SECSE: Systemic Evolutionary Chemical Space Explorer


Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities.
Compared with other screening technologies, computational de novo design has become a popular approach to overcome the
limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary
chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized
a “lego-building” process within the pocket of a certain target. The key of virtual hits generation was then turned into
a computational search problem. To enhance search and optimization, human intelligence and deep learning were
integrated. SECSE has the potential in finding novel and diverse small molecules that are attractive starting points for
further validation.

Tutorials and Usage

  1. Set Environment Variables
    export $SECSE=path/to/SECSE
    if you use AutoDock Vina for docking:
    (download here)
    export $VINA=path/to/AutoDockVINA
    if you use Gilde for docking (additional installation & license

  2. Give execution permissions to the SECSE directory
    chmod -R +X path/to/SECSE

  3. Input fragments: a tab split .smi file without header. See demo here.

  4. Parameters in config file:

    • workdir, working directory, create if not exists, otherwise overwrite, type=str
    • fragments, file path to seed fragments, smi format, type=str
    • num_gen, number of generations, type=int
    • num_per_gen, number of molecules generated each generation, type=int
    • seed_per_gen, number of selected seed molecules per generation, default=1000, type=int
    • start_gen, number of staring generation, default=0, type=int
    • docking_program, name of docking program, AutoDock-Vina (input vina) or Glide (input glide) , default=vina,


    • target, protein PDBQT if use AutoDock Vina; Grid file if choose Glide, type=str
    • RMSD, docking pose RMSD cutoff between children and parent, default=2, type=float
    • delta_score, decreased docking score cutoff between children and parent, default=-1.0, type=float
    • score_cutoff, default=-9, type=float

    Parameters when docking by AutoDock Vina:

    • x, Docking box x, type=float
    • y, Docking box y, type=float
    • z, Docking box z, type=float
    • box_size_x, Docking box size x, default=20, type=float
    • box_size_y, Docking box size y, default=20, type=float
    • box_size_z, Docking box size z, default=20, type=float

    [deep learning]

    • mode, mode of deep learning modeling, 0: not use, 1: modeling per generation, 2: modeling overall after all the
      generation, default=0, type=int
    • dl_per_gen, top N predicted molecules for docking, default=100, type=int
    • dl_score_cutoff, default=-9, type=float


    • MW, molecular weights cutoff, default=450, type=int
    • logP_lower, minimum of logP, default=0.5, type=float
    • logP_upper, maximum of logP, default=7, type=float
    • chiral_center, maximum of chiral center,default=3, type=int
    • heteroatom_ratio, maximum of heteroatom ratio, default=0.35, type=float
    • rotatable_bound_num, maximum of rotatable bound, default=5, type=int
    • rigid_body_num, default=2, type=int

    Config file of a demo case phgdh_demo_vina.ini

  5. Run SECSE
    python $SECSE/ --config path/to/config

  6. Output files

    • merged_docked_best_timestamp_with_grow_path.csv: selected molecules and growing path
    • selected.sdf: 3D conformers of all selected molecules


GNU Parallel installation

numpy~=1.20.3, pandas~=1.3.3, pandarallel~=1.5.2, tqdm~=4.62.2, biopandas~=0.2.9, openbabel~=3.1.1, rdkit~=2021.03.5,
chemprop~=1.3.1, torch~=1.9.0+cu111


Lu, C.; Liu, S.; Shi, W.; Yu, J.; Zhou, Z.; Zhang, X.; Lu, X.; Cai, F.; Xia, N.; Wang, Y. Systemic Evolutionary Chemical
Space Exploration For Drug Discovery. ChemRxiv 2021. This content is a preprint and has not been peer-reviewed.


SECSE is released under Apache License, Version 2.0.


View Github