Solubility Challenge: Can You Predict Solubilities of 32 Molecules Using a Database of 100 Reliable Measurements? - Antonio Llinàs; Robert C. Glen; Jonathan M. Goodman (J. Chem. Inf. Model., 2008)

Findings of the Challenge To Predict Aqueous Solubility. - Anton J. Hopfinger; Emilio Xavier Esposito; Antonio Llinàs; Robert C. Glen; Jonathan M. Goodman (J. Chem. Inf. Model., 2009)

Solubility Challenge Revisited after Ten Years, with Multilab Shake-Flask Data, Using Tight (SD ∼ 0.17 log) and Loose (SD ∼ 0.62 log) Test Sets. - Antonio Llinàs; Alex Avdeef (J. Chem. Inf. Model., 2019)

Findings of the Second Challenge to Predict Aqueous Solubility. - Antonio Llinàs; Ioana Oprisiu; Alex Avdeef (J. Chem. Inf. Model., 2020)

Multisolvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors. - Vigneshwari Subramanian; Ekaterina Ratkova; David Palmer 002; Ola Engkvist; Maxim V. Fedorov; Antonio Llinàs (J. Chem. Inf. Model., 2020)

Membrane Permeability in Cyclic Peptides is Modulated by Core Conformations. - Flaviu S. Cipcigan; Paul Smith; Jason Crain; Anders Hogner; Leonardo De Maria; Antonio Llinàs; Ekaterina Ratkova (J. Chem. Inf. Model., 2021)

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models. - Jonathan G. M. Conn; James W. Carter; Justin J. A. Conn; Vigneshwari Subramanian; Andrew Baxter; Ola Engkvist; Antonio Llinàs; Ekaterina Ratkova; Stephen D. Pickett; James L. McDonagh; David S. Palmer (J. Chem. Inf. Model., 2023)