuel J. Pellock, Tamuka M. Chidyausiku, Theresa A. Ramelot, Sergey Ovchinnikov, Jingzhou Hao, Khushboo Bafna, Christoffer Norn, Alex Kang, Asim K. Bera, Frank DiMaio, Lauren Carter, Cameron M. Chow, Gaetano T. Montelione & David Baker
Nature (2021) || code || trRosetta
Protein sequence design by conformational landscape optimization
Norn, Christoffer, et al.
Proceedings of the National Academy of Sciences 118.11 (2021) || code
Solubility-aware protein binding peptide design using AlphaFold
Takatsugu Kosugi, Masahito Ohue
bioRxiv 2022.05.14.491955 || Supplemental Materials
End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman
Petti, Samantha, Bhattacharya, Nicholas, Rao, Roshan, Dauparas, Justas, Thomas, Neil, Zhou, Juannan, Rush, Alexander M, Koo, Peter K, Ovchinnikov, Sergey
bioRxiv (2021)/Bioinformatics, 2022;, btac724 || ColabDesign, SMURF, AF2 back propagation || our notes1, notes2 || lecture || Discord
AlphaDesign: A de novo protein design framework based on AlphaFold
Jendrusch, Michael, Jan O. Korbel, and S. Kashif Sadiq.
bioRxiv (2021)
Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design
Moffat, Lewis, Joe G. Greener, and David T. Jones.
bioRxiv (2021)
State-of-the-art estimation of protein model accuracy using AlphaFold
James P. Roney, Sergey Ovchinnikov
bioRxiv 2022.03.11.484043 || code
Hallucinating protein assemblies
Basile I M Wicky, Lukas F Milles, Alexis Courbet, Robert J Ragotte, Justas Dauparas, Elias Kinfu, Sam Tipps, Ryan D Kibler, Minkyung Baek, Frank DiMaio, Xinting Li, Lauren Carter, Alex Kang, Hannah Nguyen, Asim K Bera, David Baker
bioRxiv 2022.06.09.493773/Science (2022) || related slides || our notes || news
EvoBind: in silico directed evolution of peptide binders with AlphaFold
Patrick Bryant, Arne Elofsson
bioRxiv 2022.07.23.501214 || code
Hallucination of closed repeat proteins containing central pockets
Linna An, Derrick R Hicks, Dmitri Zorine, Justas Dauparas, Basile I. M. Wicky, Lukas F Milles, Alexis Courbet, Asim K. Bera, Hannah Nguyen, Alex Kang, Lauren Carter, David Baker
bioRxiv 2022.09.01.506251
Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search
Bryant, Patrick, et al.
Nature communications 13.1 (2022) || gitlba, github || Supplementary data1, Supplementary data2
De novo protein design by inversion of the AlphaFold structure prediction network
Casper Goverde, Benedict Wolf, Hamed Khakzad, Stephane Rosset, Bruno E Correia
bioRxiv 2022.12.13.520346 || code
Design in the DARK: Learning Deep Generative Models for De Novo Protein Design
Moffat, Lewis, Shaun M. Kandathil, and David T. Jones.
bioRxiv (2022) || DMPfold2
AutoFoldFinder: An Automated Adaptive Optimization Toolkit for De Novo Protein Fold Design
Shuhao Zhang, Youjun Xu, Jianfeng Pei, Luhua Lai
NeurIPS 2021
Protein language models trained on multiple sequence alignments learn phylogenetic relationships
Damiano Sgarbossa, Umberto Lupo, Anne-Florence Bitbol
arXiv preprint arXiv:2203.15465 (2022)/bioRxiv 2022.04.14.488405
EvoOpt: an MSA-guided, fully unsupervised sequence optimization pipeline for protein design
Hideki Yamaguchi, Yutaka Saito
NeurIPS 2022
Towards deep learning models for target-specific antibody design
Mahajan, Sai Pooja, et al.
Biophysical Journal 121.3 (2022) || DeepAb || lecture
Hallucinating structure-conditioned antibody libraries for target-specific binders
Sai Pooja Mahajan, Jeffrey A Ruffolo, Rahel Frick, Jeffrey J. Gray
bioRxiv 2022.06.06.494991/Front. Immunol. 13:999034 || Supplementary || code
News of TRDesign
TIANRANG XLab
paper unavailable || slides || website || commercial
Multi-segment preserving sampling for deep manifold sampler
Berenberg, Daniel, et al.
arXiv preprint arXiv:2205.04259 (2022)
A high-level programming language for generative protein design
Brian Hie, Salvatore Candido, Zeming Lin, Ori Kabeli, Roshan Rao, Nikita Smetanin, Tom Sercu, Alexander Rives
bioRxiv 2022.12.21.521526
Language models generalize beyond natural proteins
Robert Verkuil, Ori Kabeli, Yilun Du, Basile IM Wicky, Lukas F Milles, Justas Dauparas, David Baker, Sergey Ovchinnikov, Tom Sercu, Alexander Rives
bioRxiv 2022.12.21.521521
AdaLead: A simple and robust adaptive greedy search algorithm for sequence design
Sinai, Sam, et al.
arXiv preprint arXiv:2010.02141 (2020) || code
Autofocused oracles for model-based design
Fannjiang, Clara, and Jennifer Listgarten.
