D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.
Conor D ParksZied GaiebMichael ChiuHuanwang YangChenghua ShaoW Patrick WaltersJohanna M JansenGeorgia McGaugheyRichard A LewisScott D BembenekMichael K AmeriksTara MirzadeganStephen K BurleyRommie Elizabeth AmaroMichael K GilsonPublished in: Journal of computer-aided molecular design (2020)
The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.
Keyphrases
- primary care
- healthcare
- capillary electrophoresis
- electronic health record
- binding protein
- gas chromatography
- emergency department
- randomized controlled trial
- clinical trial
- adverse drug
- mass spectrometry
- machine learning
- big data
- drug induced
- high resolution
- density functional theory
- transcription factor
- deep learning
- liquid chromatography
- double blind
- dna binding
- data analysis