ArticleJournal of cheminformatics2020
GEN: highly efficient SMILES explorer using autodidactic generative examination networks.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- VNFlow: integration of variational autoencoders and normalizing flows for novel molecular design.Journal of cheminformatics · 2025Article
- Status and Prospects of Research on Deep Learning-basedCurrent computer-aided drug design · 2025Review
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
- FOCUS on NOD2: Advancing IBD Drug Discovery with a User-Informed Machine Learning Framework.ACS medicinal chemistry letters · 2024Article
- A comprehensive review of artificial intelligence for pharmacology research.Frontiers in genetics · 2024Review
- Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities.Frontiers in pharmacology · 2024Review
- Mol-Zero-GAN: zero-shot adaptation of molecular generative adversarial network for specific protein targets.RSC advances · 2023Article
- A Computationally Assisted Approach for Designing Wearable Biosensors toward Non-Invasive Personalized Molecular Analysis.Advanced materials (Deerfield Beach, Fla.) · 2023Article
- Into the Unknown: How Computation Can Help Explore Uncharted Material Space.Journal of the American Chemical Society · 2022Review
- From Big Data to Artificial Intelligence: chemoinformatics meets new challenges.Journal of cheminformatics · 2020Article
- Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently.The Biochemical journal · 2020Review
- Memory-assisted reinforcement learning for diverse molecular de novo design.Journal of cheminformatics · 2020Article
- DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach.Journal of cheminformatics · 2020Article
- VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder.Molecules (Basel, Switzerland) · 2020Article
- Focused Library Generator: case of Mdmx inhibitors.Journal of computer-aided molecular design · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
Recurrent neural networks have been widely used to generate millions of de novo molecules in defined chemical spaces. Reported deep generative models are exclusively based on LSTM and/or GRU units and frequently trained using canonical SMILES. In this study, we introduce Generative Examination Networks (GEN) as a new approach to train deep generative networks for SMILES generation. In our GENs, we have used an architecture based on multiple concatenated bidirectional RNN units to enhance the validity of generated SMILES. GENs autonomously learn the target space in a few epochs and are stopped early using an independent online examination mechanism, measuring the quality of the generated set. Herein we have used online statistical quality control (SQC) on the percentage of valid molecular SMILES as examination measure to select the earliest available stable model weights. Very high levels of valid SMILES (95-98%) can be generated using multiple parallel encoding layers in combination with SMILES augmentation using unrestricted SMILES randomization. Our trained models combine an excellent novelty rate (85-90%) while generating SMILES with strong conservation of the property space (95-99%). In GENs, both the generative network and the examination mechanism are open to other architectures and quality criteria.
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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.