Advances in Neural Information Processing Systems 33 (2020)
An Efficient MCMC Approach to Energy Function Optimization in Protein Structure Prediction
Lakshmi A. Ghantasala, Risi Jaiswal, Supriyo Datta
arXiv:2211.03193
Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMC
Patrick Emami, Aidan Perreault, Jeffrey Law, David Biagioni, Peter St. Joh
NeurIPS 2022/arXiv:2212.09925
These models design backbone/scaffold/template in Cartesian coordinates, contact maps, distance maps and φ & ψ angles.
Generative modeling for protein structures
Anand, Namrata, and Possu Huang.
NeurIPS 2018
Fully differentiable full-atom protein backbone generation
Anand Namrata, Raphael Eguchi, and Po-Ssu Huang.
OpenReview ICLR 2019 workshop DeepGenStruct || without code
RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network
Sabban, Sari, and Mikhail Markovsky.
F1000Research 9 (2020) || code || pyRosetta || tensorflow || maximizaing the fluorescence of a protein
Conditioning by adaptive sampling for robust design
Brookes, David, Hahnbeom Park, and Jennifer Listgarten.
International conference on machine learning. PMLR, 2019 || without code
IG-VAE: generative modeling of immunoglobulin proteins by direct 3D coordinate generation
Raphael R. Eguchi, Christian A. Choe, Po-Ssu Huang
Biorxiv (2020) || without code ||
Generating tertiary protein structures via an interpretative variational autoencoder
Guo, Xiaojie, et al
arXiv preprint arXiv:2004.07119 (2020) || code not available
Deep sharpening of topological features for de novo protein design
Harteveld, Zander, et al.
ICLR2022 Machine Learning for Drug Discovery. 2022 || code not available
End-to-End deep structure generative model for protein design
Boqiao Lai, matthew McPartlon, Jinbo Xu
bioRxiv 2022.07.09.499440
Deep Generative Design of Epitope-Specific Binding Proteins by Latent Conformation Optimization
Raphael R Eguchi, Christian A Choe, Udit Parekh, Irene S Khalek, Michael D Ward, Neha Vithani, Gregory R Bowman, Joseph G Jardine, Possu Huang
bioRxiv 2022.12.22.521698
Function-guided protein design by deep manifold sampling
Vladimir Gligorijevic, Stephen Ra, Daniel Berenberg, Richard Bonneau, Kyunghyun Cho
NeurIPS 2021 || without code
A backbone-centred energy function of neural networks for protein design
Huang, B., Xu, Y., Hu, X. et al
Nature (2022) || code
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
Brian L. Trippe, Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, Tommi Jaakkola
arXiv:2206.04119/NeurIPS 2022 || poster
Protein structure generation via folding diffusion
Kevin E. Wu, Kevin K. Yang, Rianne van den Berg, James Y. Zou, Alex X. Lu, Ava P. Amini
arXiv:2209.15611 || code
ProteinSGM: Score-based generative modeling for de novo protein design
Jin Sub Lee, Philip M Kim
bioRxiv 2022.07.13.499967
Top-down design of protein nanomaterials with reinforcement learning 。
Isaac D Lutz, Shunzhi Wang, Christoffer Norn, Andrew J Borst, Yan Ting Zhao, Annie Dosey, Longxing Cao, Zhe Li, Minkyung Baek, Neil P King, Hannele Ruohola-Baker, David Baker
bioRxiv 2022.09.25.509419
Identify amino sequence from given backbone/scaffold/template constrains: torsion angles(φ & ψ), backbone angles(θ and τ), backbone dihedrals (φ, ψ & ω), backbone atoms (Cα, N, C, & O), Cα − Cα distance, unit direction vectors of Cα−Cα, Cα−N & Cα−C, etc(aka. inverse folding). Referred from here. Energy-based models are also inculded for task of rotamer conformation(χ angles or atom coordinates) recovery.
3D representations of amino acids—applications to protein sequence comparison and classification
Li, Jie, and Patrice Koehl.
Computational and structural biotechnology journal 11.18 (2014) || 2014
Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment‐based local and energy‐based nonlocal profiles
Li, Zhixiu, et al.
Proteins: Structure, Function, and Bioinformatics 82.10 (2014) || code unavailable
SPIN2: Predicting sequence profiles from protein structures using deep neural networks
O'Connell, James, et al.
Proteins: Structure, Function, and Bioinformatics 86.6 (2018) || code unavailable
Computational protein design with deep learning neural networks
Wang, Jingxue, et al.
Scientific reports 8.1 (2018) || code unavailable
Ligand-aware protein sequence design using protein self contacts
Jody Mou, Benjamin Fry, Chun-Chen Yao, Nicholas Polizzi
NeurIPS 2022
Design of metalloproteins and novel protein folds using variational autoencoders
Greener, Joe G., Lewis Moffat, and David T. Jones.
Scientific reports 8.1 (2018)
To improve protein sequence profile prediction through image captioning on pairwise residue distance map
Chen, Sheng, et al.
