Molecular Property Prediction

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

18 leaderboard tables shown for this task, 18 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 18 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

21 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

4 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 199 papers with code (354 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Graph Attention Networks
  30 Oct 2017 93 repositories listed Syntology ran 50 of 106 samples · 56 unverified · 43 pointer-only (licence)
  We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph…
• Semi-Supervised Classification with Graph Convolutional Networks
  9 Sep 2016 55 repositories listed Syntology ran 31 of 58 samples · 27 unverified · 22 pointer-only (licence)
  We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
• Neural Message Passing for Quantum Chemistry
  4 Apr 2017 20 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 1 pointer-only (licence)
  Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.
• How Powerful are Graph Neural Networks?
  1 Oct 2018 19 repositories listed Syntology ran 3 of 10 samples · 7 unverified · 5 pointer-only (licence)
  Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.
• Strategies for Pre-training Graph Neural Networks
  29 May 2019 11 repositories listed Syntology ran 10 of 14 samples · 4 unverified · 2 pointer-only (licence)
  Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.
• How Attentive are Graph Attention Networks?
  30 May 2021 8 repositories listed Syntology ran 6 of 16 samples · 10 unverified · 1 pointer-only (licence)
  Because GATs use a static attention mechanism, there are simple graph problems that GAT cannot express: in a controlled problem, we show that static attention hinders GAT from even fitting the training data.
• Principal Neighbourhood Aggregation for Graph Nets
  12 Apr 2020 8 repositories listed Syntology ran 33 of 55 samples · 22 unverified · 48 pointer-only (licence)
  Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.
• AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
  13 Mar 2020 7 repositories listed Syntology ran 0 of 11 samples · 11 unverified
  We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file.
• Do Transformers Really Perform Bad for Graph Representation?
  9 Jun 2021 5 repositories listed
  Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.
• InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
  31 Jul 2019 5 repositories listed
  There are also some recent methods based on language models (e.
• MoleculeNet: A Benchmark for Molecular Machine Learning
  2 Mar 2017 5 repositories listed
  However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to…
• A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
  12 Sep 2022 4 repositories listed Syntology ran 2 of 8 samples · 6 unverified · 2 pointer-only (licence)
  Although artificial intelligence (AI) has made significant progress in understanding molecules in a wide range of fields, existing models generally acquire the single cognitive ability from the single molecular modality.
• Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
  23 Jun 2022 4 repositories listed Syntology ran 3 of 4 samples · 1 unverified
  Despite their widespread success in various domains, Transformer networks have yet to perform well across datasets in the domain of 3D atomistic graphs such as molecules even when 3D-related inductive biases like…
• Recipe for a General, Powerful, Scalable Graph Transformer
  25 May 2022 4 repositories listed Syntology ran 3 of 21 samples · 18 unverified
  We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks.
• Analyzing Learned Molecular Representations for Property Prediction
  2 Apr 2019 4 repositories listed Syntology ran 3 of 15 samples · 12 unverified
  In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary…
• Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
  7 Feb 2024 3 repositories listed Syntology ran 10 of 13 samples · 3 unverified
  We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning.
• Isotropic Gaussian Processes on Finite Spaces of Graphs
  3 Nov 2022 3 repositories listed
  We propose a principled way to define Gaussian process priors on various sets of unweighted graphs: directed or undirected, with or without loops.
• Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
  30 Sep 2021 3 repositories listed
  Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods.
• ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
  19 Oct 2020 3 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  GNNs and chemical fingerprints are the predominant approaches to representing molecules for property prediction.
• Self-Supervised Graph Transformer on Large-Scale Molecular Data
  18 Jun 2020 3 repositories listed
  We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.
• GuacaMol: Benchmarking Models for De Novo Molecular Design
  22 Nov 2018 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles.
• GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks
  24 Apr 2025 2 repositories listed
  Understanding complex three-dimensional (3D) structures of graphs is essential for accurately modeling various properties, yet many existing approaches struggle with fully capturing the intricate spatial relationships…
• Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy
  22 Oct 2024 2 repositories listed Syntology ran 1 of 7 samples · 6 unverified · 6 pointer-only (licence)
  We use membership inference attacks, a common method to assess privacy that is largely unexplored in the context of drug discovery, to examine neural networks for molecular property prediction in a black-box setting.
• Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python
  18 Jul 2024 2 repositories listed
  In this work, we present scikit-fingerprints, a Python package for computation of molecular fingerprints for applications in chemoinformatics.
• The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA
  2 May 2024 2 repositories listed
  However, we observed that absolute validation loss is not a definitive indicator of model performance - contradicts previous research - at least for fine-tuning tasks: instead, model size plays a crucial role.
• Can Large Language Models Understand Molecules?
  5 Jan 2024 2 repositories listed
  Notably, LLaMA-based SMILES embeddings show results comparable to pre-trained models on SMILES in molecular prediction tasks and outperform the pre-trained models for the DDI prediction tasks.
• Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning
  11 Nov 2023 2 repositories listed
  The versatility of multimodal deep learning holds tremendous promise for advancing scientific research and practical applications.
• Lo-Hi: Practical ML Drug Discovery Benchmark
  10 Oct 2023 2 repositories listed
  We analyzed modern benchmarks and showed that they are unrealistic and overoptimistic.
• On the Stability of Expressive Positional Encodings for Graphs
  4 Oct 2023 2 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 1 pointer-only (licence)
  Despite many attempts to address non-uniqueness, most methods overlook stability, leading to poor generalization on unseen graph structures.
• Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search
  19 Jul 2023 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction.

Syntology lines on 18 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.