ArticleJournal of computer-aided molecular design2018
Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP.
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 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens.International journal of molecular sciences · 2020Review
- The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations.Journal of computer-aided molecular design · 2020Article
- D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.Journal of computer-aided molecular design · 2020Article
- DeltaDelta neural networks for lead optimization of small molecule potency.Chemical science · 2019Article
- Improving small molecule virtual screening strategies for the next generation of therapeutics.Current opinion in chemical biology · 2018Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the commercial free energy calculation protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two experimental data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for molecular modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future in-house drug design projects.
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