More Like this

1. Generating tertiary protein structures via interpretable graph variational autoencoders

   https://doi.org/10.1093/bioadv/vbab036  

   Guo, Xiaojie ; Du, Yuanqi ; Tadepalli, Sivani ; Zhao, Liang ; Shehu, Amarda ; Gromiha, ed., Michael ( November 2021 , Bioinformatics Advances)

   Modeling the structural plasticity of protein molecules remains challenging. Most research has focused on obtaining one biologically active structure. This includes the recent AlphaFold2 that has been hailed as a breakthrough for protein modeling. Computing one structure does not suffice to understand how proteins modulate their interactions and even evade our immune system. Revealing the structure space available to a protein remains challenging. Data-driven approaches that learn to generate tertiary structures are increasingly garnering attention. These approaches exploit the ability to represent tertiary structures as contact or distance maps and make direct analogies with images to harness convolution-based generative adversarial frameworks from computer vision. Since such opportunistic analogies do not allow capturing highly structured data, current deep models struggle to generate physically realistic tertiary structures.

   We present novel deep generative models that build upon the graph variational autoencoder framework. In contrast to existing literature, we represent tertiary structures as ‘contact’ graphs, which allow us to leverage graph-generative deep learning. Our models are able to capture rich, local and distal constraints and additionally compute disentangled latent representations that reveal the impact of individual latent factors. This elucidates what the factors control and makes our models more interpretable. Rigorous comparative evaluation along various metrics shows that the models, we propose advance the state-of-the-art. While there is still much ground to cover, the work presented here is an important first step, and graph-generative frameworks promise to get us to our goal of unraveling the exquisite structural complexity of protein molecules.

   Code is available at https://github.com/anonymous1025/CO-VAE.

   Supplementary data are available at Bioinformatics Advances online.

    

   more » « less
2. Interpretable Molecule Generation via Disentanglement Learning

   https://doi.org/10.1145/3388440.3414709  

   Du, Yuanqi ; Guo, Xiaojie ; Shehu, Amarda ; Zhao, Liang ( September 2020 , ACM International Conference on Bioinformatics, Computational Biology and Health Informatics)

   Designing molecules with specific structural and functional properties (e.g., drug-likeness and water solubility) is central to advancing drug discovery and material science, but it poses outstanding challenges both in wet and dry laboratories. The search space is vast and rugged. Recent advances in deep generative models are motivating new computational approaches building over deep learning to tackle the molecular space. Despite rapid advancements, state-of-the-art deep generative models for molecule generation have many limitations, including lack of interpretability. In this paper we address this limitation by proposing a generic framework for interpretable molecule generation based on novel disentangled deep graph generative models with property control. Specifically, we propose a disentanglement enhancement strategy for graphs. We also propose new deep neural architecture to achieve the above learning objective for inference and generation for variable-size graphs efficiently. Extensive experimental evaluation demonstrates the superiority of our approach in various critical aspects, such as accuracy, novelty, and disentanglement. 

   more » « less
3. Deep Latent-Variable Models for Controllable Molecule Generation

   https://doi.org/10.1109/BIBM52615.2021.9669692  

   Du, Yuanqi ; Wang, Yinkai ; Alam, Fardina ; Lu, Yuanjie ; Guo, Xiaojie ; Zhao, Liang ; Shehu, Amarda ( January 2021 , 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM))

   Representation learning via deep generative models is opening a new avenue for small molecule generation in silico. Linking chemical and biological space remains a key challenge. In this paper, we debut a graph-based variational autoencoder framework to address this challenge under the umbrella of disentangled representation learning. The framework permits several inductive biases that connect the learned latent factors to molecular properties. Evaluation on diverse benchmark datasets shows that the resulting models are powerful and open up an exciting line of research on controllable molecule generation in support of cheminformatics, drug discovery, and other application settings. 

   more » « less
4. Cross-dependent graph neural networks for molecular property prediction

   https://doi.org/10.1093/bioinformatics/btac039  

   Ma, Hehuan ; Bian, Yatao ; Rong, Yu ; Huang, Wenbing ; Xu, Tingyang ; Xie, Weiyang ; Ye, Geyan ; Huang, Junzhou ; Lu, ed., Zhiyong ( January 2022 , Bioinformatics)

   The crux of molecular property prediction is to generate meaningful representations of the molecules. One promising route is to exploit the molecular graph structure through graph neural networks (GNNs). Both atoms and bonds significantly affect the chemical properties of a molecule, so an expressive model ought to exploit both node (atom) and edge (bond) information simultaneously. Inspired by this observation, we explore the multi-view modeling with GNN (MVGNN) to form a novel paralleled framework, which considers both atoms and bonds equally important when learning molecular representations. In specific, one view is atom-central and the other view is bond-central, then the two views are circulated via specifically designed components to enable more accurate predictions. To further enhance the expressive power of MVGNN, we propose a cross-dependent message-passing scheme to enhance information communication of different views. The overall framework is termed as CD-MVGNN.

   We theoretically justify the expressiveness of the proposed model in terms of distinguishing non-isomorphism graphs. Extensive experiments demonstrate that CD-MVGNN achieves remarkably superior performance over the state-of-the-art models on various challenging benchmarks. Meanwhile, visualization results of the node importance are consistent with prior knowledge, which confirms the interpretability power of CD-MVGNN.

   The code and data underlying this work are available in GitHub at https://github.com/uta-smile/CD-MVGNN.

   Supplementary data are available at Bioinformatics online.

    

   more » « less
5. Graph Representation Learning for Protein Conformation Sampling

   Taseef Rahman ; Yuanqi Du ; Amarda Shehu ( January 2021 , IEEE Intl Conf on Comput Adv in Bio and Medical Sciences (ICCABS) 2021)

   Significant research on deep neural networks, culminating in AlphaFold2, convincingly shows that deep learning can predict the na- tive structure of a given protein sequence with high accuracy. In contrast, work on deep learning frameworks that can account for the structural plasticity of protein molecules remains in its infancy. Many researchers are now investigating deep generative models to explore the structure space of a protein. Current models largely use 2D convolution, leveraging representations of protein structures as contact maps or distance matri- ces. The goal is exclusively to generate protein-like, sequence-agnostic tertiary structures, but no rigorous metrics are utilized to convincingly make this case. This paper makes several contributions. It builds on momentum in graph representation learning and formalizes a protein tertiary structure as a contact graph. It demonstrates that graph repre- sentation learning outperforms models based on image convolution. This work also equips graph-based deep latent variable models with the abil- ity to learn from experimentally-available tertiary structures of proteins of varying lengths. The resulting models are shown to outperform state- of-the-art ones on rigorous metrics that quantify both local and distal patterns in physically-realistic protein structures. We hope this work will spur further research in deep generative models for obtaining a broader view of the structure space of a protein molecule. 

   more » « less