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

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 Deep

SUBMITTER: Rybka H 

PROVIDER: S-EPMC12076512 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature