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AIScaffold: A Web-Based Tool for Scaffold Diversification Using Deep Learning.

Junyong Lai¹, Xiangbin Li², Yanxing Wang¹

¹State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 100191 Beijing, P. R. China.

Journal of Chemical Information and Modeling

|December 28, 2020

Summary

This summary is machine-generated.

AIScaffold is a new web tool that uses deep learning for molecular scaffold diversification. It rapidly generates thousands of novel drug-like molecules to accelerate lead compound optimization in drug design.

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Area of Science:

Medicinal Chemistry
Computational Drug Design
Artificial Intelligence in Drug Discovery

Background:

Molecular scaffolds are fundamental building blocks in drug design.
Existing tools for scaffold diversification are limited, hindering medicinal chemists' workflow.
Efficient scaffold diversification is crucial for lead compound optimization.

Purpose of the Study:

To introduce AIScaffold, a novel web-based tool for rapid scaffold diversification.
To leverage deep generative models for large-scale molecule generation.
To provide medicinal chemists with an efficient tool to accelerate drug design.

Main Methods:

Development of AIScaffold, a web platform utilizing deep generative models.
Implementation of large-scale molecular diversification (up to 500,000 molecules).
Inclusion of site-specific diversification features.

Main Results:

AIScaffold performs large-scale diversification in minutes.
The tool recommends the top 500 (0.1%) diversified molecules.
Site-specific diversification capabilities are supported.

Conclusions:

AIScaffold significantly facilitates the scaffold diversification process.
The tool accelerates drug design by providing novel molecular candidates.
AIScaffold addresses the current lack of specialized tools for scaffold diversification.