ArticleJournal of computer-aided molecular design2019
Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3.
Article in Journal of computer-aided molecular design, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Structural bioinformatics for rational drug design.Research and practice in thrombosis and haemostasis · 2025Article
- Repurposing FDA-Approved Drugs Against Potential Drug Targets Involved in Brain Inflammation Contributing to Alzheimer's Disease.Targets (Basel) · 2024Article
- A novel peptide-based tau aggregation inhibitor as a potential therapeutic for Alzheimer's disease and other tauopathies.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Poisson-Boltzmann-based machine learning model for electrostatic analysis.Biophysical journal · 2024Article
- Evaluation of the Therapeutic Potential of Sulfonyl Urea Derivatives as Soluble Epoxide Hydrolase (sEH) Inhibitors.Molecules (Basel, Switzerland) · 2024Article
- Correlation of protein binding pocket properties with hits' chemistries used in generation of ultra-large virtual libraries.Journal of computer-aided molecular design · 2024Article
- A computational framework for identifying chemical compounds to bind Apolipoprotein E4 for Alzheimer's disease intervention.Frontiers in systems biology · 2023Article
- Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4.Journal of computer-aided molecular design · 2022Article
- Further exploration of the structure-activity relationship of dual soluble epoxide hydrolase/fatty acid amide hydrolase inhibitors.Bioorganic & medicinal chemistry · 2021Article
- Article
- D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.Journal of computer-aided molecular design · 2020Article
- Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4.Journal of computer-aided molecular design · 2020Article
- Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S.Journal of computer-aided molecular design · 2019Article
- Macrocycle modeling in ICM: benchmarking and evaluation in D3R Grand Challenge 4.Journal of computer-aided molecular design · 2019Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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