how to perform a de novo drug design?

Computer-Aided Drug Design at the Durrant Lab Forums AutoGrow4 how to perform a de novo drug design?

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    • #52394 Reply

      Hi everybody,
      maybe this is a naive question but how can I perform de novo drug design? After running some lead optimization runs in which I have seeded the autogrow with fragments of a known drug, I wanted to check what de novo drug is Autogrow suggesting. AS I could not find a detailed description how to perform de novo design run, I just ran Autogrow with use_docked_source_compounds = False and selecting a source_compounds that does not contain the known inhibitors nor their fragments. However, this "protocol" led to very similar results as those when running my lead optimization protocol, yielding no new drug suggestions. What I am doing wrong? Is there some kind of "memory" that reproduces the results of my previous runs? I would highly appreciate if somebody could give me a brief instruction how to run a de novo drug design properly or explain to me in which parameters the lead optimization and de novo runs differ. Maybe somebody could share a general parameter file of a random de novo drug design run?
      Thanks in advance.


    • #72361 Reply

      Hi, I am also wondering about reccomended parameters for a de novo run. In the article "AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization (2020)" the authors write that "Additional file 1: Json 3" will contain the parameters used for their de novo PARPi run, but I am unable to locate these parameters anywhere within the downloadable file.

    • #72417 Reply

      After searching a little bit more, I found the parameters in Additional file 2!:

      "filename_of_receptor": "/autogrow4/tutorial/PARP/4r6eA_PARP1_prepared.pdb",
      "center_x": -70.76,
      "center_y": 21.82,
      "center_z": 28.33,
      "size_x": 25.0,
      "size_y": 16.0,
      "size_z": 25.0,
      "number_of_mutants_first_generation": 500,
      "number_of_crossovers_first_generation": 500,
      "number_elitism_advance_from_previous_gen_first_generation": 40,
      "number_of_mutants": 2500,
      "number_of_crossovers": 2500,
      "number_elitism_advance_from_previous_gen": 500,
      "top_mols_to_seed_next_generation_first_generation": 50,
      "top_mols_to_seed_next_generation": 500,
      "diversity_mols_to_seed_first_generation": 500,
      "diversity_seed_depreciation_per_gen": 5,
      "num_generations": 30,
      "number_of_processors": 280,
      "dock_choice": "QuickVina2Docking",
      "scoring_choice": "VINA",
      "selector_choice": "Rank_Selector",
      "LipinskiStrictFilter": true,
      "GhoseFilter": true,
      "PAINSFilter": true,
      "reduce_files_sizes": true,
      "max_variants_per_compound": 5,
      "filter_source_compounds": false,
      "use_docked_source_compounds": true,
      "rxn_library": "all_rxns",
      "multithread_mode": "mpi"

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