ArticleCommunications chemistry2023
Variational autoencoder-based chemical latent space for large molecular structures with 3D complexity.
Article in Communications chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- Discovering interpretable drug formulation behavior patterns via a mechanistic-augmented conditional variational autoencoder.Scientific reports · 2026Article
- Natural Products in Epilepsy Treatment: From Traditional Medicine Towards Computational Drug Discovery.Current issues in molecular biology · 2026Review
- From mechanistic modeling to AI-driven design: computational strategies for targeting the γ-secretase complex.Briefings in bioinformatics · 2026Review
- Unveiling key descriptorsChemical science · 2026Article
- Disentangled autoencoding equivariant diffusion model for controlled generation of 3D molecules.Nature communications · 2026Article
- MolMod: a molecular modification platform for molecular property optimization via fragment-based generation.Molecular diversity · 2026Article
- Graph latent diffusion-based molecular representation learning for enhanced generalization in molecular property prediction.Journal of cheminformatics · 2026Article
- Nature meets machine: the AI renaissance in natural product drug discovery.Natural products and bioprospecting · 2026Review
- A review of recent advances in generative artificial intelligence models for biomolecular sciences.Acta pharmaceutica Sinica. B · 2026Review
- Developments and challenges in hit progression within fragment-based drug discovery.Nature communications · 2026Review
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- RetroScore: graph edit distance-guided retrosynthesis for accessibility scoring with route metrics.Journal of cheminformatics · 2025Article
- NCIVISION: A Siamese Neural Network for Molecular Similarity Prediction MEP and RDG Images.Molecules (Basel, Switzerland) · 2025Article
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- Perspective on Theoretical Modeling of Soft Molecular Machines and Devices: A Fusion of Data-Driven Approaches with Traditional Computational Chemistry Algorithms.ChemistryOpen · 2025Article
- Stereochemistry-aware string-based molecular generation.PNAS nexus · 2025Article
- MolAI: A Deep Learning Framework for Data-Driven Molecular Descriptor Generation and Advanced Drug Discovery Applications.Journal of chemical information and modeling · 2025Article
- A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network.IEEE journal of biomedical and health informatics · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
The structural diversity of chemical libraries, which are systematic collections of compounds that have potential to bind to biomolecules, can be represented by chemical latent space. A chemical latent space is a projection of a compound structure into a mathematical space based on several molecular features, and it can express structural diversity within a compound library in order to explore a broader chemical space and generate novel compound structures for drug candidates. In this study, we developed a deep-learning method, called NP-VAE (Natural Product-oriented Variational Autoencoder), based on variational autoencoder for managing hard-to-analyze datasets from DrugBank and large molecular structures such as natural compounds with chirality, an essential factor in the 3D complexity of compounds. NP-VAE was successful in constructing the chemical latent space from large-sized compounds that were unable to be handled in existing methods, achieving higher reconstruction accuracy, and demonstrating stable performance as a generative model across various indices. Furthermore, by exploring the acquired latent space, we succeeded in comprehensively analyzing a compound library containing natural compounds and generating novel compound structures with optimized functions.
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Registered trials
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.