Molecular Manufacturing Made Easy with AI

AI-powered robots can help simplify time-consuming design and manufacturing tasks and help automate molecule manufacture to search for novel drugs and materials.

With its new revolutionary applications emerging in different areas of various industries, AI has penetrated almost every aspect of our lives. Despite everyone knowing the potential of AI, its applications at micro levels are quite fascinating. From nanobots to creating new protein structures, the use cases of AI at the molecular level is blowing the minds of people. One of the recent applications of AI at the molecular level is to automate molecule manufacture. Yes, you heard it right -- AI can free bench chemists from time-consuming research and help inspire the creation of new molecules. A team of researchers at Massachusetts Institue of Technology (MIT) has developed an AI-guided robotic system that can automate the manufacturing of small molecules that can be used in pharmacy, solar energy, and polymer chemistry.

How AI-Guided Robots Can Automate Molecule Manufacture

One of the significant challenges faced while automating molecule manufacturing is the diversity of molecule reactions and difficulty in finding a suitable reaction environment to support the synthesis of molecules. AI system promises to cut down the tedious parts of molecule building by automating end-to-end peptide manufacturing and molecule synthesis in three different steps.

The Steps Involved in Automate Molecule Synthesis

The AI-system uses three steps to achieve molecule manufacturing. The first step is the suggestion of a route or a way to build molecules. With the help of machine learning algorithms, the system suggests a route for synthesizing molecules. The software uses data of the previously published chemical reactions to create routes. Data like the structure of different molecules, their reaction to other molecules, and how they are created can help ML algorithms to design new molecules. Based on the data, the system can not only give new routes for molecules that we already know; it can also generalize new molecules that have never been made before.

The second step involves the review of the suggested route by chemical experts. The researchers can make changes to the suggestions by performing lab experiments with chemical reactions. Experts can then create a complete final formula for synthesizing molecules based on the suggestions. The formula can then be embedded into the robotic platform that creates the molecules.

The final step is loading the final formula on a robotic platform where robotic arms can create the final molecule structure. After loading the robotic platform with the formula, the robotic arms can assemble all the pieces of design in a continuous flow path with the molecular ingredients to create the molecule.

The diversity of the AI-system was tested by creating fifteen different medicinal molecules of varying complexity, ranging from simple drugs like aspirin to a complicated family of five ACE inhibitor drugs that can treat heart diseases. The system took around two hours to synthesis simple molecules to about sixty-eight hours for creating the complex ones. With advancements in AI technology to automate molecule manufacture, robotic systems might someday be able to synthesize even the most complex of molecules at a quicker pace. The increased speed for creating novel molecules and materials would allow the researchers to become more productive as they can quickly get an insight into the end result of any synthesized molecule, pushing the frontiers of materials science far beyond where they are today.