| 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 | 146 | 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 | 123 | 2020 |
| Molecular dynamics electric field crystallization simulations of paracetamol produce a new polymorph C Parks, A Koswara, HH Tung, N Nere, S Bordawekar, ZK Nagy, ... Crystal Growth & Design 17 (7), 3751-3765, 2017 | 27 | 2017 |
| Solubility curves and nucleation rates from molecular dynamics for polymorph prediction – moving beyond lattice energy minimization C Parks, A Koswara, F DeVilbiss, HH Tung, NK Nere, S Bordawekar, ... Physical Chemistry Chemical Physics, 2017 | 27 | 2017 |
| Nanocrystal dissolution kinetics and solubility increase prediction from molecular dynamics: The case of α-, β-, and γ-glycine C Parks, A Koswara, HH Tung, NK Nere, S Bordawekar, ZK Nagy, ... Molecular Pharmaceutics 14 (4), 1023-1032, 2017 | 26 | 2017 |
| An analysis of proteochemometric and conformal prediction machine learning protein-ligand binding affinity models C Parks, Z Gaieb, RE Amaro Frontiers in molecular biosciences 7, 93, 2020 | 16 | 2020 |
| Exploring new crystal structures of glycine via electric field-induced structural transformations with molecular dynamics simulations PS Bulutoglu, C Parks, NK Nere, S Bordawekar, D Ramkrishna Processes 7 (5), 268, 2019 | 10 | 2019 |
| Accelerating multiple replica molecular dynamics simulations using the Intel® Xeon Phi™ coprocessor C Parks, L Huang, Y Wang, D Ramkrishna Molecular Simulation 43 (9), 714-723, 2017 | 6 | 2017 |
| Extending the CrystalLandscape through Electric Field Controlled Crystallization –a Molecular Dynamics Case Study C Parks, A Koswara, HH Tung, N Nere, S Bordawekar, ZK Nagy, ... ChemRxiv, 2018 | 4 | 2018 |
| Mining for potent inhibitors through artificial intelligence and physics: a unified methodology for ligand based and structure based drug design J Li, O Zhang, K Sun, Y Wang, X Guan, D Bagni, M Haghighatlari, ... Journal of Chemical Information and Modeling 64 (24), 9082-9097, 2024 | 2 | 2024 |
| Systems and methods for producing a chemical product A Koswara, ZK Nagy, CD Parks US Patent 10,751,685, 2020 | 2 | 2020 |
| Evaluation of Binding Site Comparison Algorithms and Proteometric Machine Learning Models in the Detection of Protein Pockets Capable of Binding the Same Ligand Z Gaieb, C Parks, R Amaro ChemRxiv preprint ChemRxiv 9178136, 2019 | 1 | 2019 |
| Molecular dynamics investigation of solid state form prediction, selection, and control: towards application to crystallization CD Parks Purdue University, 2017 | 1 | 2017 |
| Reinforcement Learning with Real-time Docking of 3D Structures to Cover Chemical Space: Mining for Potent SARS-CoV-2 Main Protease Inhibitors J Li, O Zhang, FL Kearns, M Haghighatlari, C Parks, X Guan, I Leven, ... arXiv preprint arXiv:2110.01806, 2021 | | 2021 |
| Machine Learning for Acute Oral System Toxicity Regression and Classification Z Gaieb, R Amaro | | 2019 |
| Machine learning for hit discovery: Recent work in virtual screening and de novo drug design R Amaro, C Parks, Z Gaieb ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 258, 2019 | | 2019 |
| Virtual screening and de novo drug design with machine learning C Parks, Z Gaieb, R Amaro ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 258, 2019 | | 2019 |
| D3R Grand Challenge 4: Blind prediction of protein-ligand poses and affinity predictions Z Gaieb, C Parks, M Chiu, H Yang, C Shao, P Walters, R Lewis, ... ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 258, 2019 | | 2019 |
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