Journal of chemical information and modeling 60.1 (2019) || SPROF
Deep learning of Protein Sequence Design of Protein-protein Interactions
Syrlybaeva, Raulia, and Eva-Maria Strauch.
bioRxiv (2022)/Bioinformatics, 2022;, btac733 || Supplementary || code
A structure-based deep learning framework for protein engineering
Shroff, Raghav, et al.
bioRxiv (2019)
ProDCoNN: Protein design using a convolutional neural network
Zhang, Yuan, et al.
Proteins: Structure, Function, and Bioinformatics 88.7 (2020) || code unavailable
Protein sequence design with a learned potential
Namrata Anand, Raphael Eguchi, Irimpan I. Mathews, Carla P. Perez, Alexander Derry, Russ B. Altman & Po-Ssu Huang
Nacture Communications (2022) || code
Protein Sequence Design with Deep Learning and Tooling like Monte Carlo Sampling and Analysis
Leonardo Castorina
paper not available || code
Learning from protein structure with geometric vector perceptrons
Jing, Bowen, et al.
arXiv preprint arXiv:2009.01411 (2020) || GVP
Fast and flexible protein design using deep graph neural networks
Alexey Strokach, David Becerra, Carles Corbi-Verge, Albert Perez-Riba, Philip M. Kim
Cell Systems (2020) || code::ProteinSolver
Mimetic Neural Networks: A unified framework for Protein Design and Folding
Moshe Eliasof, Tue Boesen, Eldad Haber, Chen Keasar, Eran Treister
arXiv:2102.03881/Front. Bioinform. 2:715006
TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs
Li, Alex J., et al.
NeurIPS 2021 / arXiv (2022)
A neural network model for prediction of amino-acid probability from a protein backbone structure
Koya Sakuma, Naoya Kobayashi
Unpublished yet (June 2021)|| GCNdesgin
XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers
Maguire, Jack B., et al.
PLoS computational biology 17.9 (2021)
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB
Gao, Zhangyang, Cheng Tan, and Stan Li.
arXiv preprint arXiv:2202.01079 (2022) || code
Generative De Novo Protein Design with Global Context
Cheng Tan, Zhangyao Gao, Jun Xia and Stan Z. Li
arXiv || Apr 2022 || code
Masked inverse folding with sequence transfer for protein representation learning
Kevin K Yang, Hugh Yeh, Niccolò Zanichelli
bioRxiv 2022.05.25.493516 || code || model
Robust deep learning based protein sequence design using ProteinMPNN
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Alexis Courbet, Robbert J. de Haas, Neville Bethel, Philip J. Y. Leung, Timothy F. Huddy, Sam Pellock, Doug Tischer, Frederick Chan, Brian Koepnick, Hannah Nguyen, Alex Kang, Banumathi Sankaran, Asim Bera, Neil P. King, David Baker
bioRxiv 2022.06.03.494563/Science (2022) || code || hugging face || lecture || colab(in_jax) || ProteinMPNN+ESMFold
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement
Jin, Wengong, Regina Barzilay, and Tommi Jaakkola.
arXiv preprint arXiv:2207.06616 (2022)/International Conference on Machine Learning. PMLR, 2022 || code || poster
Neural Network-Derived Potts Models for Structure-Based Protein Design using Backbone Atomic Coordinates and Tertiary Motifs
Alex J. Li, Mindren Lu, Israel Desta, Vikram Sundar, Gevorg Grigoryan, and Amy E. Keating
bioRxiv 2022.08.02.501736
Conditional Antibody Design as 3D Equivariant Graph Translation
Xiangzhe Kong, Wenbing Huang, Yang Liu
arXiv:2208.06073
SE(3) Equivalent Graph Attention Network as an Energy-Based Model for Protein Side Chain Conformation
Deqin Liu, Sheng Chen, Shuangjia Zheng, Sen Zhang, Yuedong Yang
bioRxiv 2022.09.05.506704 || code
PiFold: Toward effective and efficient protein inverse folding
Zhangyang Gao, Cheng Tan, Stan Z. Li
arXiv:2209.12643v2 || github
De novo protein design for novel folds using guided conditional Wasserstein generative adversarial networks
Mostafa Karimi, Shaowen Zhu, Yue Cao, Yang Shen
Journal of chemical information and modeling 60.12 (2020) || gcWGAN
HelixGAN: A bidirectional Generative Adversarial Network with search in latent space for generation under constraints
Xuezhi Xie, Philip M. Kim
Machine Learning for Structural Biology Workshop, NeurIPS 2021 || without code
Generative models for graph-based protein design
John Ingraham, Vikas K Garg, Dr.Regina Barzilay, Tommi Jaakkola
NeurIPS 2019 || GraphTrans
Fold2Seq: A Joint Sequence (1D)-Fold (3D) Embedding-based Generative Model for Protein Design
Cao, Yue, et al.
International Conference on Machine Learning. PMLR, 2021
Rotamer-Free Protein Sequence Design Based on Deep Learning and Self-Consistency
Liu, Yufeng, et al.
