ArticleJournal of cheminformatics2020
Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors.
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 22 papers.
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Who cites it
22 citing papers in PubMed, 71 citations in OpenAlex.
- NaviDiv: a web app for monitoring chemical diversity in generative molecular design.Digital discovery · 2026Article
- Identification of nanomolar adenosine ANature communications · 2025Article
- Scaffold and SAR studies on c-MET inhibitors using machine learning approaches.Journal of pharmaceutical analysis · 2025Article
- Review
- CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability.Journal of cheminformatics · 2025Article
- fragSMILES as a chemical string notation for advanced fragment and chirality representation.Communications chemistry · 2025Article
- Predicting cell morphological responses to perturbations using generative modeling.Nature communications · 2025Article
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
- PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models.Journal of cheminformatics · 2024Article
- MTMol-GPT: De novo multi-target molecular generation with transformer-based generative adversarial imitation learning.PLoS computational biology · 2024Article
- AiZynth impact on medicinal chemistry practice at AstraZeneca.RSC medicinal chemistry · 2024Article
- PED: a novel predictor-encoder-decoder model for Alzheimer drug molecular generation.Frontiers in artificial intelligence · 2024Article
- Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities.Frontiers in pharmacology · 2024Review
- ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.ArXiv · 2023Article
- Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit.Journal of chemical information and modeling · 2023Review
- MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules.Journal of cheminformatics · 2023Article
- GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics.Journal of cheminformatics · 2021Article
- Comprehensive Survey of Recent Drug Discovery Using Deep Learning.International journal of molecular sciences · 2021Review
- Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.Molecular diversity · 2021Article
- Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study.Journal of cheminformatics · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant known molecules, generated molecules may have a problem of novelty. Novelty is important in highly competitive areas of medicinal chemistry, such as the discovery of kinase inhibitors. In this study, de novo molecular generation based on recurrent neural networks was applied to discover a new chemical space of kinase inhibitors. During the application, the practicality was evaluated, and new inspiration was found. With the successful discovery of one potent Pim1 inhibitor and two lead compounds that inhibit CDK4, AI-based molecular generation shows potentials in drug discovery and development.
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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.