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

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,...

وصف كامل

محفوظ في:
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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