Nature portfolio (2022)/Nature computational science(2022) || Supplementary || Comment || code
A Deep SE(3)-Equivariant Model for Learning Inverse Protein Folding
Mmatthew McPartlon, Ben Lai, Jinbo Xu
bioRxiv (2022)
Learning inverse folding from millions of predicted structures
Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives
bioRxiv (2022) || esm
Accurate and efficient protein sequence design through learning concise local environment of residues
Huang, Bin, et al.
bioRxiv (2022) || Supplementary
PeTriBERT : Augmenting BERT with tridimensional encoding for inverse protein folding and design
Baldwin Dumortier, Antoine Liutkus, Clément Carré, Gabriel Krouk
bioRxiv 2022.08.10.503344
Evolutionary-scale prediction of atomic level protein structure with a language model
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives
bioRxiv 2022.07.20.500902 || blog || github
DenseCPD: improving the accuracy of neural-network-based computational protein sequence design with DenseNet
Qi, Yifei, and John ZH Zhang.
Journal of chemical information and modeling 60.3 (2020) || code unavailable
Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, Jianzhu Ma
bioRxiv 2022.07.10.499510
De novo protein backbone generation based on diffusion with structured priors and adversarial training
Yufeng Liu, Linghui Chen, Haiyan Liu
bioRxiv 2022.12.17.520847
These models generate sequences from expected function.
Antibody complementarity determining region design using high-capacity machine learning
Liu, Ge, et al.
Bioinformatics 36.7 (2020): 2126-2133 || code
Protein design and variant prediction using autoregressive generative models
Shin, Jung-Eun, et al.
Nature communications 12.1 (2021) || code::SeqDesign || mutation effect prediction || sequence generation || April 2021
Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning
Mason, Derek M., et al.
Nature Biomedical Engineering 5.6 (2021) || code
Variational auto-encoding of protein sequences
Sinai, Sam, et al.
arXiv preprint arXiv:1712.03346 (2017)
Design by adaptive sampling
Brookes, David H., and Jennifer Listgarten.
arXiv preprint arXiv:1810.03714 (2018)
Pepcvae: Semi-supervised targeted design of antimicrobial peptide sequences
Das, Payel, et al.
arXiv preprint arXiv:1810.07743 (2018)
Deep generative models for T cell receptor protein sequences
Davidsen, Kristian, et al.
Elife 8 (2019)
How to hallucinate functional proteins
Costello, Zak, and Hector Garcia Martin.
arXiv preprint arXiv:1903.00458 (2019)
Convergent selection in antibody repertoires is revealed by deep learning
Friedensohn, Simon, et al.
BioRxiv (2020) || Supplementary || code available after publication
Variational autoencoder for generation of antimicrobial peptides
Dean, Scott N., and Scott A. Walper.
ACS omega 5.33 (2020)
Generating functional protein variants with variational autoencoders
Hawkins-Hooker, Alex, et al.
PLoS computational biology 17.2 (2021)
Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations
Das, Payel, et al.
Nature Biomedical Engineering 5.6 (2021)
Deep generative models create new and diverse protein structures
Zeming, Tom, Yann and Alexander.
NeurIPS 2021
PepVAE: variational autoencoder framework for antimicrobial peptide generation and activity prediction
Dean, Scott N., et al.
Frontiers in microbiology 12 (2021) || code || Supplementary
HydrAMP: a deep generative model for antimicrobial peptide discovery
Szymczak, Paulina, et al.
bioRxiv (2022) || code
Therapeutic enzyme engineering using a generative neural network
Giessel, Andrew, et al.
Scientific Reports 12.1 (2022)
GM-Pep: A High Efficiency Strategy to De Novo Design Functional Peptide Sequences
Chen, Qushuo, et al.
Journal of Chemical Information and Modeling (2022) || code
Mean Dimension of Generative Models for Protein Sequences
Christoph Feinauer, Emanuele Borgonovo
bioRxiv 2022.12.12.520028 || code
Feedback GAN for DNA optimizes protein functions
Gupta, Anvita, and James Zou.
Nature Machine Intelligence 1.2 (2019) || code
Generating protein sequences from antibiotic resistance genes data using Generative Adversarial Networks
Chhibbar, Prabal, and Arpit Joshi.
arXiv preprint arXiv:1904.13240 (2019)
ProGAN: Protein solubility generative adversarial nets for data augmentation in DNN framework
Han, Xi, et al.
Computers & Chemical Engineering 131 (2019)
GANDALF: Peptide Generation for Drug Design using Sequential and Structural Generative Adversarial Networks
Rossetto, Allison, and Wenjin Zhou.
Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 2020
Generating ampicillin-level antimicrobial peptides with activity-aware generative adversarial networks
Tucs, Andrejs, et al.
ACS omega 5.36 (2020)
Designing feature-controlled humanoid antibody discovery libraries using generative adversarial networks
Amimeur, Tileli, et al.
BioRxiv (2020)
Generating ampicillin-level antimicrobial peptides with activity-aware generative adversarial networks
Tucs, Andrejs, et al.
