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ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm

Jia-Cai Yi^1,2, Zi-Yi Yang², Wen-Tao Zhao¹

¹School of Computer Science, National University of Defense Technology, Changsha 410073, Hunan, PR China.

Briefings in Bioinformatics

|February 22, 2024

Summary

This summary is machine-generated.

This study introduces ChemMORT, a platform for optimizing drug absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. ChemMORT enhances drug discovery by improving ADMET profiles without sacrificing efficacy.

Keywords:

ADMET evaluation deep learning inverse QSAR lead optimization particle swarm optimization reversible molecular representation substructure modification

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Area of Science:

Computational chemistry
Drug discovery and development
Medicinal chemistry

Background:

Drug discovery is expensive and time-consuming, with poor ADMET properties causing up to 50% of failures.
Optimizing multiple ADMET parameters is challenging due to vast chemical space and limited expert knowledge.

Purpose of the Study:

To develop a computational platform, ChemMORT, for optimizing multiple ADMET endpoints simultaneously.
To ensure that drug potency is maintained during ADMET property optimization.

Main Methods:

ChemMORT integrates three modules: SMILES Encoder, Descriptor Decoder, and Molecular Optimizer.
The SMILES Encoder generates 512-dimensional molecular vectors.
The Descriptor Decoder reconstructs molecular structures, and the Molecular Optimizer refines ADMET properties using particle swarm optimization and inverse QSAR principles.

Main Results:

ChemMORT effectively optimizes ADMET properties while preserving bioactivity.
The platform demonstrated utility in a case study involving poly (ADP-ribose) polymerase-1 inhibitors.

Conclusions:

ChemMORT offers a valuable tool for improving drug candidates' ADMET profiles.
This platform can accelerate drug discovery by addressing critical development challenges.