| Virtual screening—an overview WP Walters, MT Stahl, MA Murcko Drug discovery today 3 (4), 160-178, 1998 | 1424 | 1998 |
| Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins PS Charifson, JJ Corkery, MA Murcko, WP Walters Journal of medicinal chemistry 42 (25), 5100-5109, 1999 | 957 | 1999 |
| A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance E Perola, WP Walters, PS Charifson Proteins: Structure, Function, and Bioinformatics 56 (2), 235-249, 2004 | 614 | 2004 |
| Can we learn to distinguish between “drug-like” and “nondrug-like” molecules? Ajay, WP Walters, MA Murcko Journal of medicinal chemistry 41 (18), 3314-3324, 1998 | 565 | 1998 |
| Rethinking drug design in the artificial intelligence era P Schneider, WP Walters, AT Plowright, N Sieroka, J Listgarten, ... Nature Reviews Drug Discovery 19 (5), 353-364, 2020 | 563 | 2020 |
| Prediction of ‘drug-likeness’ WP Walters, MA Murcko Advanced drug delivery reviews 54 (3), 255-271, 2002 | 535 | 2002 |
| Designing screens: how to make your hits a hit WP Walters, M Namchuk Nature reviews Drug discovery 2 (4), 259-266, 2003 | 480 | 2003 |
| What do medicinal chemists actually make? A 50-year retrospective WP Walters, J Green, JR Weiss, MA Murcko Journal of medicinal chemistry 54 (19), 6405-6416, 2011 | 402 | 2011 |
| Recognizing molecules with drug-like properties WP Walters, AA Murcko, MA Murcko Current opinion in chemical biology 3 (4), 384-387, 1999 | 312 | 1999 |
| Going further than Lipinski's rule in drug design WP Walters Expert opinion on drug discovery 7 (2), 99-107, 2012 | 217 | 2012 |
| Axial Ligand Orientation in Iron (III) Porphyrinates: Effect of Axial. pi.-Acceptors. Characterization of the Low-Spin Complex [Fe (TPP)(4-CNPy) 2] ClO4 MK Safo, FA Walker, AM Raitsimring, WP Walters, DP Dolata, ... Journal of the American Chemical Society 116 (17), 7760-7770, 1994 | 216 | 1994 |
| D3R grand challenge 2015: evaluation of protein–ligand pose and affinity predictions S Gathiaka, S Liu, M Chiu, H Yang, JA Stuckey, YN Kang, J Delproposto, ... Journal of computer-aided molecular design 30, 651-668, 2016 | 211 | 2016 |
| Applications of deep learning in molecule generation and molecular property prediction WP Walters, R Barzilay Accounts of chemical research 54 (2), 263-270, 2020 | 201 | 2020 |
| D3R Grand Challenge 2: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies Z Gaieb, S Liu, S Gathiaka, M Chiu, H Yang, C Shao, VA Feher, ... Journal of computer-aided molecular design 32, 1-20, 2018 | 195 | 2018 |
| Acidic and basic drugs in medicinal chemistry: a perspective PS Charifson, WP Walters Journal of Medicinal Chemistry 57 (23), 9701-9717, 2014 | 195 | 2014 |
| Virtual chemical libraries: miniperspective WP Walters Journal of medicinal chemistry 62 (3), 1116-1124, 2018 | 192 | 2018 |
| Assessing the impact of generative AI on medicinal chemistry WP Walters, M Murcko Nature biotechnology 38 (2), 143-145, 2020 | 172 | 2020 |
| D3R Grand Challenge 3: blind prediction of protein–ligand poses and affinity rankings Z Gaieb, CD Parks, M Chiu, H Yang, C Shao, WP Walters, MH Lambert, ... Journal of computer-aided molecular design 33, 1-18, 2019 | 129 | 2019 |
| D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies CD Parks, Z Gaieb, M Chiu, H Yang, C Shao, WP Walters, JM Jansen, ... Journal of computer-aided molecular design 34, 99-119, 2020 | 107 | 2020 |
| Filtering databases and chemical libraries PS Charifson, WP Walters Journal of computer-aided molecular design 16 (5), 311-323, 2002 | 95 | 2002 |
