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h5 指数:34
h5 中位数:46

h5 指数是指在过去整整 5 年中所发表文章的 h 指数。h 指在 2018-2022 年间发表的 h 篇文章每篇至少都被引用过 h 次的最大值。隐藏

出版物的 h5 中位数，是指出版物的 h5 指数所涵盖的所有文章获得的引用次数的中位值。隐藏

#20 Medicinal Chemistry

标题/作者	引用次数	年份
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions R Rodríguez-Pérez, J Bajorath Journal of Computer-Aided Molecular Design 34, 1013-1026	207	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	179	2018
Overview of the SAMPL6 host–guest binding affinity prediction challenge A Rizzi, S Murkli, JN McNeill, W Yao, M Sullivan, MK Gilson, MW Chiu, ... Journal of Computer-Aided Molecular Design 32 (10), 937-963	122	2018
Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges DD Nguyen, Z Cang, K Wu, M Wang, Y Cao, GW Wei Journal of Computer-Aided Molecular Design 33, 71-82	117	2019
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	110	2019
Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2 Z Kurkcuoglu, PI Koukos, N Citro, ME Trellet, J Rodrigues, IS Moreira, ... Journal of Computer-Aided Molecular Design 32, 175-185	106	2018
The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations A Rizzi, T Jensen, DR Slochower, M Aldeghi, V Gapsys, D Ntekoumes, ... Journal of Computer-Aided Molecular Design 34 (5), 601-633	97	2020
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	80	2020
Artificial intelligence in chemistry and drug design N Brown, P Ertl, R Lewis, T Luksch, D Reker, N Schneider Journal of Computer-Aided Molecular Design 34, 709-715	78	2020
Biomolecular force fields: where have we been, where are we now, where do we need to go and how do we get there? P Dauber-Osguthorpe, AT Hagler Journal of Computer-Aided Molecular Design 33 (2), 133-203	77	2019
MathDL: mathematical deep learning for D3R Grand Challenge 4 DD Nguyen, K Gao, M Wang, GW Wei Journal of Computer-Aided Molecular Design 34, 131-147	70	2020
Force field development phase II: Relaxation of physics-based criteria… or inclusion of more rigorous physics into the representation of molecular energetics AT Hagler Journal of Computer-Aided Molecular Design 33 (2), 205-264	56	2019
Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation P Charoenkwan, C Nantasenamat, MM Hasan, W Shoombuatong Journal of Computer-Aided Molecular Design 34, 1105-1116	53	2020
Assessing the accuracy of octanol–water partition coefficient predictions in the SAMPL6 Part II log P Challenge M Işık, TD Bergazin, T Fox, A Rizzi, JD Chodera, DL Mobley Journal of Computer-Aided Molecular Design 34 (4), 335-370	50	2020
Convolutional neural network scoring and minimization in the D3R 2017 community challenge J Sunseri, JE King, PG Francoeur, DR Koes Journal of Computer-Aided Molecular Design 33, 19-34	50	2019
Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction MC Robinson, RC Glen, AA Lee Journal of Computer-Aided Molecular Design 34, 717-730	48	2020
Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach PCH Lam, R Abagyan, M Totrov Journal of Computer-Aided Molecular Design 32, 187-198	46	2018
Supervised molecular dynamics for exploring the druggability of the SARS-CoV-2 spike protein G Deganutti, F Prischi, CA Reynolds Journal of Computer-Aided Molecular Design 35, 195-207	46	2021
Octanol–water partition coefficient measurements for the SAMPL6 blind prediction challenge M Işık, D Levorse, DL Mobley, T Rhodes, JD Chodera Journal of Computer-Aided Molecular Design 34 (4), 405-420	45	2020
Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity S Jain, E Kotsampasakou, GF Ecker Journal of Computer-Aided Molecular Design 32, 583-590	44	2018

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