1 File Tree

1.1 Root

.
└── DUD-E
    ├── aa2ar
    ├── abl1
    ├── ace
    ├── aces
    ├── ada
    ├── ada17
    ├── adrb1
    ├── adrb2
    ├── akt1
    ├── akt2
    ├── aldr
    ├── ampc
    ├── andr
    ├── aofb
    ├── bace1
    ├── braf
    ├── cah2
    ├── casp3
    ├── cdk2
    ├── comt
    ├── cp2c9
    ├── cp3a4
    ├── csf1r
    ├── cxcr4
    ├── def
    ├── dhi1
    ├── dpp4
    ├── drd3
    ├── dyr
    ├── egfr
    ├── esr1
    ├── esr2
    ├── fa10
    ├── fa7
    ├── fabp4
    ├── fak1
    ├── fgfr1
    ├── fkb1a
    ├── fnta
    ├── fpps
    ├── gcr
    ├── glcm
    ├── gria2
    ├── grik1
    ├── hdac2
    ├── hdac8
    ├── hivint
    ├── hivpr
    ├── hivrt
    ├── hmdh
    ├── hs90a
    ├── hxk4
    ├── igf1r
    ├── inha
    ├── ital
    ├── jak2
    ├── kif11
    ├── kit
    ├── kith
    ├── kpcb
    ├── lck
    ├── lkha4
    ├── mapk2
    ├── mcr
    ├── met
    ├── mk01
    ├── mk10
    ├── mk14
    ├── mmp13
    ├── mp2k1
    ├── nos1
    ├── nram
    ├── pa2ga
    ├── parp1
    ├── pde5a
    ├── pgh1
    ├── pgh2
    ├── plk1
    ├── pnph
    ├── ppara
    ├── ppard
    ├── pparg
    ├── prgr
    ├── ptn1
    ├── pur2
    ├── pygm
    ├── pyrd
    ├── reni
    ├── rock1
    ├── rxra
    ├── sahh
    ├── src
    ├── tgfr1
    ├── thb
    ├── thrb
    ├── try1
    ├── tryb1
    ├── tysy
    ├── urok
    ├── vgfr2
    ├── wee1
    └── xiap

103 directories

1.2 Target Example

./pde5a
├── actives_final.ism # clustered ligands
├── actives_final.mol2
├── actives_final.sdf.gz
├── crystal_ligand.mol2 # unambiguous cocrystal ligand as prepared by DOCK Blaster
├── decoys_final.ism # clustered decoys
├── decoys_final.mol2.gz
├── decoys_final.sdf.gz
└── receptor.pdb # 

0 directories, 8 files

2 Data Scale

3 Data Construction

3.1 Data Extraction

1. Targets
   • 38 targets from DUD

     Forty protein targets were selected based on the availability of annotated ligands, crystal structures and, often, previous docking studies

   • new

     we favored new target classes with pharmacological precedence

   • many ligands and crystal structures

     we sought targets with many ligands and crystal structures, as they likely reflect a combination of target relevance and ease of study

   • modestly enrich known ligands

     we preferred targets that could modestly enrich known ligands using fully automated docking, as these may be both easy to prepare and amenable to docking

2. Ligands

   Ligands assigned to protein targets (ChEMBL confidence score ≥4) with affinities (IC50, EC50, Ki, Kd, and log variants thereof) of 1 μM or better were extracted from the ChEMBL09 database

3. Decoys
   • experimental decoys

     we assigned experimental decoys as molecules with no measurable affinity at 30 μM or higher (greater than relation only)

3.2 Ligand Prepare

• Conformers
  • Ligand conformations were generated by OpenEye’s Omega.

3.3 Decoys Generation

• Why DUD-E should ensure that decoys and ligands are not similar？
  • 2-D chemical dissimilarity filter to prevent decoys from being active

4 Usage

4.1 Code

• esp-sim
  • benchmark_4_dud-4.ipynb

4.2 Describtion

• ACPC

  For each of the 40 targets, the query was the last ligand in the ligands list of each target.

• eSim

  1. Cognate ligand
  
  2. Initial Structure 3D Generation
  
  3. Conformer generation