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Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

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MOLER: Incorporate Molecule-Level Reward to Enhance Deep Generative Model for Molecule Optimization.

Tianfan Fu¹, Cao Xiao², Lucas M Glass³

¹Department of Computer Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.

IEEE Transactions on Knowledge and Data Engineering

|January 2, 2023

Summary

Molecule-Level Reward functions (MOLER) improve molecular optimization by ensuring generated molecules resemble the input in size and structure. This deep learning approach enhances chemical properties and success rates for drug discovery.

Keywords:

Automatic molecule optimization drug discovery generative models molecule generation

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Interactive Molecular Model Assembly with 3D Printing

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Published on: August 13, 2020

Area of Science:

Computational chemistry
Artificial intelligence in drug discovery

Background:

Deep generative models are successful in molecular optimization but often produce molecules with undesirable deviations in similarity and size.
Current models focus on substructure generation, neglecting the global molecular context.

Purpose of the Study:

To introduce Molecule-Level Reward functions (MOLER) for improved molecular optimization.
To ensure generated molecules maintain similarity and comparable size to the input molecule.

Main Methods:

MOLER integrates molecule-level similarity and size constraints into deep generative models.
Policy gradient techniques are employed for efficient optimization of reward-based objectives.
The method is designed to be compatible with various deep generative architectures.

Main Results:

MOLER demonstrated significant improvements in molecular optimization tasks.
Achieved up to 20.2% relative improvement in success rate compared to baseline methods.
Successfully enhanced properties such as Quantitative Estimate of Drug-likeness (QED), D2 receptor affinity (DRD2), and LogP.

Conclusions:

MOLER effectively addresses the limitations of substructure-level generation in deep molecular optimization.
The proposed approach enhances molecular similarity and size control, leading to better chemical properties.
MOLER offers a computationally efficient and versatile method for advancing drug discovery and molecular design.