Autonomous, multi-property-driven molecular discovery: from predictions to measurements and back

[agitter]

Autonomous, multi-property-driven molecular discovery: from predictions to measurements and back https://doi.org/10.26434/chemrxiv-2023-r7b01

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. Two case studies are demonstrated on dye-like molecules, targeting absorption wavelength, lipophilicity, and photo-oxidative stability. In the first, the platform experimentally realized 312 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure–function space of four rarely reported scaffolds. In each iteration, the property-prediction models which guided the exploration learned the structure–property space of diverse inexpensive scaffold derivatives realized through using multi-step syntheses. Conversely, the second study exploited property models trained on a chemical space with pre-existing examples to discover 6 top-performing molecules within the structure-property space. By closing the molecular discovery cycle of prediction, synthesis, measurement, and model retraining, the platform demonstrates the potential for integrated platforms to automatically understand a local chemical space and discover functional molecules.

This is a relevant example of academic research, but I didn't read it carefully enough to submit a pull request and complete the emoji categorization.

[sgbaird]

@agitter thanks! No worries, I'll try to take a look and check. Do you mind opening a PR and leaving the other content blank?

[agitter]

Are you following a particular style for the references? I formatted the first one manually but this one has more authors.

[sgbaird]

@agitter not too particular. General format:

https://github.com/sgbaird/awesome-self-driving-labs/blob/98e36ef0c3a6baf9c99d7ddc2dffca858332d99e/readme.md?plain=1#L49