Cell-free protein synthesis with technical additives – expanding the parameter space of in vitro gene expression

• Tabea Bartsch,
• Stephan Lütz and
• Katrin Rosenthal

Beilstein J. Org. Chem. 2024, 20, 2242–2253, doi:10.3762/bjoc.20.192

• various options for soluble substances. Effects of technical additives on the CFPS performance In vitro sfGFP production with additives The in vitro expression of sfGFP, or GFP variants in general, is well established and is often used as a model system for optimization (e.g., with active learning

Finding the most potent compounds using active learning on molecular pairs

• Zachary Fralish and
• Daniel Reker

Beilstein J. Org. Chem. 2024, 20, 2152–2162, doi:10.3762/bjoc.20.185

• Zachary Fralish Daniel Reker Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA 10.3762/bjoc.20.185 Abstract Active learning allows algorithms to steer iterative experimentation to accelerate and de-risk molecular optimizations, but actively trained models might still

• from the current best training compound to prioritize further data acquisition. We apply the ActiveDelta concept to both graph-based deep (Chemprop) and tree-based (XGBoost) models during exploitative active learning for 99 Ki benchmarking datasets. We show that both ActiveDelta implementations excel

• at identifying more potent inhibitors compared to the standard exploitative active learning implementations of Chemprop, XGBoost, and Random Forest. The ActiveDelta approach is also able to identify more chemically diverse inhibitors in terms of their Murcko scaffolds. Finally, deep models such as