ArticleMolecules (Basel, Switzerland)2021
Generative Adversarial Learning of Protein Tertiary Structures.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 25 citations in OpenAlex.
- DyVarMap: Integrating Conformational Dynamics and Interpretable Machine Learning for Cancer-Associated Missense Variant Classification in FGFR2.Bioengineering (Basel, Switzerland) · 2026Article
- A hybrid variational autoencoder and WGAN with gradient penalty for tertiary protein structure generation.Scientific reports · 2025Article
- Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches.International journal of molecular sciences · 2024Review
- PLM-GAN: A Large-Scale Protein Loop Modeling Using pix2pix GAN.ACS omega · 2024Article
- Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data.The Journal of chemical physics · 2023Article
- Data Size and Quality Matter: Generating Physically-Realistic Distance Maps of Protein Tertiary Structures.Biomolecules · 2022Article
- Generating tertiary protein structures via interpretable graph variational autoencoders.Bioinformatics advances · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Protein molecules are inherently dynamic and modulate their interactions with different molecular partners by accessing different tertiary structures under physiological conditions. Elucidating such structures remains challenging. Current momentum in deep learning and the powerful performance of generative adversarial networks (GANs) in complex domains, such as computer vision, inspires us to investigate GANs on their ability to generate physically-realistic protein tertiary structures. The analysis presented here shows that several GAN models fail to capture complex, distal structural patterns present in protein tertiary structures. The study additionally reveals that mechanisms touted as effective in stabilizing the training of a GAN model are not all effective, and that performance based on loss alone may be orthogonal to performance based on the quality of generated datasets. A novel contribution in this study is the demonstration that Wasserstein GAN strikes a good balance and manages to capture both local and distal patterns, thus presenting a first step towards more powerful deep generative models for exploring a possibly very diverse set of structures supporting diverse activities of a protein molecule in the cell.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.