Machine Learning for Drug Discovery at ICLR 2022

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 […]

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!

 

Small Molecules/Drug Discovery

GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation

Energy-Inspired Molecular Conformation Optimization

An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch

Differentiable Scaffolding Tree for Molecule Optimization

Data-Efficient Graph Grammar Learning for Molecular Generation

Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design

Learning to Extend Molecular Scaffolds with Structural Motifs

Equivariant Transformers for Neural Network based Molecular Potentials

Pre-training Molecular Graph Representation with 3D Geometry

Spherical Message Passing for 3D Molecular Graphs

Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations

Chemical-Reaction-Aware Molecule Representation Learning

Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond

GeneDisco: A Benchmark for Experimental Design in Drug Discovery

Proteins

Ancestral protein sequence reconstruction using a tree-structured Ornstein-Uhlenbeck variational autoencoder

OntoProtein: Protein Pretraining With Gene Ontology Embedding

Geometric Transformers for Protein Interface Contact Prediction

Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design

Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking

Assorted Biology/Chemistry

Granger causal inference on DAGs identifies genomic loci regulating transcription

Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

Crystal Diffusion Variational Autoencoder for Periodic Material Generation

Maximum n-times Coverage for Vaccine Design

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

MoReL: Multi-omics Relational Learning

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Imaging Phenotypes of Disease

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:

Point Clouds

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

Equivariance

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

 

Graph NNs

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.