Back to Search
Start Over
Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3.
- Source :
-
Journal of computer-aided molecular design [J Comput Aided Mol Des] 2019 Jan; Vol. 33 (1), pp. 35-46. Date of Electronic Publication: 2018 Aug 09. - Publication Year :
- 2019
-
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.
Details
- Language :
- English
- ISSN :
- 1573-4951
- Volume :
- 33
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Journal of computer-aided molecular design
- Publication Type :
- Academic Journal
- Accession number :
- 30094533
- Full Text :
- https://doi.org/10.1007/s10822-018-0139-5