ACS omega 5.36 (2020) || code
Conditional Generative Modeling for De Novo Protein Design with Hierarchical Functions
Kucera, Tim, Matteo Togninalli, and Laetitia Meng-Papaxanthos
bioRxiv (2021)/Bioinformatics 38.13 (2022) || code
Expanding functional protein sequence spaces using generative adversarial networks
Repecka, Donatas, et al.
Nature Machine Intelligence 3.4 (2021) || code
A Generative Approach toward Precision Antimicrobial Peptide Design.
Ferrell, Jonathon B., et al.
BioRxiv (2021)
AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides
Van Oort, Colin M., et al.
Journal of chemical information and modeling 61.5 (2021)
DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity
Li, Guangyuan, et al.
Briefings in bioinformatics 22.6 (2021) || code || web
PandoraGAN: Generating antiviral peptides using Generative Adversarial Network
Surana, Shraddha, et al.
bioRxiv (2021)
Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides
Hasegawa, Kano, et al.
Journal of Bioinformatics and Computational Biology (2022) || code
Including maked language models and autoregressive language models.
Progen: Language modeling for protein generation
Madani, Ali, et al.
arXiv preprint arXiv:2004.03497 (2020) || code
Signal peptides generated by attention-based neural networks
Wu, Zachary, et al.
ACS Synthetic Biology 9.8 (2020)
Generative Language Modeling for Antibody Design
Shuai, Richard W., Jeffrey A. Ruffolo, and Jeffrey J. Gray.
bioRxiv (2021)
Deep neural language modeling enables functional protein generation across families
Madani, Ali, et al.
bioRxiv (2021)
ProtTrans: towards cracking the language of Life's code through self-supervised deep learning and high performance computing
Elnaggar, Ahmed, et al.
arXiv preprint arXiv:2007.06225 (2020)
Protein sequence sampling and prediction from structural data
Gabriel A. Orellana, Javier Caceres-Delpiano, Roberto Ibañez, Michael P. Dunne, Leonardo Alvarez
bioRxiv 2021.09.06.459171
Transformer-based protein generation with regularized latent space optimization
Castro, E., Godavarthi, A., Rubinfien, J. et al.
Nat Mach Intell (2022)/arXiv:2201.09948 || code
BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning
Prihoda, David, et al.
mAbs. Vol. 14. No. 1. Taylor & Francis, 2022
Guided Generative Protein Design using Regularized Transformers
Castro, Egbert, et al.
arXiv preprint arXiv:2201.09948 (2022)
Towards Controllable Protein design with Conditional Transformers
Ferruz Noelia, and Birte Höcker.
arXiv preprint arXiv:2201.07338 (2022)/Nature Machine Intelligence (2022) || review of Heading 5.4
ProtGPT2 is a deep unsupervised language model for protein design
Noelia Ferruz, View ProfileSteffen Schmidt, View ProfileBirte Höcker
bioRxiv/Nature Communications || model::huggingface datasets::hugingface || lecture || research highlights
Few Shot Protein Generation
Ram, Soumya, and Tristan Bepler.
arXiv preprint arXiv:2204.01168 (2022)
RITA: a Study on Scaling Up Generative Protein Sequence Models
Hesslow, Daniel, et al.
arXiv preprint arXiv:2205.05789 (2022) || code
ProGen2: Exploring the Boundaries of Protein Language Models
Erik Nijkamp, Jeffrey Ruffolo, Eli N. Weinstein, Nikhil Naik, Ali Madani
arXiv:2206.13517 || code
AbBERT: Learning Antibody Humanness via Masked Language Modeling
Denis Vashchenko, Sam Nguyen, Andre Goncalves, Felipe Leno da Silva, Brenden Petersen, Thomas Desautels, Daniel Faissol
bioRxiv 2022.08.02.502236
Accelerating Antibody Design with Active Learning
Seung-woo Seo, Min Woo Kwak, Eunji Kang, Chaeun Kim, Eunyoung Park, Tae Hyun Kang, Jinhan Kim
bioRxiv 2022.09.12.507690
Reprogramming Large Pretrained Language Models for Antibody Sequence Infilling
Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das
ICLR 2023/arXiv:2210.07144
Machine Learning Optimization of Candidate Antibodies Yields Highly Diverse Sub-nanomolar Affinity Antibody Libraries
Lin Li, Esther Gupta, John Spaeth, Leslie Shing, Rafael Jaimes, Rajmonda Sulo Caceres, Tristan Bepler, Matthew E. Walsh
bioRxiv 2022.10.07.502662 || Supplementary || code will be available
ZymCTRL: a conditional language model for the contollable generation of artificial enzymes
Noelia Ferruz
NeurIPS 2022 || hugging face || poster
Accelerating protein design using autoregressive generative models
Riesselman, Adam, et al.
BioRxiv (2019)
Discovering de novo peptide substrates for enzymes using machine learning
Tallorin, Lorillee, et al.
Nature communications 9.1 (2018) || code
Now What Sequence? Pre-trained Ensembles for Bayesian Optimization of Protein Sequences
Ziyue Yang, Katarina A Milas, Andrew D White
bioRxiv 2022.08.05.502972 || code || Supplementary || Colab
Lattice protein design using Bayesian learning
Takahashi, Tomoei, George Chikenji, and Kei Tokita.
arXiv:2003.06601/Physical Review E 104.1 (2021): 014404
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation
Khan, Asif, et al.
arXiv preprint (2022)
Statistical Mechanics of Protein Design
Takahashi, Tomoei, George Chikenji, and Kei Tokita.
arXiv preprint arXiv:2205.03696 (2022)
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design
Ji Won Park, Samuel Stanton, Saeed Saremi, Andrew Watkins, Henri Dwyer, Vladimir Gligorijevic, Richard Bonneau, Stephen Ra, Kyunghyun Cho
arXiv:2210.04096
Model-based reinforcement learning for biological sequence design
Angermueller, Christof, et al.
International conference on learning representations. 2019
Structured Q-learning For Antibody Design
Cowen-Rivers, Alexander I., et al.
arXiv preprint arXiv:2209.04698 (2022)
Protein Sequence Design in a Latent Space via Model-based Reinforcement Learning
Minji Lee, Luiz Felipe Vecchietti, Hyunkyu Jung, Hyunjoo Ro, Ho Min Kim, Meeyoung Cha
ICLR 2023/NeurIPS 2022 || Supplementary
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization
Leo Feng, Padideh Nouri, Aneri Muni, Yoshua Bengio, Pierre-Luc Bacon
arXiv:2209.06259/NeurIPS 2022 || poster
Biological Sequence Design with GFlowNets
Jain, Moksh, et al.
arXiv preprint arXiv:2203.04115 (2022) || lecture
Deep learning to design nuclear-targeting abiotic miniproteins
Schissel, Carly K., et al.
Nature Chemistry 13.10 (2021) || code
Recurrent neural network model for constructive peptide design
Müller, Alex T., Jan A. Hiss, and Gisbert Schneider.
Journal of chemical information and modeling 58.2 (2018)
Machine learning designs non-hemolytic antimicrobial peptides
Capecchi, Alice, et al.
Chemical Science 12.26 (2021)
Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides
Tran, Duy Phuoc, et al.
Scientific reports 11.1 (2021)
Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria
Nagarajan, Deepesh, et al
Journal of Biological Chemistry 293.10 (2018)
Deep learning enables the design of functional de novo antimicrobial proteins
Caceres-Delpiano, Javier, et al.
bioRxiv (2020)
ECNet is an evolutionary context-integrated deep learning framework for protein engineering
Luo, Yunan, et al.
Nature communications 12.1 (2021)
Deep learning for novel antimicrobial peptide design
Wang, Christina, Sam Garlick, and Mire Zloh.
Biomolecules 11.3 (2021)
Antibody design using LSTM based deep generative model from phage display library for affinity maturation
Saka, Koichiro, et al.
Scientific reports 11.1 (2021)
Deep learning to design nuclear-targeting abiotic miniproteins
Schissel, Carly K., et al.
Nature Chemistry 13.10 (2021)
In silico proof of principle of machine learning-based antibody design at unconstrained scale
Akbar, Rahmad, et al.
Mabs. Vol. 14. No. 1. Taylor & Francis, 2022 || code
Large-scale design and refinement of stable proteins using sequence-only models
Singer, Jedediah M., et al.
PloS one 17.3 (2022) || code
Deep-learning based bioactive therapeutic peptides generation and screening
Haiping Zhang, Konda Mani Saravanan, Yanjie Wei, Yang Jiao, Yang Yang, Yi Pan, Xuli Wu, John Z.H. Zhang
bioRxiv 2022.11.14.516530 || code || Supplementary
Efficient generative modeling of protein sequences using simple autoregressive models
Trinquier, Jeanne, et al.
Nature communications 12.1 (2021): 1-11 || code
Conformal prediction for the design problem
Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, Michael I. Jordan
arXiv:2202.03613v4 || code
How pairwise coevolutionary models capture the collective residue variability in proteins?
Figliuzzi, Matteo, Pierre Barrat-Charlaix, and Martin Weigt.
Molecular biology and evolution 35.4 (2018): 1018-1027 || code
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
Nataša Tagasovska, Nathan C. Frey, Andreas Loukas, Isidro Hötzel, Julien Lafrance-Vanasse, Ryan Lewis Kelly, Yan Wu, Arvind Rajpal, Richard Bonneau, Kyunghyun Cho, Stephen Ra, Vladimir Gligorijević
arXiv:2210.10838 || slides
denoising-diffusion-protein-sequence
Zhangzhi Peng
Paper unavailable || github
These models generate structures(including side chains) from expected function or recover a part of structures(aka. inpainting)
One-sided design of protein-protein interaction motifs using deep learning
Syrlybaeva, Raulia, and Eva-Maria Strauch.
bioRxiv (2022) || code || our notes || lecture
Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models
Namrata Anand, Tudor Achim
GitHub (2022)/arXiv (2022) || our notes || lecture
Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, Jianzhu Ma
bioRxiv 2022.07.10.499510 || code || hugging face
Illuminating protein space with a programmable generative model
John Ingraham, Max Baranov, Zak Costello, Vincent Frappier, Ahmed Ismail, Shan Tie, Wujie Wang, Vincent Xue, Fritz Obermeyer, Andrew Beam, Gevorg Grigoryan
Generate Biomedicines Preprint || plausible code || website
Deep learning methods for designing proteins scaffolding functional sites
Wang J, Lisanza S, Juergens D, Tischer D, Anishchenko I, Baek M, Watson JL, Chun JH, Milles LF, Dauparas J, Expòsit M, Yang W, Saragovi A, Ovchinnikov S, Baker D
bioRxiv(2021)/Science(2022) || RFDesign || our notes || lecture || RoseTTAFold || Supplementary, Other Supplementary
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Joseph L. et al.
