Cheminformatic Tools and Databases for Pharmacology
LEA3D offers a facility to screen or to design small molecules

The user first builds a scoring function and then, at the second step, either uploads a library of molecules or selects the de novo drug design option

The FDA-approved drug structure data set can be selected at the step 2


Step 1: Fill the scoring function form (shape similarity and/or docking and/or properties) and Submit

The field "Weight in final score" must be different from 0 in order to activate the individual function.

Set the docking function

 Use the docking program PLANTS (reference (12))
  • (*) Protein structure file in pdb format (more):  

      (option) skip the protonation step and upload your protein structure file in .mol2 format (*):

      (*) this file will be the input for PLANTS (can contains water, ions, co-factors... in .mol2 format too) but the protein structure file in pdb format is still required for the display

  • (*) Definition of the binding site around a residue or a ligand in the pdb structure:

    RESIDUE name (case sensitive)   RESIDUE number   CHAIN (or leave empty)

    or set coordinates of the center of the binding site: x ,   y ,   z

    (*) Binding site radius

    rq: water molecules are excluded in this online implementation except if you submit the prepared .mol2 file.

  • (*) Weight in final score (e.g. 1)  
  • (*) mandatory fields

    It is recommended to associate the docking function with an upper limit for the Molecular Weight (MW<500 for example; see the "select properties" function below) to speed up the calculation because this will discard large molecules before the docking.

    Calculate molecular shape similarities with a reference molecule

     Flexible alignment of molecules on your reference molecule with SenSaaS (reference (19))
    • The uploaded reference molecule must be with hydrogens and must have 3D coordinates (you can use this drawing tool)

      (*) Reference molecule in .sdf format  

    • (*) Weight in final score (e.g. 1)  

    (*) mandatory fields

    Select properties

     Molecular properties (help ; reference values were extracted from drug-like and/or lead-like (narrower than drug-like) studies)
    Property nameMinimal valueMaximal valueWeight in final score (e.g. 1)Reference for the property or for the setting values
    Molecular weightDrug-like (1)
    Lead-like if MW <= 350 (15)
    Fragment-like if MW <= 300 (Ro3; 16)
    MolLogPRDKit implementation of Wildman and Crippen (18)
    Drug-like (1) (8)
    Lead-like if logP <= 3 (15)
    Fragment-like if logP <= 3 (Ro3; 16)
    Number of atoms (H excluded)Drug-like (2)
    Number of h-donorsDrug-like (1)
    Fragment-like if nhd <= 3 (Ro3; 16)
    Number of h-acceptorsDrug-like (1)
    Fragment-like if nha <= 3 (Ro3; 16)
    Polar solvent accessible surface areaDrug-like (3) (14) (9)
    Fragment-like if PSA <= 60 (Ro3; 16)
    Number of rotatable bondsDrug-like (2)
    Fragment-like if rot <= 3 (Ro3; 16)
    Number of ringsDrug-like (2)
    Number of aromatic ringsDrug-like (2)


    Preparation of the protein file for PLANTS

    The program PLANTS needs a .mol2 protein file format thus our procedure automatically protonates the uploaded .pdb structure file by using the program PDBPQR with AMBER forcefield option. Then, the pdb file is converted into a .mol2 file format (tool to prepare protein input files).

    • The final score is the sum of each selected property. The score is expressed in percentage (%) where each selected property contributes proportionally to its weight (ie. ∑(weighti) → 100% with i a selected property).

    • How to set property values:

      Example 1: set the minimal value only
      Molecular weight100-1.0Means MW must be ≥ 100
      Example 2: set the maximal value only
      Molecular weight-4691.0Means MW must be ≤ 469
      Example 3: set the minimal and the maximal value with the same value
      Molecular weight1001001.0Means MW must be exactly 100
      Example 4: set the minimal and the maximal value with different values
      Molecular weight504691.0Means MW must be ≥ 50 and must be ≤ 469


    (1) 90th percentile: Proudfoot et al., Bioorg. Med. Chem. Letters, 15, 1087-1090, 2005.

    (2) Lepre et al., DDT, 6(3), 2001.

    (3) Clark and Pickett, DDT, 5(2), 2000.

    (4) Ghose et al., J. Comb. Chem., 1, 55-68, 1999.

    (5) Akritopoulou-zanze et al., DDT, 12, 948-952, 2007.

    (6) Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J., Experimental and computational approaches to estmate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 23, 4-25, 1997.

    (7) Roche et al., Development of a virtual screening method for identification of frequent hitters in compound librairies, J. Med. Chem., 45, 137-142, 2002.

    (8) Wang R. et al., J. Chem. Inf. Comput. Sci., 37, 615-621, 1997.

    (9) Eisenhaber, F., Argos, P., Improved strategy in analytic surface calculation for molecular systems: Handling singularities and computational efficiency., J. Comput. Chem., 11, 1272-1280, 1993.

    (10) Viswanadhan,V.N., Ghose A.K ., Revankar,G.R. and Robins,R.K., J. Chem. Inf. Comput.Sci, 29, 163-172, 1989.

    (11) Willet P., Barnard J.M., Downs G.M., Chemical similarity searching. Journal of Chemical Information and Computer Sciences, 38(6), 983-996, 1998.

    (12) Korb O, Stützle T, Exner TE., Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model., 49(1), 84-96, 2009.

    (13) Wang R., Lab H. and Wang S., Further development and validation of empirical scoring functions for structure-based binding affinity prediction, Journal of Computer-Aided Molecular Design, 16, 11-26, 2002.

    (14) Veber, D. F.; Johnson, S. R.; Cheng, H. Y.; Smith, B. R.; Ward, K. W.; Kopple, K. D., Molecular properties that influence the oral bioavailability of drug candidates, Journal of Medicinal Chemistry, 45(12), 2615-23, 2002.

    (15) Teague S.J., Davis A.M., Leeson P.D. and Oprea T., The Design of Leadlike Combinatorial Libraries, Angew. Chem. Int. Ed., 38(24), 3743-3747, 1999.

    (16) Congreve M., Carr R., Murray C. and Jhoti H. A 'rule of three' for fragment-based lead discovery?, Drug Discov Today, 8, 876-877, 2003.

    (17) fraction of carbon atoms that are sp3 hybridized. fsp3 = Number of C(sp3)/ Number of C. Lovering F, Bikker J, Humblet C, Escape from flatland: increasing saturation as an approach to improving clinical success, J Med Chem., 52, (21), 6752-6, 2009.

    (18) Wildman S.A. and Crippen G.M., Prediction of Physicochemical Parameters by Atomic Contributions, J. Chem. Inf. Comput. Sci., 39:868-73 1999.

    (19) Douguet D. and Payan F., SenSaaS (SENsitive Surface As A Shape): molecular alignment by registration of point-based surfaces (2019). More details at arXiv.

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