| scholarly article | Q13442814 |
| P356 | DOI | 10.1021/ACS.JCTC.0C01184 |
| P50 | author | David Baker | Q3814528 |
| Minkyung Baek | Q110951880 | ||
| Guangfeng Zhou | Q50911013 | ||
| Frank DiMaio | Q64868019 | ||
| Hahnbeom Park | Q87884662 | ||
| P2860 | cites work | Comparison of simple potential functions for simulating liquid water | Q26778447 |
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| An Empirical Polarizable Force Field Based on the Classical Drude Oscillator Model: Development History and Recent Applications | Q28834286 | ||
| Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 | Q28842789 | ||
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| Novel procedure for modeling ligand/receptor induced fit effects. | Q30352584 | ||
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| The Cambridge Structural Database in retrospect and prospect | Q30486586 | ||
| DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction | Q31007675 | ||
| Small molecule conformational preferences derived from crystal structure data. A medicinal chemistry focused analysis | Q31142361 | ||
| Conformation-family Monte Carlo: a new method for crystal structure prediction | Q33947687 | ||
| Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field | Q34459931 | ||
| Report on the sixth blind test of organic crystal structure prediction methods | Q34536112 | ||
| On achieving high accuracy and reliability in the calculation of relative protein–ligand binding affinities | Q35751099 | ||
| OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins | Q35846188 | ||
| Surflex-Dock: Docking benchmarks and real-world application | Q36098724 | ||
| Prediction of the structure of symmetrical protein assemblies | Q36140836 | ||
| Characterization of Biaryl Torsional Energetics and its Treatment in OPLS All-Atom Force Fields | Q36999047 | ||
| Facts and fictions about polymorphism. | Q38591930 | ||
| Can computed crystal energy landscapes help understand pharmaceutical solids? | Q38805031 | ||
| Overview of the SAMPL5 host-guest challenge: Are we doing better? | Q38818618 | ||
| The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. | Q38826829 | ||
| Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test. | Q39647348 | ||
| Application of Molecular Dynamics Simulations in Molecular Property Prediction I: Density and Heat of Vaporization. | Q41824906 | ||
| Automation of the CHARMM General Force Field (CGenFF) II: assignment of bonded parameters and partial atomic charges. | Q42184293 | ||
| Protein-ligand docking against non-native protein conformers | Q44921180 | ||
| Surflex-Dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search | Q46358770 | ||
| GalaxyDock BP2 score: a hybrid scoring function for accurate protein-ligand docking | Q47565870 | ||
| P275 | copyright license | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International | Q24082749 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P433 | issue | 3 | |
| P304 | page(s) | 2000-2010 | |
| P577 | publication date | 2021-02-12 | |
| P1433 | published in | Journal of Chemical Theory and Computation | Q1768377 |
| P1476 | title | Force Field Optimization Guided by Small Molecule Crystal Lattice Data Enables Consistent Sub-Angstrom Protein–Ligand Docking | |
| P478 | volume | 17 |
| Q112608595 | Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine |
| Q112579143 | Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins |
| Q112571283 | Rationalizing PROTAC-Mediated Ternary Complex Formation Using Rosetta |
| Q130476293 | Recent advances in de novo protein design: Principles, methods, and applications |
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