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Rapid, Enzymatic Methods for Amplification of Minimal, Linear Templates for Protein Prototyping using Cell-Free Systems

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ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.

Gregory W Kyro¹, Anton Morgunov¹, Rafael I Brent¹

¹Yale University.

|September 25, 2023

Summary

This study introduces an efficient active learning method for generative artificial intelligence in drug discovery. The approach successfully generates novel molecules, even reproducing existing inhibitors for specific protein targets.

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Area of Science:

Computational chemistry
Artificial intelligence
Drug discovery

Background:

Generative artificial intelligence (AI) models are increasingly applied to drug discovery.
Exploring vast chemical spaces requires efficient methods for identifying molecules with desired properties.

Conclusions:

The developed active learning methodology is effective for targeted molecular generation using generative AI.
The approach is generalizable and applicable to various protein targets, including those without existing inhibitors.
The open-source ChemSpaceAL Python package facilitates implementation and reproducibility of this method.