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Self driving chemical space exploration

Springer, Clayton and Gora, Jacob Self driving chemical space exploration.


We describe an algorithmic approach to lead optimization.
We take inspiration from geostatistics methods which consider the uncertainty in predictions as well as the predictions themselves. This leads to approach which has a concept of a chemical space. From a current dataset the
approach will suggest both large and small changes aimed finding chemical matter that is better than what is known.
This approach is also called 'active learning'.
We use data from SHP2 project, as a retrospective example to show how the approach works.

Item Type: Article
Date Deposited: 08 Jul 2022 00:45
Last Modified: 08 Jul 2022 00:45