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Updated: Feb 26, 2026

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

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

Published on: July 25, 2013

Biomimetic molecular design tools that learn, evolve, and adapt.

David A Winkler^1,2,3

¹CSIRO Manufacturing, Bayview Avenue, Clayton 3168, Australia.

Beilstein Journal of Organic Chemistry

|July 12, 2017

Summary

Biological systems learn and evolve. Biomimetic approaches use artificial intelligence and evolutionary algorithms for adaptive molecular design, impacting chemistry, engineering, and medicine.

Keywords:

automated chemical synthesis deep learning evolutionary algorithms in silico evolution machine learning materials design and development neural networks

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Area of Science:

Biomimetic chemistry and materials science.
Computational molecular design.
Artificial intelligence in scientific discovery.

Background:

Living systems exhibit remarkable adaptation through learning and evolution.
Biomimicry initially focused on synthesizing natural products.
Current research aims to replicate molecular machine principles and employ adaptive methods.

Purpose of the Study:

To describe new developments in biomimetic adaptive, evolving, and learning computational molecular design.
To explore the potential impacts of these methods in chemistry, engineering, and medicine.

Main Methods:

Convergence of automation, robotics, artificial intelligence, and evolutionary algorithms.
Development of in silico-based adaptive evolution of materials.
Application to organic chemistry for reaction systematization and self-optimizing synthesis systems.

Main Results:

Adaptive, evolving, machine learning-based molecular design methods are nearing rapid growth.
These methods show potential for disruptive impact across scientific disciplines.
Biomimetic approaches are advancing the design and optimization of molecular systems.

Conclusions:

Biomimetic adaptive evolution represents a significant advancement in molecular design.
These computational methods are poised to revolutionize chemistry, engineering, and medicine.
The integration of AI and evolutionary algorithms accelerates scientific innovation.