Exploiting machine learning for end-to-end drug discovery and development S Ekins, AC Puhl, KM Zorn, TR Lane, DP Russo, JJ Klein, AJ Hickey, ... Nature materials 18 (5), 435-441, 2019 | 487 | 2019 |
2D depiction of protein− ligand complexes AM Clark, P Labute Journal of chemical information and modeling 47 (5), 1933-1944, 2007 | 173 | 2007 |
Comparing multiple machine learning algorithms and metrics for estrogen receptor binding prediction DP Russo, KM Zorn, AM Clark, H Zhu, S Ekins Molecular pharmaceutics 15 (10), 4361-4370, 2018 | 149 | 2018 |
Open source Bayesian models. 1. Application to ADME/Tox and drug discovery datasets AM Clark, K Dole, A Coulon-Spektor, A McNutt, G Grass, JS Freundlich, ... Journal of chemical information and modeling 55 (6), 1231-1245, 2015 | 122 | 2015 |
Electrophilic substitution reactions at the phenyl ring of the chelated 2-(2 ‘-Pyridyl) phenyl ligand bound to Ruthenium (II) or Osmium (II) AM Clark, CEF Rickard, WR Roper, LJ Wright Organometallics 18 (15), 2813-2820, 1999 | 107 | 1999 |
Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery T Lane, DP Russo, KM Zorn, AM Clark, A Korotcov, V Tkachenko, ... Molecular pharmaceutics 15 (10), 4346-4360, 2018 | 98 | 2018 |
Machine learning models identify molecules active against the Ebola virus in vitro S Ekins, JS Freundlich, AM Clark, M Anantpadma, RA Davey, P Madrid F1000Research 4, 2015 | 94 | 2015 |
Open source Bayesian models. 2. Mining a “big dataset” to create and validate models with ChEMBL AM Clark, S Ekins Journal of chemical information and modeling 55 (6), 1246-1260, 2015 | 92 | 2015 |
Assessment of substrate-dependent ligand interactions at the organic cation transporter OCT2 using six model substrates PJ Sandoval, KM Zorn, AM Clark, S Ekins, SH Wright Molecular pharmacology 94 (3), 1057-1068, 2018 | 90 | 2018 |
2D structure depiction AM Clark, P Labute, M Santavy Journal of chemical information and modeling 46 (3), 1107-1123, 2006 | 80 | 2006 |
Mobile apps for chemistry in the world of drug discovery AJ Williams, S Ekins, AM Clark, JJ Jack, RL Apodaca Drug Discovery Today, 2011 | 66 | 2011 |
Detection and assignment of common scaffolds in project databases of lead molecules AM Clark, P Labute Journal of medicinal chemistry 52 (2), 469-483, 2009 | 61 | 2009 |
Multiple machine learning comparisons of HIV cell-based and reverse transcriptase data sets KM Zorn, TR Lane, DP Russo, AM Clark, V Makarov, S Ekins Molecular pharmaceutics 16 (4), 1620-1632, 2019 | 57 | 2019 |
Ebola virus Bayesian machine learning models enable new in vitro leads M Anantpadma, T Lane, KM Zorn, MA Lingerfelt, AM Clark, JS Freundlich, ... ACS omega 4 (1), 2353-2361, 2019 | 53 | 2019 |
Incorporating green chemistry concepts into mobile chemistry applications and their potential uses S Ekins, AM Clark, AJ Williams ACS Sustainable Chemistry & Engineering 1 (1), 8-13, 2013 | 52 | 2013 |
Flexible 3D pharmacophores as descriptors of dynamic biological space JH Nettles, JL Jenkins, C Williams, AM Clark, A Bender, Z Deng, ... Journal of Molecular Graphics and Modelling 26 (3), 622-633, 2007 | 52 | 2007 |
Bromination and nitration reactions of metallated (Ru and Os) multiaromatic ligands and crystal structures of selected products AM Clark, CEF Rickard, WR Roper, LJ Wright Journal of Organometallic Chemistry 598 (2), 262-275, 2000 | 51 | 2000 |
Looking Back to the Future: Predicting in Vivo Efficacy of Small Molecules versus Mycobacterium tuberculosis S Ekins, R Pottorf, RC Reynolds, AJ Williams, AM Clark, JS Freundlich Journal of chemical information and modeling 54 (4), 1070-1082, 2014 | 46 | 2014 |
Four disruptive strategies for removing drug discovery bottlenecks S Ekins, CL Waller, MP Bradley, AM Clark, AJ Williams Drug Discovery Today 18 (5-6), 265-271, 2013 | 46 | 2013 |
New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0 AM Clark, M Sarker, S Ekins Journal of cheminformatics 6, 1-17, 2014 | 45 | 2014 |