Key papers

• Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems (2026)
  Xiao Zhu et al.
  4.95
• What Your Model Threw Away and Why You'll Want It Back: Masking, Fingerprinting, and Privacy from Discarded Geometry (2026)
  Zachary P. Bradshaw
  4.33
• Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools (2026)
  Konstantinos Bougiatiotis et al.
  4.33
• Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction (2026)
  James T. Pegg et al.
  4.33
• AI-Augmented Adaptive Digital Twin Modeling for Brain Tumor Evolution Prediction and Treatment Scheduling (2026)
  Wenxi Liu et al.
  4.33
• Multimodal Empirical Bayes Variational Autoencoders for Joint Longitudinal and Time-to-Event Modeling (2026)
  Anders Sj\"oberg et al.
  4.33
• S1-Omni: A Unified Multimodal Reasoning Model for Scientific Understanding, Prediction, and Generation (2026)
  Jiahao Zhao et al.
  4.33
• Spectral Flow Certificates for Depth-Aware Long-Range Propagation in Graph Neural Networks (2026)
  Ranjan Veerabhadraswamy et al.
  4.33
• Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts (2026)
  Keith G. Mills et al.
  4.33
• ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs (2025)
  Evan Dramko et al.
  1.39
• A Novel Graph-based Approach for Determining Molecular Similarity (2016)
  Maritza Hernandez et al.
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• Predicting Drug Interactions and Mutagenicity with Ensemble Classifiers on Subgraphs of Molecules (2016)
  Andrew Schaumberg et al.
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• Molecular Graph Convolutions: Moving Beyond Fingerprints (2016)
  Steven Kearnes et al.
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• Composing graphical models with neural networks for structured representations and fast inference (2016)
  Matthew J. Johnson and David Duvenaud and Alexander B. Wiltschko and Sandeep R. Datta and Ryan P. Adams
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• Function-Described Graphs for Structural Pattern Recognition (2016)
  Francesc Serratosa
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• Prediction of allosteric sites and mediating interactions through bond-to-bond propensities (2016)
  Benjamin R. C. Amor et al.
  —
• An algorithm for motif-based network design (2016)
  Tuomo M\"aki-Marttunen
  —
• Modelling Chemical Reasoning to Predict Reactions (2016)
  Marwin H.S. Segler et al.
  —
• Recovering Metabolic Networks using A Novel Hyperlink Prediction Method (2016)
  Muhan Zhang et al.
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• Learning molecular energies using localized graph kernels (2016)
  G. Ferr\'e et al.
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• Learning Graph-Level Representation for Drug Discovery (2017)
  Junying Li et al.
  —
• Deep Gaussian Mixture Models (2017)
  Cinzia Viroli and Geoffrey J. McLachlan
  —
• Semi-supervised learning of hierarchical representations of molecules using neural message passing (2017)
  Hai Nguyen et al.
  —
• A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks (2017)
  Farshad Harirchi et al.
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• Graph Memory Networks for Molecular Activity Prediction (2018)
  Trang Pham et al.
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• Multi-Objective De Novo Drug Design with Conditional Graph Generative Model (2018)
  Yibo Li et al.
  —
• GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders (2018)
  Martin Simonovsky et al.
  —
• Junction Tree Variational Autoencoder for Molecular Graph Generation (2018)
  Wengong Jin et al.
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• NeVAE: A Deep Generative Model for Molecular Graphs (2018)
  Bidisha Samanta et al.
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• Chemi-net: a graph convolutional network for accurate drug property prediction (2018)
  Ke Liu et al.
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• Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders (2018)
  Tengfei Ma et al.
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• Constrained Graph Variational Autoencoders for Molecule Design (2018)
  Qi Liu et al.
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• Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network (2018)
  Seongok Ryu et al.
  —
• MolGAN: An implicit generative model for small molecular graphs (2018)
  Nicola De Cao and Thomas Kipf
  —
• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (2018)
  Jiaxuan You et al.
  —
• Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials (2018)
  Peter Bj{\o}rn J{\o}rgensen et al.
  —
• ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors (2018)
  Mikhail Zaslavskiy et al.
  —
• N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules (2018)
  Shengchao Liu et al.
  —
• BayesGrad: Explaining Predictions of Graph Convolutional Networks (2018)
  Hirotaka Akita et al.
  —
• Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks (2018)
  Tian Xie et al.
  —
• Molecular Hypergraph Grammar with its Application to Molecular Optimization (2018)
  Hiroshi Kajino
  —
• Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (2018)
  Christopher Morris et al.
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• Structure-Based Networks for Drug Validation (2018)
  C\u{a}t\u{a}lina Cangea et al.
  —
• Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks (2018)
  Phillip Pope et al.
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• Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis (2019)
  Katsuhiko Ishiguro et al.
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• Molecular geometry prediction using a deep generative graph neural network (2019)
  Elman Mansimov et al.
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• Analyzing Learned Molecular Representations for Property Prediction (2019)
  Kevin Yang et al.
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• Tiered Latent Representations and Latent Spaces for Molecular Graphs (2019)
  Daniel T. Chang
  —
• Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models (2019)
  Carlos Fernandez-Lozano et al.
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• Decoding Molecular Graph Embeddings with Reinforcement Learning (2019)
  Steven Kearnes et al.
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• Adversarial Learned Molecular Graph Inference and Generation (2019)
  Sebastian P\"olsterl et al.
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• Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling (2019)
  Emmanuel Noutahi et al.
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• GraphNVP: An Invertible Flow Model for Generating Molecular Graphs (2019)
  Kaushalya Madhawa et al.
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• Path-Augmented Graph Transformer Network (2019)
  Benson Chen et al.
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• All SMILES Variational Autoencoder (2019)
  Zaccary Alperstein et al.
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• MolecularRNN: Generating realistic molecular graphs with optimized properties (2019)
  Mariya Popova et al.
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• Neural Markov Logic Networks (2019)
  Giuseppe Marra and Ond\v{r}ej Ku\v{z}elka
  —
• Scaffold-based molecular design using graph generative model (2019)
  Jaechang Lim et al.
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• Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules (2019)
  Niklas W. A. Gebauer and Michael Gastegger and Kristof T. Sch\"utt
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• A Two-Step Graph Convolutional Decoder for Molecule Generation (2019)
  Xavier Bresson and Thomas Laurent
  —