ArticleScience and technology of advanced materials2022
Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring.
Article in Science and technology of advanced materials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Large language models open new way of AI-assisted molecule design for chemists.Journal of cheminformatics · 2025Article
- A data-driven generative strategy to avoid reward hacking in multi-objective molecular design.Nature communications · 2025Article
- Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search.Journal of chemical information and modeling · 2022Article
- QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization.Journal of chemical information and modeling · 2022Article
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Authors and funding
7 authors.
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Abstract
Recently, artificial intelligence (AI)-enabled de novo molecular generators (DNMGs) have automated molecular design based on data-driven or simulation-based property estimates. In some domains like the game of Go where AI surpassed human intelligence, humans are trying to learn from AI about the best strategy of the game. To understand DNMG's strategy of molecule optimization, we propose an algorithm called characteristic functional group monitoring (CFGM). Given a time series of generated molecules, CFGM monitors statistically enriched functional groups in comparison to the training data. In the task of absorption wavelength maximization of pure organic molecules (consisting of H, C, N, and O), we successfully identified a strategic change from diketone and aniline derivatives to quinone derivatives. In addition, CFGM led us to a hypothesis that 1,2-quinone is an unconventional chromophore, which was verified with chemical synthesis. This study shows the possibility that human experts can learn from DNMGs to expand their ability to discover functional molecules.
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