| How frequently are pan-assay interference compounds active? Large-scale analysis of screening data reveals diverse activity profiles, low global hit frequency, and many … S Jasial, Y Hu, J Bajorath Journal of medicinal chemistry 60 (9), 3879-3886, 2017 | 124 | 2017 |
| Activity-relevant similarity values for fingerprints and implications for similarity searching S Jasial, Y Hu, M Vogt, J Bajorath F1000Research 5, 2016 | 73 | 2016 |
| Activity-relevant similarity values for fingerprints and implications for similarity searching S Jasial, Y Hu, M Vogt, J Bajorath F1000Research 5, 2016 | 73 | 2016 |
| Determining the degree of promiscuity of extensively assayed compounds S Jasial, Y Hu, J Bajorath PLoS One 11 (4), e0153873, 2016 | 50 | 2016 |
| Highly promiscuous small molecules from biological screening assays include many pan-assay interference compounds but also candidates for polypharmacology E Gilberg, S Jasial, D Stumpfe, D Dimova, J Bajorath Journal of Medicinal Chemistry 59 (22), 10285-10290, 2016 | 47 | 2016 |
| Prediction of compound profiling matrices using machine learning R Rodríguez-Pérez, T Miyao, S Jasial, M Vogt, J Bajorath ACS omega 3 (4), 4713-4723, 2018 | 38 | 2018 |
| Machine learning distinguishes with high accuracy between pan-assay interference compounds that are promiscuous or represent dark chemical matter S Jasial, E Gilberg, T Blaschke, J Bajorath Journal of Medicinal Chemistry 61 (22), 10255-10264, 2018 | 32 | 2018 |
| Assessing the growth of bioactive compounds and scaffolds over time: implications for lead discovery and scaffold hopping S Jasial, Y Hu, J Bajorath Journal of chemical information and modeling 56 (2), 300-307, 2016 | 18 | 2016 |
| Comparing predictive ability of QSAR/QSPR models using 2D and 3D molecular representations A Sato, T Miyao, S Jasial, K Funatsu Journal of Computer-Aided Molecular Design 35, 179-193, 2021 | 16 | 2021 |
| Extracting compound profiling matrices from screening data M Vogt, S Jasial, J Bajorath Acs Omega 3 (4), 4706-4712, 2018 | 14 | 2018 |
| Structure-promiscuity relationship puzzles—extensively assayed analogs with large differences in target annotations Y Hu, S Jasial, E Gilberg, J Bajorath The AAPS journal 19, 856-864, 2017 | 14 | 2017 |
| Sparse topological pharmacophore graphs for interpretable scaffold hopping H Nakano, T Miyao, J Swarit, K Funatsu Journal of Chemical Information and Modeling 61 (7), 3348-3360, 2021 | 8 | 2021 |
| Activity-relevant similarity values for fingerprints and implications for similarity searching. F1000Research 5 S Jasial, Y Hu, M Vogt, J Bajorath Chem Inf Sci 591, 2016 | 8 | 2016 |
| Promiscuity progression of bioactive compounds over time Y Hu, S Jasial, J Bajorath F1000Research 4 (Chem Inf Sci), 2015 | 8 | 2015 |
| Interpretation of ligand-based activity cliff prediction models using the matched molecular pair kernel S Tamura, S Jasial, T Miyao, K Funatsu Molecules 26 (16), 4916, 2021 | 7 | 2021 |
| Dark chemical matter in public screening assays and derivation of target hypotheses S Jasial, J Bajorath MedChemComm 8 (11), 2100-2104, 2017 | 7 | 2017 |
| Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships T Miyao, S Jasial, J Bajorath, K Funatsu Journal of Computer-Aided Molecular Design 33 (8), 729-743, 2019 | 5 | 2019 |
| Chemometrics Approach Based on Wavelet Transforms for the Estimation of Monomer Concentrations from FTIR Spectra A Wakiuchi, S Jasial, S Asano, R Hashizume, M Hatanaka, Y Ohnishi, ... ACS omega 8 (22), 19781-19788, 2023 | 2 | 2023 |
| Screening and Validation of Odorants against Influenza A Virus Using Interpretable Regression Models S Jasial, J Hu, T Miyao, Y Hirama, S Onishi, R Matsui, K Osaki, K Funatsu ACS Pharmacology & Translational Science 6 (1), 139-150, 2022 | 1 | 2022 |
| Computationally derived compound profiling matrices M Vogt, S Jasial, J Bajorath Future Science OA 4 (8), FSO327, 2018 | 1 | 2018 |
