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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

Bayesian molecular design with a chemical language model.

Hisaki Ikebata¹, Kenta Hongo^2,3,4, Tetsu Isomura⁵

¹The Graduate University for Advanced Studies (SOKENDAI), Tachikawa, Japan.

Journal of Computer-Aided Molecular Design

|March 11, 2017

Summary

This summary is machine-generated.

This study accelerates molecular design using machine learning. It enables the creation of novel molecules with desired properties by combining forward and backward predictions and a chemical language model.

Keywords:

Bayesian analysis Inverse-QSPR Molecular design Natural language processing SMILES Small organic molecules

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Area of Science:

Computational chemistry
Materials science
Machine learning

Background:

Accelerating the discovery of novel molecules with specific properties is crucial for material science.
Traditional computational methods can be time-consuming and may not efficiently explore the vast chemical space.

Purpose of the Study:

To develop and demonstrate a machine learning-based framework for accelerating computational molecular design.
To enable the identification of hypothetical molecules with predefined properties, such as specific HOMO-LUMO gaps and internal energies.

Main Methods:

Utilizing both forward and backward prediction models based on machine learning.
Employing Bayes' law to invert forward models for backward prediction, conditioned on desired properties.
Integrating a chemical language model, developed using natural language processing on SMILES strings, to ensure chemical validity and refine molecular structures.
Applying sequential Monte Carlo techniques to explore high-probability regions of the posterior distribution.

Main Results:

Successfully designed small organic molecules meeting specified property requirements (HOMO-LUMO gap and internal energy).
Demonstrated the exclusion of chemically unfavorable structures through the chemical language model.
The developed method effectively refines chemical strings to achieve desired properties.

Conclusions:

The proposed machine learning approach significantly accelerates the discovery of novel molecules with targeted properties.
The integration of a chemical language model is effective in ensuring the chemical plausibility of designed molecules.
The R package iqspr is available for broader application of this computational molecular design method.