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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...

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Updated: Dec 22, 2025

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

Hubert Rybka^1,2, Tomasz Danel², Sabina Podlewska³

¹Doctoral School of Exact and Natural Sciences, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków, Poland.

Journal of Chemical Information and Modeling

|April 28, 2025

Summary

This summary is machine-generated.

This study introduces PROFIS, a generative model for designing novel drug compounds. PROFIS uses a recurrent neural network and Bayesian optimization to create target-focused compound libraries, aiding drug discovery.

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Area of Science:

Computational chemistry
Drug discovery
Artificial intelligence in medicine

Background:

Developing novel, effective drug candidates is a critical challenge in pharmaceutical research.
Existing methods for generating compound libraries may lack structural novelty or target specificity.

Purpose of the Study:

To introduce PROFIS, a novel generative model for designing structurally novel and target-focused compound libraries.
To demonstrate the model's capability in identifying potential novel ligands for specific drug targets.
To showcase the application of PROFIS in scaffold-hopping for exploring new chemical spaces.

Main Methods:

Utilizing a recurrent neural network trained to decode molecular fingerprints into SMILES strings.
Employing a biological activity predictor trained on fingerprint embeddings to identify high-activity subspaces.
Applying a Bayesian optimization algorithm to search for optimal latent representations.

Main Results:

PROFIS successfully generates structurally novel and target-focused compound libraries.
The model demonstrates effectiveness in scaffold-hopping, enabling the design of ligands outside explored chemical spaces.
Application to dopamine D2 receptor ligands showcases the protocol's potential for drug discovery.

Conclusions:

PROFIS offers a versatile and effective approach for generating diverse compound libraries for any biological target.
The model's ability to explore novel chemical spaces accelerates the identification of potential drug candidates.
Shared scripts on GitHub ensure the widespread applicability and adoption of the PROFIS protocol.