Bakerlab Preprint/bioRxiv 2022.12.09.519842 || news || Supplementary
De novo design of high-affinity protein binders to bioactive helical peptides
Susana Vázquez Torres, Philip J. Y. Leung, Isaac D. Lutz, Preetham Venkatesh, Joseph L Watson, Fabian Hink, Huu-Hien Huynh, Andy Hsien-Wei Yeh, David Juergens, Nathaniel R. Bennett, Andrew N. Hoofnagle, Eric Huang, Michael J. MacCoss, Marc Expòsit, Gyu Rie Lee, Elif Nihal Korkmaz, Jeff Nivala, Lance Stewart, Joseph M. Rodgers, David Baker
bioRxiv 2022.12.10.519862 || Supplementary
De Novo Design of Site-specific Protein Binders Using Surface Fingerprints
Wehrle, Sarah, et al.
Protein Science 30.CONF (2021)/bioRxiv (2022) || Supplementary || masif_seed || masif
Iterative refinement graph neural network for antibody sequence-structure co-design
Jin, Wengong, et al.
arXiv preprint arXiv:2110.04624 (2021) || RefineGNN || lecture1, lecture2
Antibody Complementarity Determining Regions (CDRs) design using Constrained Energy Model
Fu, Tianfan, and Jimeng Sun.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022 || code
Conditional Antibody Design as 3D Equivariant Graph Translation
Xiangzhe Kong, Wenbing Huang, Yang Liu
ICLR 2023/arXiv:2208.06073
Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design
Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Tianbo Peng, Yingce Xia, Liang He, Shufang Xie, Tao Qin, Haiguang Liu, Kun He, Tie-Yan Liu
arXiv:2211.08406
Protein Sequence and Structure Co-Design with Equivariant Translation
Chence Shi, Chuanrui Wang, Jiarui Lu, Bozitao Zhong, Jian Tang
arXiv:2210.08761
Deep generative models of genetic variation capture the effects of mutations
Adam J. Riesselman, John B. Ingraham & Debora S. Marks
Nature Methods || code::DeepSequence || Oct 2018
Deciphering protein evolution and fitness landscapes with latent space models
Xinqiang Ding, Zhengting Zou & Charles L. Brooks III
Nature Communications || code::PEVAE || Dec 2019
Is transfer learning necessary for protein landscape prediction?
Shanehsazzadeh, Amir, David Belanger, and David Dohan.
arXiv preprint arXiv:2011.03443 (2020)
Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions
Amirali Aghazadeh, Hunter Nisonoff, Orhan Ocal, David H. Brookes, Yijie Huang, O. Ozan Koyluoglu, Jennifer Listgarten & Kannan Ramchandran
Nature Communications || code || Sep 2021
The generative capacity of probabilistic protein sequence models
Francisco McGee, Sandro Hauri, Quentin Novinger, Slobodan Vucetic, Ronald M. Levy, Vincenzo Carnevale & Allan Haldane
Nature Communications || code::generation_capacity_metrics || code::sVAE || Nov 2021
Learning the local landscape of protein structures with convolutional neural networks
Kulikova, Anastasiya V., et al
Journal of Biological Physics 47.4 (2021)
Proximal Exploration for Model-guided Protein Sequence Design
Zhizhou Ren, Jiahan Li, Fan Ding, Yuan Zhou, Jianzhu Ma, Jian Peng
BioRxiv (2022)
Efficient evolution of human antibodies from general protein language models and sequence information alone
Hie, Brian L., et al.
bioRxiv (2022) || code
Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval
Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A., Marks, D.S., Gal, Y.
ICML (2022)/arXiv:2205.13760 || code || hugging face
Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments
Ruyun Hu, Lihao Fu, Yongcan Chen, Junyu Chen, Yu Qiao, Tong Si
bioRxiv 2022.08.11.503535
Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness
Bachas, Sharrol, et al.
bioRxiv 2022.08.16.504181 || poster
Construction of a Deep Neural Network Energy Function for Protein Physics
Yang, Huan, Zhaoping Xiong, and Francesco Zonta
Journal of Chemical Theory and Computation (2022)
Inferring protein fitness landscapes from laboratory evolution experiments
Sameer D’Costa, Emily C. Hinds, Chase R. Freschlin, Hyebin Song, Philip A. Romero
bioRxiv 2022.09.01.506224 || Supplementary
BayeStab: Predicting Effects of Mutations on Protein Stability with Uncertainty Quantification
Wang, Shuyu, et al.
