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In 2017, the company developed an AI system for predicting chemical reactions in forward synthesis.
For instance,recurrent neural networks(RNN) and transformers can perform sequence-to-sequence mapping.
In 2019, the IBM Research team improved the AI behind RXN for Chemistry to also support retrosynthesis.
This is the inverse process of chemical synthesis.
In this case, you already know the molecular structure you want to achieve.
The AI must predict the series of steps and chemical components needed to reach the desired result.
IBM RXN for Chemistry also has the possibility to design retrosynthetic routes in an interactive mode.
Chemical synthesis becomes a human-AI interaction game, Laino says.
This model predicts reaction conditions (e.g.
temperature, duration) for the different actions (e.g.
add, stir, filter).
This is a key component in integrating the AI system with robo-labs, which expect distinct commands.
In all cases, the AI can choose between several predictions.
Tackling explainability issues
A pure neural networkbased approach comes with some benefits.
The AI models scale well with the availability of data.
And the system will benefit from all the research going into deep learning in general and transformers in particular.
But deep learning comes withinterpretabilitychallenges.
The scientists employing the system should be able to explore and correct the reasoning used by the AI system.
The researchers have published their findings intwo separatepaperspublished in the ChemRxiv preprint server.
The team has developed avisualization toolfor the RXN AI models and made it available online.
We saw the real potential in front of us.
I asked myself: Can an AI model drive an autonomous chemical lab?
After discussing the idea with the rest of the team, the idea of RoboRXN was conceived.
A user connected to the IBM Cloud app and provided a target molecule to RoboRXN.
The AI system processed the request and provided a suggestive instruction set for the experiment.
A live camera view allowed us to follow the steps as the robotic lab conducted the experiments.
The project is hardware agnostic.
Different types of hardware can be easily interfaced.
The team also envisions RoboRXN to scale and run parallel experiments.
Computational scientists can work remotely, accessing supercomputing resources available online.
We decided to provide the same at the level of a chemical lab.
A chemical laboratory accessible remotely, that is supervised by AI and executed by robotic chemical hardware.
RoboRXN can be considered for chemists what robotic vacuum cleaners are for humans.
Organizations can use the IBM Cloud to run RoboRXN and store the results obtained from the robotic labs.
Alternatively, they can have the entire system installed on-premise or in a private cloud.
IBM does not currently have plans to use data obtained from RoboRXN to finetune its AI models.
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