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Preparation and Reactions of Thiols

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Thiols are prepared using the hydrosulfide anion as a nucleophile in a nucleophilic substitution reaction with alkyl halides. For instance, bromobutane reacts with sodium hydrosulfide to give butanethiol.

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Conformation
The simplest example of a diene is 1,3-butadiene, an acyclic conjugated π system. At room temperature, the molecule exists as a mixture of s-cis and s-trans conformers by virtue of rotation around the carbon–carbon single bond. Although the s-trans isomer is...

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Related Experiment Video

Updated: Jul 23, 2025

Optimization of the Ugi Reaction Using Parallel Synthesis and Automated Liquid Handling

Optimization of the Ugi Reaction Using Parallel Synthesis and Automated Liquid Handling

Published on: November 11, 2008

Bayesian Optimization-Assisted Screening to Identify Improved Reaction Conditions for Spiro-Dithiolane Synthesis.

Masaru Kondo^1,2, Hettiarachchige Dona Piyumi Wathsala³, Kazunori Ishikawa¹

¹SANKEN, Osaka University, Ibaraki-shi 567-0047, Japan.

Molecules (Basel, Switzerland)

|July 14, 2023

Summary

Bayesian optimization improved synthesis of 2,3,7,8-tetrathiaspiro[4.4]nonane (1), a key intermediate. This method efficiently identified optimal reaction conditions for large-scale production.

Keywords:

Bayesian optimization (BO)Gaussian process regression (GPR)Latin hypercube sampling (LHS)process chemistry random screening scalable synthesis spiro-dithiolane

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Area of Science:

Organic Synthesis
Chemical Process Development
Computational Chemistry

Background:

2,3,7,8-tetrathiaspiro[4.4]nonane (1) is a crucial intermediate for synthesizing tetramercaptan pentaerythritol.
Efficient and scalable synthesis routes are essential for industrial applications.

Purpose of the Study:

To optimize reaction conditions for the large-scale synthesis of compound 1 using Bayesian optimization.
To enhance the yield and efficiency of the synthetic process.

Main Methods:

Bayesian optimization (BO)-assisted screening was employed to identify optimal reaction parameters.
Initial training sets (ITS) were generated using random screening and Latin hypercube sampling (LHS).
The performance of BO was evaluated based on conversion yield within a limited number of experimental trials.

Main Results:

An initial BO-assisted screening with random sampling achieved a 78% conversion yield of compound 1 within seven experiments.
Subsequent BO-assisted screening utilizing an ITS selected by LHS further improved the yield to 89% within eight trials.
The optimized conditions proved suitable for hundred-gram scale-up synthesis.

Conclusions:

Bayesian optimization is an effective strategy for rapidly identifying improved reaction conditions in chemical synthesis.
LHS-selected ITS enhances the efficiency of BO-assisted screening for process optimization.
The developed methodology enables scalable and high-yield synthesis of key intermediate 1.