1. LiP-MS, a machine learning-based chemoproteomic approach to identify drug targets in complex proteomes. (October 2022) Authors: Redfern, D.; Beaton, N.; Sabino, F.; Below, C.; Bruderer, R.; Feng, Y.; Castaldi, P.; Reiter, L. Journal: European journal of cancer Issue: Volume 174(2022)Supplement 1 Page Start: S115 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
2. HR-LiP, a novel structural proteomics approach for the prediction of small molecule drug-protein binding events. (October 2022) Authors: Redfern, D.; Beaton, N.; Adhikari, J.; Bruderer, R.; Tomlinson, R.; Wrobel, L.; Hill, S.M.; Rubinsztein, D.C.; Feng, Y.; Cornella-Taracido, I.; Reiter, L. Journal: European journal of cancer Issue: Volume 174(2022)Supplement 1 Page Start: S104 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
3. High-Resolution Limited Proteolysis (HR-LIP): A novel approach for target validation and lead compound optimization. (October 2020) Authors: Feng, Y.; Beaton, N.; Adhikari, J.; Bruderer, R.; Tomlinson, R.; Cornella-Taracido, I.; Reiter, L. Journal: European journal of cancer Issue: Volume 138(2020)Supplement 2 Page Start: S39 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
4. LiP-Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes. (October 2020) Authors: Feng, Y.; Beaton, N.; Piazza, I.; Bruderer, R.; Picotti, P.; Reiter, L. Journal: European journal of cancer Issue: Volume 138(2020)Supplement 2 Page Start: S54 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