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Chemoinformatic approaches for navigating large chemical spaces.

¹Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany.

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|February 1, 2024

Summary

Exploring vast chemical spaces (CSs) requires tailored chemoinformatic strategies. Deep generative models offer promising, yet unverified, approaches for navigating these complex molecular landscapes efficiently.

Keywords:

Chemical space DNA-encoded chemical library chemical space navigation combinatorial chemistry deep generative models make-on-demand library molecular representation molecular similarity

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Area of Science:

Cheminformatics
Computational Chemistry
Drug Discovery

Background:

Large chemical spaces (CSs) encompass diverse molecular collections, from traditional libraries to virtual spaces explored by AI.
Navigating these vast CSs presents significant computational challenges due to their sheer size and complexity.

Purpose of the Study:

To provide an overview of different types of large chemical spaces.
To discuss suitable molecular representations and similarity metrics for large CS exploration.
To summarize navigation strategies within generative models for CSs.

Main Methods:

Review of existing chemoinformatic approaches for CS navigation.
Discussion of molecular representations and similarity metrics.
Analysis of navigation techniques employed by deep generative models.

Main Results:

Different types of large CSs necessitate distinct chemoinformatic navigation methods.
Efficient CS navigation requires scalable algorithms and intelligent molecule selection.
Deep generative models show potential for implicit feature learning but require experimental validation.

Conclusions:

The diversity of large chemical spaces demands specialized computational tools.
Deep generative models offer a promising avenue for efficient CS exploration, pending experimental validation.
Further research is needed to bridge the gap between virtual CS exploration and real-world chemical discovery.