ArticleMolecular informatics2025
From High Dimensions to Human Insight: Exploring Dimensionality Reduction for Chemical Space Visualization.
Article in Molecular informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Efficient visualization of chemical space.Chemical science · 2026Article
- Cardiosim-Tox: an interpretable multitask deep learning QSAR platform with multimodal feature fusion for predicting hERG, Cav1.2, and Nav1.5 blockade risk and potency.Archives of toxicology · 2026Article
- SmileyLlama: modifying large language models for directed chemical space exploration.Nature computational science · 2026Article
- QSAR in the Browser: An Interactive Cheminformatics Web Application.Journal of chemical information and modeling · 2026Article
- Machine Learning for Graduation Prediction in Higher Education: A Systematic Review with a Bio-Inspired Optimization Perspective.Biomimetics (Basel, Switzerland) · 2026Review
- Computational design of low-volatility lubricants for space using interpretable machine learning.Journal of cheminformatics · 2026Article
- Undersampling Techniques for Nonlinear Chemical Space Visualization.Molecular informatics · 2026Article
- CRUSH-Cleavage Rules Using SMIRKS Heuristics: an enhanced molecular fragmentation algorithm.Journal of cheminformatics · 2026Article
- FIRST-ICU: forecasting interventions and risk stratification in the ICU using graph neural network autoencoders.NPJ digital medicine · 2026Article
- Chemical space visualization at scale: a survey of end-to-end pipelines and dataset-size archetypes.Journal of cheminformatics · 2026Review
- Generalization of long-range machine learning potentials in complex chemical spaces.Digital discovery · 2026Article
- SPACESHIP: Autonomous Mapping of Hardware-Dependent Synthesizable Space in Solution-Phase Gold Nanomaterials.Journal of the American Chemical Society · 2026Article
- Interpretable and Scalable Similarity Metrics for DNA-Encoded Library Design Using Generative Topographic Mapping.Molecular informatics · 2026Article
- Identifying effective coping strategies against mobbing for Generations Y and Z using a Pythagorean fuzzy decision support mechanism.Scientific reports · 2025Article
- DiaNat-DB-v2: A Molecular Database of Antidiabetic Compounds from Medicinal Plants and Functional Foods.ACS omega · 2025Article
- Comparative Chemical Space Analysis of Pesticides and Substances with Genotoxicity Data.Chemical research in toxicology · 2025Article
- Accurate structure-activity relationship prediction of antioxidant peptides using a multimodal deep learning framework.Journal of cheminformatics · 2025Article
- A Benchmark Set of Bioactive Molecules for Diversity Analysis of Compound Libraries and Combinatorial Chemical Spaces.Journal of chemical information and modeling · 2025Article
- Visualising lead optimisation series using reduced graphs.Journal of cheminformatics · 2025Article
- Protecting your skin: a highly accurate LSTM network integrating conjoint features for predicting chemical-induced skin irritation.Journal of cheminformatics · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dimensionality reduction is an important exploratory data analysis method that allows high-dimensional data to be represented in a human-interpretable lower-dimensional space. It is extensively applied in the analysis of chemical libraries, where chemical structure data - represented as high-dimensional feature vectors-are transformed into 2D or 3D chemical space maps. In this paper, commonly used dimensionality reduction techniques - Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and Generative Topographic Mapping (GTM) - are evaluated in terms of neighborhood preservation and visualization capability of sets of small molecules from the ChEMBL database.
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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.