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For the last decade, the field of deep learning and AI has been dominated by applications to images and text. However, in the past two years, the field has seen an upsurge of chemical and biological applications.
The international conference on learning representations [ICLR], is the largest academic AI conference in the world, with an h5-index of 253, and was no exception to this trend in chemical/biological topics. ICLR 2022 included 14 conference papers on small molecules, 5 on proteins, 7 on other biological topics, and an entire workshop devoted to machine learning for drug discovery.
There were also many methods papers for data types commonly encountered in chemistry. This included 4 papers on point clouds [small molecules, ions, and proteins], 15 papers on graph neural networks [small molecules and biochemical interaction networks], and 12 papers treating equivariance [an important property of data with 3D coordinates, including molecular structures].
Here I’ve gathered and summarized all ICLR papers with application to chemistry and biology. Happy reading!
Following are other papers that are not directly focused on chemical/biological applications, but which deal with related topics. Papers can be found by name in the ICLR conference proceedings on OpenReview:
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
Deep Point Cloud Reconstruction
TPU-GAN: Learning temporal coherence from dynamic point cloud sequences
Scattering Networks on the Sphere for Scalable and Rotationally Equivariant Spherical CNNs
Top-N: Equivariant Set and Graph Generation without Exchangeability
Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks
Equivariant Subgraph Aggregation Networks
Geometric and Physical Quantities improve E(3) Equivariant Message Passing
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?
Group equivariant neural posterior estimation
Properties from mechanisms: an equivariance perspective on identifiable representation learning
Equivariant Graph Mechanics Networks with Constraints
Frame Averaging for Invariant and Equivariant Network Design
A Program to Build E(N)-Equivariant Steerable CNNs
DEGREE: Decomposition Based Explanation for Graph Neural Networks
Graph Condensation for Graph Neural Networks
Automated Self-Supervised Learning for Graphs
On Evaluation Metrics for Graph Generative Models
A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"
Do We Need Anisotropic Graph Neural Networks?
Large-Scale Representation Learning on Graphs via Bootstrapping
GRAND++: Graph Neural Diffusion with A Source Term
Graph Neural Networks with Learnable Structural and Positional Representations
Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction
How Attentive are Graph Attention Networks?
Information Gain Propagation: a New Way to Graph Active Learning with Soft Labels
Expressiveness and Approximation Properties of Graph Neural Networks
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions.
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