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Related Experiment Video

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Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

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Published on: February 25, 2021

Machine learning-assisted search for novel coagulants: When machine learning can be efficient even if data

Andrij Rovenchak^1,2, Maksym Druchok^1,3

¹SoftServe, Inc., Lviv, Ukraine.

Journal of Computational Chemistry

|January 4, 2024

Summary

This study introduces a deep learning autoencoder to generate novel drug candidates. The model effectively represents chemical space, aiding in the discovery of new coagulants and predicting anticoagulant activity.

Keywords:

anticoagulants coagulants machine learning molecular design

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Area of Science:

Drug discovery and development
Computational chemistry
Machine learning in pharmacology

Background:

Designing effective drug candidates requires precise target interaction with minimal side effects.
Data-driven approaches are increasingly important, but limited experimental data for specific targets poses a challenge.
Existing methods struggle with targets like Protein C (coagulants) due to data scarcity, unlike Thrombin (anticoagulants).

Purpose of the Study:

To develop a novel deep learning approach for generating potential drug candidates.
To effectively represent and navigate chemical space for drug discovery.
To identify new inhibitors for targets with limited known compounds, such as Protein C.

Main Methods:

Developed a deep learning autoencoder model trained on a large dataset of molecules in SMILES format.
Utilized the autoencoder to map chemical space and generate novel molecular structures.
Applied sampling strategies to propose new coagulant candidates and tested on anticoagulant prediction.

Main Results:

Successfully generated novel coagulant candidates by navigating the chemical space.
Demonstrated the model's ability to predict Thrombin inhibition for anticoagulant candidates.
Compared the autoencoder approach with MegaMolBART, another deep learning generative model.

Conclusions:

The developed autoencoder provides an effective method for representing chemical space and generating novel drug candidates.
This approach shows promise for discovering inhibitors for targets with limited experimental data.
The study highlights the potential of deep learning for accelerating drug discovery, particularly for under-explored targets.