Representing high throughput expression profiles via perturbation barcodes reveals compound targets. - Tracey M. Filzen; Peter S. Kutchukian; Jeffrey D. Hermes; Jing Li; Matthew Tudor (PLoS Comput. Biol., 2017)

Is Multitask Deep Learning Practical for Pharma? - Bharath Ramsundar; Bowen Liu 014; Zhenqin Wu; Andreas Verras; Matthew Tudor; Robert P. Sheridan; Vijay S. Pande (J. Chem. Inf. Model., 2017)

Nearest Neighbor Gaussian Process for Quantitative Structure-Activity Relationships. - Anthony Difranzo; Robert P. Sheridan; Andy Liaw; Matthew Tudor (J. Chem. Inf. Model., 2020)

Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure-Activity Relationship Models? - Robert P. Sheridan; Prabha Karnachi; Matthew Tudor; Yuting Xu; Andy Liaw; Falgun Shah; Alan C. Cheng; Elizabeth Joshi; Meir Glick; Juan Alvarez (J. Chem. Inf. Model., 2020)

Augmenting Hit Identification by Virtual Screening Techniques in Small Molecule Drug Discovery. - Xin Cindy Yan; John M. Sanders; Ying-Duo Gao; Matthew Tudor; Andrew M. Haidle; Daniel J. Klein; Antonella Converso; Charles A. Lesburg; Yi Zang; Harold B. Wood (J. Chem. Inf. Model., 2020)

Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships. - Robert P. Sheridan; Andy Liaw; Matthew Tudor (CoRR, 2021)

Prediction Accuracy of Production ADMET Models as a Function of Version: Activity Cliffs Rule. - Robert P. Sheridan; J. Christopher Culberson; Elizabeth Joshi; Matthew Tudor; Prabha Karnachi (J. Chem. Inf. Model., 2022)