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Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

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Compound dataset and custom code for deep generative multi-target compound design.

Thomas Blaschke¹, Jürgen Bajorath¹

¹Department of Life Science Informatics & Data Science, B-IT, LIMES Program Unit Chemical Biology & Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, Bonn, D-53113, Germany.

Future Science OA

|May 28, 2021

Summary

This summary is machine-generated.

Researchers developed a deep learning framework for designing multi-target compounds (MT-CPDs). This infrastructure, including software and a benchmark dataset, supports the advancement of novel drug discovery for complex diseases.

Keywords:

biological assays computer-aided drug design generative modeling large-scale data analysis machine learning multi-target compounds

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Area of Science:

Computational Chemistry
Drug Discovery
Machine Learning

Background:

Designing compounds that interact with multiple biological targets (multi-target compounds or MT-CPDs) is crucial for treating complex diseases.
Current methods for MT-CPD design face challenges in efficiency and scope.

Purpose of the Study:

To establish a data and software infrastructure for evaluating multi-target compound design using deep generative modeling.
To provide a foundation for advancing deep learning approaches in MT-CPD discovery.

Main Methods:

The REINVENT 2.0 generative modeling approach was adapted for MT-CPD design.
A comprehensive benchmark dataset was curated, including 2809 MT-CPDs, numerous single-target compounds, and inactive compounds from biological screens.

Main Results:

A proof-of-concept for deep generative MT-CPD design was successfully established.
Custom-developed software and the curated benchmark dataset are now freely available to the research community.

Conclusions:

Deep learning for MT-CPD design is in its early conceptual stages, requiring experimental validation.
The provided data and software infrastructure will facilitate further research and the generation of candidate molecules for experimental drug discovery programs.