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  3. Recurrent Neural Networks Predict Future Peptide Aggregation For Drug Development.
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  3. Recurrent Neural Networks Predict Future Peptide Aggregation For Drug Development.

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Recurrent Neural Networks Predict Future Peptide Aggregation for Drug Development.

Prageeth R Wijewardhane1, Katelyn Smith2, Jonathan Fine1,2

  • 1Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States.

Molecular Pharmaceutics
|October 15, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence (AI) models predict peptide aggregation using Thioflavin T (ThioT) assays. Recurrent neural networks accurately forecast future ThioT curves, optimizing pharmaceutical development and reducing resource needs.

Keywords:
deep learningdrug developmentlong short-term memory (LSTM)peptide aggregationphysical stability predictionsequence-to-sequence modelingtherapeutic peptidesthioflavin T (ThioT) assay

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Area of Science:

  • Pharmaceutical Science
  • Computational Chemistry
  • Biotechnology

Background:

  • Physical stability of active pharmaceutical ingredients (APIs) is crucial for drug development.
  • Solution conditions significantly impact therapeutic peptide stability.
  • Thioflavin T (ThioT) assays measure peptide aggregation but large-scale studies are resource-intensive.

Purpose of the Study:

  • To develop artificial intelligence (AI) methods for predicting peptide aggregation and ThioT curves.
  • To enable fast and cost-effective prediction of peptide physical stability.
  • To reduce the need for extensive, resource-heavy stability assays in pharmaceutical development.

Main Methods:

  • Formulated peptide aggregation prediction as a natural language processing "language translation" problem.
  • Developed a sequence-to-sequence long short-term memory (LSTM)-based recurrent neural network (RNN) model.
  • Used initial and 1-month ThioT assay data to predict future (6 and 12 months) ThioT curves.

    • Main Results:

    • The LSTM model achieved an excellent average Mean Absolute Error (MAE) of 2.04 for predicting 6-month ThioT curves.
    • The LSTM model's predictions were experimentally validated.
    • Both LSTM and Multilayer Perceptron (MLP) models showed comparable MAEs at the 12-month time point with limited data.

    Conclusions:

    • LSTM models can accurately predict future ThioT curves using short-term stability data (initial and 1 month).
    • Recurrent neural network models offer a valuable tool for the pharmaceutical industry.
    • These AI models can accelerate the exploration of formulation landscapes for API physical stability.