Profiling Prediction of Kinase Inhibitors: Toward the Virtual Assay.

By B. Merget, Samo Turk, Sameh Eid, Friedrich Rippmann, Simone Fulle. 2017

105 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Francesca Grisoni
2. Gisbert Schneider
3. José Jiménez-Luna
4. Nils Weskamp
5. Artem Cherkasov
6. Martin Ester
7. Tong He
8. Fuqiang Ban

In Molecular Biology

1. Francesca Grisoni
2. José Jiménez-Luna
3. Gisbert Schneider
4. Nils Weskamp
5. Artem Cherkasov
6. Martin Ester
7. Tong He
8. Fuqiang Ban

In Pharmacology

1. Francesca Grisoni
2. Gisbert Schneider
3. Artem Cherkasov

Citations in the corpus

• SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines (2017, background): “Merget et al. [26] evaluates different machine learning methods and data balancing schemes and reports that random forests yielded the best activity prediction and allowed accurate inference of compound selectivity.”
• Artificial intelligence in drug discovery: recent advances and future perspectives (2021, background): “Despite the promise of multitask learning, to date, only modest performance improvements over single-task models have been reported [43–46].”