Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning

• Pablo Quijano Velasco,
• Kedar Hippalgaonkar and
• Balamurugan Ramalingam

Beilstein J. Org. Chem. 2025, 21, 10–38, doi:10.3762/bjoc.21.3

Biomimetic molecular design tools that learn, evolve, and adapt

• David A Winkler

Beilstein J. Org. Chem. 2017, 13, 1288–1302, doi:10.3762/bjoc.13.125

• , medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis

• methods and their potential impacts in chemistry, engineering, and medicine. Keywords: automated chemical synthesis; deep learning; evolutionary algorithms; in silico evolution; machine learning; materials design and development; neural networks; Introduction There is still not a clear understanding of

• of evolutionary algorithms to explore materials space more quickly and effectively than other methods. When coupled with learning algorithms, in silico evolutionary adaptation is possible, as we now describe. Evolving materials for the future The development and application of evolutionary methods