PROFIS: Design of Target-Focused Libraries by Probing Continuous Fingerprint Space with Recurrent Neural Networks

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,...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Hubert Rybka (21190469) (author)
مؤلفون آخرون:	Tomasz Danel (15875368) (author), Sabina Podlewska (3750124) (author)
منشور في:	2025
الموضوعات:	Biochemistry Molecular Biology Pharmacology Biotechnology Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified recurrent neural networks generate candidate ligands designing ligands outside bayesian optimization algorithm generate diverse libraries recurrent neural network focused compound libraries study introduces profis biological activity predictor 2 </ sub given drug target focused libraries profis network drug molecule biological target activity subspaces worth noting widespread use structurally novel selfies strings scripts shared paper demonstrates output notation model relies latent representations decode deepsmiles also emphasizes
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PROFIS: Design of Target-Focused Libraries by Probing Continuous Fingerprint Space with Recurrent Neural Networks

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