ArticleJournal of chemical information and modeling2025
PROFIS: Design of Target-Focused Libraries by Probing Continuous Fingerprint Space with Recurrent Neural Networks.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Unlocking Selenium Chemical Space via a Programmable Synthesis Platform Bearing Cannabinoid Receptor Recognition Motifs.Journal of the American Chemical Society · 2026Article
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3 authors.
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Abstract
This study introduces PROFIS, a new generative model capable of the design of structurally novel and target-focused compound libraries. The model relies on a recurrent neural network that was trained to decode embedded molecular fingerprints into SMILES strings. To identify potential novel ligands, a biological activity predictor is first trained on the low-dimensional fingerprint embedding space, enabling the identification of high-activity subspaces for a given drug target. The search for latent representations that are expected to yield active structures upon decoding to SMILES is conducted with a Bayesian optimization algorithm. We present the rationale for using SMILES as the output notation of the recurrent neural network and compare its performance with models trained to decode DeepSMILES and SELFIES strings. The paper demonstrates the application of this protocol to generate candidate ligands of the dopamine D
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