Data to 7 Oct 2026

GuacaMol: Benchmarking Models for De Novo Molecular Design

arXiv 1811.09621 ↗First posted 22 Nov 2018q-bio.QM cs.LG physics.chem-phcited by 294

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Cited by 39 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in many application areas, models for molecular design based on neural networks appeared recently and show promising results. However, the new models have not been profiled on consistent tasks, and comparative studies to well-established algorithms have only seldom been performed. To standardize the assessment of both classical and neural models for de novo molecular design, we propose an evaluation framework, GuacaMol, based on a suite of standardized benchmarks. The benchmark tasks encompass measuring the fidelity of the models to reproduce the property distribution of the training sets, the ability to generate novel molecules, the exploration and exploitation of chemical space, and a variety of single and multi-objective optimization tasks. The benchmarking open-source Python code, and a leaderboard can be found on https://benevolent.ai/guacamol

Key facts. Cited by 294 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Nov 2018.

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Citing papers per year: 2021: 28; 2022: 35; 2023: 52; 2024: 60; 2025: 68; 2026: 51.

294 papers.

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2022DiGress: Discrete Denoising diffusion for graph generationcited by 503
2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
2021Geometric Deep Learning on Molecular Representationscited by 113
2024Scientific Large Language Models: A Survey on Biological & Chemical Domainscited by 80
2022Local Latent Space Bayesian Optimization over Structured Inputscited by 78
2022SELFIES and the future of molecular string representationscited by 76
2024Discrete-state Continuous-time Diffusion for Graph Generationcited by 73
2021Learning to Extend Molecular Scaffolds with Structural Motifscited by 70
2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
2022Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matchingcited by 67
2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
2021High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learningcited by 54

38 more

2021Molecule Generation by Principal Subgraph Mining and Assemblingcited by 53
2021Differentiable Scaffolding Tree for Molecular Optimizationcited by 53
2025Position: Graph Learning Will Lose Relevance Due To Poor Benchmarkscited by 52
2021Improving black-box optimization in VAE latent space using decoder uncertaintycited by 51
2025GenMol: A Drug Discovery Generalist with Discrete Diffusioncited by 47
2022Reinforced Genetic Algorithm for Structure-based Drug Designcited by 46
2023De Novo Molecular Generation via Connection-aware Motif Miningcited by 45
2021Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Designcited by 44
2022Retrieval-based Controllable Molecule Generationcited by 44
2024Graph Diffusion Transformers for Multi-Conditional Molecular Generationcited by 44
2025El Agente: An Autonomous Agent for Quantum Chemistrycited by 43
2025Feedback Descent: Open-Ended Text Optimization via Pairwise Comparisoncited by 42
2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 40
2023SELFormer: Molecular Representation Learning via SELFIES Language Modelscited by 40
2024Quantifying the Gain in Weak-to-Strong Generalizationcited by 40
2024RGFN: Synthesizable Molecular Generation Using GFlowNetscited by 39
2021Learning to design drug-like molecules in three-dimensional space using deep generative modelscited by 37
2023Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasetscited by 37
2024Genetic-guided GFlowNets for Sample Efficient Molecular Optimizationcited by 36
2021DOCKSTRING: easy molecular docking yields better benchmarks for ligand designcited by 33
20223DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Designcited by 33
2021JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Designcited by 31
2023Genetic algorithms are strong baselines for molecule generationcited by 31
2021Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotationscited by 29
2022Discovering Many Diverse Solutions with Bayesian Optimizationcited by 29
2024Cometh: A continuous-time discrete-state graph diffusion modelcited by 29
2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
2021How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Moleculescited by 27
2024Diffusion Models in textitDe Novo Drug Designcited by 26
2024LICO: Large Language Models for In-Context Molecular Optimizationcited by 26
2023Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3Dcited by 24
2024Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trialscited by 24
2024SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluationcited by 23
2021MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generationcited by 22
2023Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materialscited by 22
2023ExPT: Synthetic Pretraining for Few-Shot Experimental Designcited by 22
2024A survey and benchmark of high-dimensional Bayesian optimization of discrete sequencescited by 22
2024Molecule Generation with Fragment Retrieval Augmentationcited by 22

50 most cited of 294 All 294 (JSON)