Name
Title | ||
|---|---|---|
Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3. |
Abstract | ||
|---|---|---|
In context of D3R Grand Challenge 3 we have investigated several ligand activity prediction protocols that combined elements of a physics-based energy function (ICM VLS score) and the knowledge-based Atomic Property Field 3D QSAR approach. Activity prediction models utilized poses produced by ICM-Dock with ligand bias and 4D receptor conformational ensembles (LigBEnD). Hybrid APF/P (APF/Physics) models were superior to pure physics- or knowledge-based models in our preliminary tests using rigorous three-fold clustered cross-validation and later proved successful in the blind prediction for D3R GC3 sets, consistently performing well across four different targets. The results demonstrate that knowledge-based and physics-based inputs into the machine-learning activity model can be non-redundant and synergistic. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10822-018-0139-5 | Journal of computer-aided molecular design |
Keywords | Field | DocType |
D3R,D3R GC3,ICM,APF,3D QSAR,Docking,Computer-aided drug design | Quantitative structure–activity relationship,Docking (dog),Ligand,Computational chemistry,Chemistry,Conformational ensembles,Computational biology | Journal |
Volume | Issue | ISSN |
33.0 | SP1.0 | 1573-4951 |
Citations | PageRank | References |
1 | 0.35 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Polo C-H Lam | 1 | 1 | 0.35 |
| Ruben Abagyan | 2 | 430 | 55.44 |
| Maxim Totrov | 3 | 269 | 31.59 |