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Learnable protein representations in computational biology for predicting drug-target affinity.

Rachit Kumar^1,2,3, Joseph Romano^4,5, Marylyn Ritchie⁶

¹Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA. rachit.kumar@pennmedicine.upenn.edu.

Journal of Cheminformatics

|January 10, 2026

Summary

This review explores learnable protein representations for predicting drug-target affinity, examining data sources, training methods, and deep learning techniques. It offers insights to enhance drug development and assessment strategies.

Keywords:

Deep learning Graph neural networks Protein representation learning Structural biology

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Published on: December 1, 2020

Area of Science:

Computational Biology
Bioinformatics
Drug Discovery

Background:

Drug-target affinity prediction is crucial for drug development.
Effective protein representations are key to accurate predictions.
Existing methods vary in data sources, training, and encoding techniques.

Purpose of the Study:

To review learnable protein representations for drug-target affinity prediction.
To analyze different perspectives including data sources, training paradigms, and deep learning methods.
To provide insights for improving drug-target affinity prediction methods.

Main Methods:

Literature review of computational biology studies.
Analysis of protein representation types and their applications.
Focus on deep learning-based encoding and embedding methods.

Main Results:

Various learnable protein representations are utilized in computational biology.
Deep learning approaches are prominent for generating protein embeddings.
The choice of data source and training paradigm significantly impacts prediction accuracy.

Conclusions:

Protein representations are vital for advancing drug-target affinity prediction.
Further research into novel representations and training strategies can improve drug discovery pipelines.
This review serves as a resource for developing enhanced prediction models.