Protein Science (2022) || code || website
Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design
Neil Thomas, Atish Agarwala, David Belanger, Yun S. Song, Lucy Colwell
bioRxiv 2022.10.28.514293 || code
Accurate Mutation Effect Prediction using RoseTTAFold
Sanaa Mansoor, Minkyung Baek, David Juergens, Joseph L Watson, David Baker
bioRxiv 2022.11.04.515218
Learning the shape of protein micro-environments with a holographic convolutional neural network
Pun, Michael N., et al.
bioRxiv (2022) || code
Unified rational protein engineering with sequence-based deep representation learning
Alley, Ethan C., et al.
Nature methods 16.12 (2019)
Protein Structure Representation Learning by Geometric Pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, Jian Tang
arXiv || Jan 2022
Evolutionary velocity with protein language models
Brian L. Hie, Kevin K. Yang, and Peter S. Kim
bioRxiv
Advancing protein language models with linguistics: a roadmap for improved interpretability
Mai Ha Vu, Rahmad Akbar, Philippe A. Robert, Bartlomiej Swiatczak, Victor Greiff, Geir Kjetil Sandve, Dag Trygve Truslew Haug
arXiv:2207.00982
Deciphering the language of antibodies using self-supervised learning
Leem, Jinwoo, et al.
Patterns (2022): 100513 || code
On Pre-training Language Model for Antibody
Anonymous(Paper under double-blind review)
ICLR 2023 || Supplementary
Antibody Representation Learning for Drug Discovery
Lin Li, Esther Gupta, John Spaeth, Leslie Shing, Tristan Bepler, Rajmonda Sulo Caceres
arXiv:2210.02881
Unlike function-scaffold-sequence paradigm in protein design, major molecular design models based on paradigm form DL from 3 kinds of level: atom-based, fragment-based, reaction-based, and they can be categorized as Gradient optimization or Optimized sampling(gradient-free). Click here for detail review
In consideration of learning more various of generative models for design, these recommended latest models from Molecular Design might be helpful and even be able to be transplanted to protein design. More paper list at CondaPereira's GitHub repo: Essay_For_Molecular_Generation.
Inverse design of 3d molecular structures with conditional generative neural networks
Gebauer, Niklas WA, et al.
arXiv preprint arXiv:2109.04824 (2021) || code || Sept 21
Differentiable scaffolding tree for molecular optimization
Fu, T., Gao, W., Xiao, C., Yasonik, J., Coley, C. W., & Sun, J.
arXiv preprint arXiv:2109.10469 || code || Sept 21
LIMO: Latent Inceptionism for Targeted Molecule Generation
Eckmann, Peter, et al.
arXiv preprint arXiv:2206.09010 (2022) || code
Improving de novo molecular design with curriculum learning
Guo, Jeff, et al.
Nature Machine Intelligence (2022) || code
Equivariant Energy-Guided SDE for Inverse Molecular Design
Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu
arXiv:2209.15408
Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design
Keir Adams, Connor W. Coley
arXiv:2210.04893 || code
Modular Flows: Differential Molecular Generation
Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg
arXiv:2210.06032 || code
Structure-based Drug Design with Equivariant Diffusion Models
Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bronstein, Bruno Correia
NeurIPS 2022/arXiv:2210.13695 || code
De novo drug design framework based on mathematical programming method and deep learning model
Yujing Zhao, Qilei Liu, Xinyuan Wu, Lei Zhang, Jian Du, Qingwei Meng.
AIChE Journal. 2022, e17748
Structure-based de novo drug design using 3D deep generative models
Li, Yibo, Jianfeng Pei, and Luhua Lai.
Chemical science 12.41 (2021)
A 3D Generative Model for Structure-Based Drug Design
Luo, Shitong, et al.
Advances in Neural Information Processing Systems 34 (2021)
CELLS: Cost-Effective Evolution in Latent Space for Goal-Directed Molecular Generation
Chen, Zhiyuan, et al.
arXiv preprint arXiv:2112.00905 (2021)
DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology
Liu, Xuhan, et al.
Journal of cheminformatics 13.1 (2021) || DrugEx
Generating 3D Molecules for Target Protein Binding
Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, Shuiwang Ji
arxiv (2022) || GraphBP
Optimizing molecules using efficient queries from property evaluations
Hoffman, Samuel C., et al.
Nature Machine Intelligence 4.1 (2022)
Deep Evolutionary Learning for Molecular Design
K. Grantham, M. Mukaidaisi, H. K. Ooi, M. S. Ghaemi, A. Tchagang and Y. Li
IEEE Computational Intelligence Magazine, vol. 17, no. 2, pp. 14-28, May 2022
Fragment-Based Ligand Generation Guided by Geometric Deep Learning on Protein-Ligand Structure
Powers, Alexander, et al.
bioRxiv (2022)
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Peng, Xingang, et al.
arXiv preprint arXiv:2205.07249 (2022) || code
Reinforced Genetic Algorithm for Structure-based Drug Design
Fu, Tianfan, et al.
arXiv preprint arXiv:2211.16508 (2022)/ICML22 || code || website
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