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On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach.

Sangsoo Lim¹, Sangseon Lee², Yinhua Piao³

¹Bioinformatics Institute, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea.

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|September 2, 2022

Summary

This summary is machine-generated.

Deep learning models can navigate vast chemical space for drug discovery by utilizing compound data. This survey explores how these technologies model chemical information and integrate diverse datasets to enhance predictions for novel therapeutics.

Keywords:

Chemical information modeling Chemical space Computer-aided drug discovery Data augmentation Deep learning

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Area of Science:

Computational chemistry
cheminformatics
drug discovery

Background:

Vast chemical space exists beyond known compounds in databases like PubChem and ZINC.
Annotated properties and assay data are crucial for drug discovery efforts.
Machine learning, particularly deep learning, shows promise in navigating chemical space.

Purpose of the Study:

To survey how deep learning technologies model and utilize chemical compound information for drug-related tasks.
To explore methods for addressing data insufficiency in deep learning for chemical space navigation.
To identify complementary data types that enhance deep learning model prediction power.

Main Methods:

Compilation of machine learning tasks in drug discovery.
Survey of deep learning technologies and their applications in drug-related tasks.
Review of deep learning techniques for handling limited annotated data.
Investigation of auxiliary data sources (e.g., assay, gene expression) for model improvement.

Main Results:

Deep learning effectively models chemical compound information for various drug discovery tasks.
Techniques exist to mitigate data scarcity challenges in deep learning for chemical space exploration.
Integrating diverse data types significantly boosts the predictive performance of deep learning models.

Conclusions:

Deep learning offers powerful tools for exploring chemical space and accelerating drug discovery.
Addressing data limitations and integrating multi-modal data are key for maximizing deep learning's potential.
Emerging technologies promise further advancements in computational chemical analysis.