| Overview of the SAMPL6 host-guest binding affinity prediction challenge |
34 |
| D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies |
31 |
| D3R Grand Challenge 3: blind prediction of protein-ligand poses and affinity rankings |
27 |
| Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges |
16 |
| Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2 |
12 |
| pK(a)measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments |
12 |
| SAMPL6 host-guest blind predictions using a non equilibrium alchemical approach |
11 |
| Biomolecular force fields: where have we been, where are we now, where do we need to go and how do we get there? |
10 |
| Predicting ligand binding affinity using on- and off-rates for the SAMPL6 SAMPLing challenge |
10 |
| High accuracy quantum-chemistry-based calculation and blind prediction of macroscopic pKa values in the context of the SAMPL6 challenge |
10 |
| The SAMPL6 challenge on predicting aqueous pK(a) values from EC-RISM theory |
9 |
| Convolutional neural network scoring and minimization in the D3R 2017 community challenge |
9 |
| Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen |
9 |
| Discovery and evaluation of novel Mycobacterium tuberculosis ketol-acid reductoisomerase inhibitors as therapeutic drug leads |
9 |
| Force field development phase II: Relaxation of physics-based criteria... or inclusion of more rigorous physics into the representation of molecular energetics |
8 |
| Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach |
8 |
| Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2 |
8 |
| Comparison of the umbrella sampling and the double decoupling method in binding free energy predictions for SAMPL6 octa-acid host-guest challenges |
8 |
| An explicit-solvent hybrid QM and MM approach for predicting pKa of small molecules in SAMPL6 challenge |
8 |
| Calculate protein-ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3 |
8 |
| Protein-ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3 |
7 |
| SAMPL6 host-guest challenge: binding free energies via a multistep approach |
7 |
| SAMPL6 challenge results from pK(a) predictions based on a general Gaussian process model |
7 |
| Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4 |
7 |
| WhichP450: a multi-class categorical model to predict the major metabolising CYP450 isoform for a compound |
7 |
| Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2 |
7 |
| Water molecules in protein-ligand interfaces. Evaluation of software tools and SAR comparison |
7 |
| HTMoL: full-stack solution for remote access, visualization, and analysis of molecular dynamics trajectory data |
6 |
| Touching proteins with virtual bare hands |
6 |
| Binding free energies in the SAMPL6 octa-acid host-guest challenge calculated with MM and QM methods |
5 |
| Detailed potential of mean force studies on host-guest systems from the SAMPL6 challenge |
5 |
| A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities |
5 |
| Absolute and relative pK(a) predictions via a DFT approach applied to the SAMPL6 blind challenge |
5 |
| Blinded evaluation of cathepsin S inhibitors from the D3RGC3 dataset using molecular docking and free energy calculations |
5 |
| Monte Carlo on the manifold and MD refinement for binding pose prediction of protein-ligand complexes: 2017 D3R Grand Challenge |
5 |
| Protein-ligand docking using FFT based sampling: D3R case study |
5 |
| Binding affinities of the farnesoid X receptor in the D3R Grand Challenge 2 estimated by free-energy perturbation and docking |
5 |
| Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP+ |
5 |
| Rescoring of docking poses under Occam's Razor: are there simpler solutions? |
5 |
| Demystifying the pH dependent conformational changes of human heparanase pertaining to structure-function relationships: an in silico approach |
5 |
| Evaluating the performance of MM/PBSA for binding affinity prediction using class A GPCR crystal structures |
5 |
| Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2 |
5 |
| Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2 |
5 |
| Multi-task generative topographic mapping in virtual screening |
5 |
| Assessing and improving the performance of consensus docking strategies using the DockBox package |
4 |
| Electrostatic-field and surface-shape similarity for virtual screening and pose prediction |
4 |
| Insight into the molecular mechanism of yeast acetyl-coenzyme A carboxylase mutants F510I, N485G, I69E, E477R, and K73R resistant to soraphen A |
4 |
| Discovery of a nanomolar inhibitor of the human glyoxalase-I enzyme using structure-based poly-pharmacophore modelling and molecular docking |
4 |
| Disruptor of telomeric silencing 1-like (DOT1L): disclosing a new class of non-nucleoside inhibitors by means of ligand-based and structure-based approaches |
4 |
| Assessment of tautomer distribution using the condensed reaction graph approach |
4 |
