ArticleJournal of chemical information and modeling2017
Chemical Space Mimicry for Drug Discovery.
Article in Journal of chemical information and modeling, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed.
- AI-Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery.Medicinal research reviews · 2026Review
- From Latent Manifolds to Targeted Molecular Probes: An Interpretable, Kinome-Scale Generative Machine Learning Framework for Family-Based Kinase Ligand Design.Biomolecules · 2026Article
- How evaluation choices distort the outcome of generative drug discovery.Journal of cheminformatics · 2025Article
- Going beyond SMILES enumeration for data augmentation in generative drug discovery.Digital discovery · 2025Article
- PCF-VAE: posterior collapse free variational autoencoder for de novo drug design.Scientific reports · 2025Article
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- CombiningRSC advances · 2024Article
- Designed with interactome-based deep learning.Nature chemical biology · 2024Article
- Chemical language modeling with structured state space sequence models.Nature communications · 2024Article
- Prospective de novo drug design with deep interactome learning.Nature communications · 2024Article
- Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities.Frontiers in pharmacology · 2024Review
- Artificial intelligence for natural product drug discovery.Nature reviews. Drug discovery · 2023Review
- A molecule perturbation software library and its application to study the effects of molecular design constraints.Journal of cheminformatics · 2023Article
- cMolGPT: A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation.Molecules (Basel, Switzerland) · 2023Article
- Leveraging molecular structure and bioactivity with chemical language models for de novo drug design.Nature communications · 2023Article
- Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.Journal of chemical information and modeling · 2022Article
- Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation.Journal of cheminformatics · 2022Article
- Perplexity-Based Molecule Ranking and Bias Estimation of Chemical Language Models.Journal of chemical information and modeling · 2022Article
- Artificial Intelligence inCurrent topics in medicinal chemistry · 2022Review
- Deep Learning Applied to Ligand-Based De Novo Drug Design.Methods in molecular biology (Clifton, N.J.) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
We describe a new library generation method, Machine-based Identification of Molecules Inside Characterized Space (MIMICS), that generates sets of molecules inspired by a text-based input. MIMICS-generated libraries were found to preserve distributions of properties while simultaneously increasing structural diversity. Newly identified MIMICS-generated compounds were found to be bioactive as inhibitors of specific components of the unfolded protein response (UPR) and the VEGFR2 pathway in cell-based assays, thus confirming the applicability of this methodology toward drug design applications. Wider application of MIMICS could facilitate the efficient utilization of chemical space.
Indexed as
Identifiers
What OpenQuestion holds
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.