De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update

Lin, Eugene; Lin, Chih-Chien; Lane, Hsien‐Yuan

doi:10.1021/acs.jcim.1c01361

Cited by 26 publications

(20 citation statements)

References 123 publications

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“…Computational tools promise to help to overcome key limitations of cytokine drugs, including pleiotropy, redundancy, poor pharmacokinetics and toxicity 102 . There are multiple approaches to computer-assisted protein design, for example, relying on predicted changes in free energy 103 , multiple sequence alignments 103,104 , backbone redesign 105,106 or deep learning 107,108 . Moreover, computational tools initially designed for protein structure prediction, such as Rosetta 109 or AlphaFold 110 , can provide valuable insights into protein design.…”

Section: Methodsmentioning

confidence: 99%

Engineering cytokine therapeutics

Deckers¹,

Anbergen²,

Hokke

et al. 2023

Nat Rev Bioeng

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Cytokines have pivotal roles in immunity, making them attractive as therapeutics for a variety of immune-related disorders. However, the widespread clinical use of cytokines has been limited by their short blood half-lives and severe side effects caused by low specificity and unfavourable biodistribution. Innovations in bioengineering have aided in advancing our knowledge of cytokine biology and yielded new technologies for cytokine engineering. In this Review, we discuss how the development of bioanalytical methods, such as sequencing and high-resolution imaging combined with genetic techniques, have facilitated a better understanding of cytokine biology. We then present an overview of therapeutics arising from cytokine re-engineering, targeting and delivery, mRNA therapeutics and cell therapy. We also highlight the application of these strategies to adjust the immunological imbalance in different immune-mediated disorders, including cancer, infection and autoimmune diseases. Finally, we look ahead to the hurdles that must be overcome before cytokine therapeutics can live up to their full potential.

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Section: Methodsmentioning

confidence: 99%

Engineering cytokine therapeutics

Deckers¹,

Anbergen²,

Hokke

et al. 2023

Nat Rev Bioeng

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“…Nowadays, GAN has gained popularity in both academia and industry due to its numerous applications, not only in the fields of peptide and protein design, chemical material design, , and medicine, − but also in image generation, − speech synthesis, − and computer vision, − among others. These studies demonstrate the broad range of applications of the GAN methods.…”

Section: Methods For Small Molecular Data Challengesmentioning

confidence: 99%

Machine Learning Methods for Small Data Challenges in Molecular Science

et al. 2023

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Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

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“…Briefly, deep generative models broadly fall into four categories: Recursive Neural Networks (RNN) [59][60][61][62][63], Generative Adversarial Networks (GAN) [64][65][66], Variational Auto-Encoders (VAE) [67][68][69][70][71][72][73][74][75][76][77] and reinforcement learning (RL)-based strategies [78][79][80][81][82][83]. The inception technique is also noteworthy for its simplicity [84].…”

Section: Generative Designmentioning

confidence: 99%

Application of Machine Learning to the Design of Energetic Materials: Preliminary Experience and Comparison with Alternative Techniques

Wespiser

Mathieu

2023

Propellants Explo Pyrotec

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The last few years have seen a steep rise in the use of data-driven methods in different scientific fields historically relying on theoretical or empirical approaches. Chemistry is at the forefront of this paradigm shift due to the longstanding use of computational tools involved in the calculation of molecular structures and properties. In this paper, we showcase examples from the literature as well as work in progress in our lab in order to give a brief overview on how these methods can benefit the energetic materials community. A deep learning approach is compared to "traditional" QSPR and semi-empirical approaches for molecular property prediction, and specificities inherent to energetic materials are discussed. Deep generative models for the design of new energetic materials are also presented. We conclude by giving our view on the most promising strategies for future in silico generation of new energetic materials satisfying the performance/sensitivity trade-off.

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De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update

Cited by 26 publications

References 123 publications

Engineering cytokine therapeutics

Engineering cytokine therapeutics

Machine Learning Methods for Small Data Challenges in Molecular Science

Application of Machine Learning to the Design of Energetic Materials: Preliminary Experience and Comparison with Alternative Techniques

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