Generative Models for Graph-Based Protein Design

By John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola. 2019

631 citations recorded by the source corpus.

Subfields: Generative Models, Graph Learning, Computational Biology & Bioinformatics

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In Generative Models

In Graph Learning

In Computational Biology & Bioinformatics

Citations in the corpus

Rethinking Attention with Performers (2020, background): “…2018), document generation/summarization, time series prediction, generative modeling (e.g. image generation (Parmar et al., 2018)), music generation (Huang et al., 2019), and bioinformatics (Rives et al., 2019; Madani et al., 2020; Ingraham et al., 2019; Elnaggar et al., 2019; Du et al., 2020).”
Directional Message Passing for Molecular Graphs (2020, background): “Some recent GNNs have incorporated directional information by considering the change in local coordinate systems per atom (Ingraham et al., 2019).”
Robust deep learning based protein sequence design using ProteinMPNN (2022, methodology): “The journal of machine learning research, 15(1), 1929-1958.”
TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, background): “These works have been shown great potential in various applications like protein structure modeling [13, 17–19], molecular low-energy generation prediction [24–26], property/function prediction [22, 23] and molecule design [20, 21].”
Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models (2022, background): “Similarly, machine learning approaches have come to perform well for the sequence generation inverse problem [3, 21, 20].”
BERTology Meets Biology: Interpreting Attention in Protein Language Models (2020, methodology): “In addition to sequence-only approaches, Transformer architectures have been adapted to incorporate structural information [32].”
ProGen: Language Modeling for Protein Generation (2020, background): “Recent generative modeling work such as Ingraham et al. (2019) extends the transformer to condition it on a graph-structured speciﬁcation of a desired target.”
Accelerating Protein Design Using Autoregressive Generative Models (2019, methodology): “Finally, incorporating protein structural information may further improve the power of autoregressive models (Ingraham et al., 2019).”
Learning inverse folding from millions of predicted structures (2022, methodology): “Recently a new class of deep learning based approaches has been proposed, using generative models to predict sequences for structures (Ingraham et al., 2019; Strokach et al., 2020; Anand-Achim et al., 2021; Jing et al., 2021b), generate backbone structures (Anand & Huang, 2018; Eguchi et al.,…”
Learning from Protein Structure with Geometric Vector Perceptrons (2020, methodology): “These methods vary in their representation of geometry: while some, such as ProteinSolver and GraphQA, represent edges as a function of their length, others, such as Structured Transformer, indirectly encode the 3D geometry of the proximity graph in terms of relative orientations and other scalar fe”
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing (2020, background): “Recently, the leap of NLP through advanced LMs have successfully been generalized toward understanding the language of life through advanced LMs trained on proteins [16, 17, 18, 19, 20, 21, 22, 23, 24].”
Protein sequence design by conformational landscape optimization (2021, neutral): “Over the past 25 years, a number of algorithms (1 – 3) have been developed to solve this problem, including recent deep learning-based solutions (4 – 6).”
Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers (2020, background): “image generation [37]), music generation [27], and analysis of biological sequences [40, 34, 28, 21, 20].”
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022, neutral): “Another class of methods, referred to as fixed backbone sequence design [12, 20, 40, 27, 18], attempts to solve the problem of identifying a sequence that folds into any given designable backbone structure.”