ArticleJournal of computer-aided molecular design2018
Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations.
Article in Journal of computer-aided molecular design, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 MRSC advances · 2023Article
- Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.Frontiers in bioinformatics · 2022Article
- Artificial Intelligence in Hematology: Current Challenges and Opportunities.Current hematologic malignancy reports · 2020Review
- D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.Journal of computer-aided molecular design · 2020Article
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2 authors.
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Abstract
We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org/ ). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein. To deal with these conformational changes we employed multiple simulations of molecular dynamics (MD). Our MD-based protocols were top-ranked in estimating the free energy of binding of the ligands and FXR protein. Our approach was ranked second in the prediction of the binding poses where we also combined MD with molecular docking and artificial neural networks. Our approach showed mediocre results for high-throughput scoring of interactions.
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