ArticleJournal of cheminformatics2019
Exploring the GDB-13 chemical space using deep generative models.
Article in Journal of cheminformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed.
- Deep learning for small-molecule drug discovery: From molecular design to clinical translation.Journal of pharmaceutical analysis · 2026Review
- Assessing the factors influencing the quality of pocket-conditioned 3D generative models.Journal of cheminformatics · 2026Article
- Design and optimization of novel succinate dehydrogenase inhibitors against agricultural fungi based on transformer model.Molecular diversity · 2026Article
- Article
- Test-Time Training Scaling Laws for Chemical Exploration in Drug Design.Journal of chemical information and modeling · 2025Article
- A quantum chemical dataset of interacting molecular pairs for chemical reaction studies.Journal of cheminformatics · 2025Article
- How evaluation choices distort the outcome of generative drug discovery.Journal of cheminformatics · 2025Article
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- Identification of nanomolar adenosine ANature communications · 2025Article
- Transfer Learning-Enhanced Prediction of Glass Transition Temperature in Bismaleimide-Based Polyimides.Polymers · 2025Article
- PepINVENT: generative peptide design beyond natural amino acids.Chemical science · 2025Article
- A beginner's approach to deep learning applied to VS and MD techniques.Journal of cheminformatics · 2025Review
- CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability.Journal of cheminformatics · 2025Article
- Positional embeddings and zero-shot learning using BERT for molecular-property prediction.Journal of cheminformatics · 2025Article
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
- Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking Simulations.Journal of chemical theory and computation · 2024Article
- Review
- MTMol-GPT: De novo multi-target molecular generation with transformer-based generative adversarial imitation learning.PLoS computational biology · 2024Article
- Reinvent 4: Modern AI-driven generative molecule design.Journal of cheminformatics · 2024Article
- ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.ArXiv · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Recent applications of recurrent neural networks (RNN) enable training models that sample the chemical space. In this study we train RNN with molecular string representations (SMILES) with a subset of the enumerated database GDB-13 (975 million molecules). We show that a model trained with 1 million structures (0.1% of the database) reproduces 68.9% of the entire database after training, when sampling 2 billion molecules. We also developed a method to assess the quality of the training process using negative log-likelihood plots. Furthermore, we use a mathematical model based on the "coupon collector problem" that compares the trained model to an upper bound and thus we are able to quantify how much it has learned. We also suggest that this method can be used as a tool to benchmark the learning capabilities of any molecular generative model architecture. Additionally, an analysis of the generated chemical space was performed, which shows that, mostly due to the syntax of SMILES, complex molecules with many rings and heteroatoms are more difficult to sample.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.