Systemic Evolutionary Chemical Space Exploration for Drug Discovery

Overview

SECSE


SECSE: Systemic Evolutionary Chemical Space Explorer

plot

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 required):
    export $SCHRODINGER=path/to/SCHRODINGER

  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:
    [DEFAULT]

    • 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, type=str

    [docking]

    • 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

    [properties]

    • 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/run_secse.py --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

Dependencies


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

Citation


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.

License


SECSE is released under Apache License, Version 2.0.

You might also like...
ETMO: Evolutionary Transfer Multiobjective Optimization

ETMO: Evolutionary Transfer Multiobjective Optimization To promote the research on ETMO, benchmark problems are of great importance to ETMO algorithm

Guiding evolutionary strategies by (inaccurate) differentiable robot simulators @ NeurIPS, 4th Robot Learning Workshop
Guiding evolutionary strategies by (inaccurate) differentiable robot simulators @ NeurIPS, 4th Robot Learning Workshop

Guiding Evolutionary Strategies by Differentiable Robot Simulators In recent years, Evolutionary Strategies were actively explored in robotic tasks fo

BESS: Balanced Evolutionary Semi-Stacking for Disease Detection via Partially Labeled Imbalanced Tongue Data

Balanced-Evolutionary-Semi-Stacking Code for the paper ''BESS: Balanced Evolutionary Semi-Stacking for Disease Detection via Partially Labeled Imbalan

This is the repo for the paper `SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization'. (published in Bioinformatics'21)

SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization This is the code for our paper ``SumGNN: Multi-typed Drug

Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network
Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network

DeepCDR Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network This work has been accepted to ECCB2020 and was also published in the

Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2

CoaDTI Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2 Abstract Environment The test was conducted i

The code for SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network.

SAG-DTA The code is the implementation for the paper 'SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network'. Requirements py

[ICLR 2021] Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments.
[ICLR 2021] Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments.

[ICLR 2021] RAPID: A Simple Approach for Exploration in Reinforcement Learning This is the Tensorflow implementation of ICLR 2021 paper Rank the Episo

A mini library for Policy Gradients with Parameter-based Exploration, with reference implementation of the ClipUp optimizer  from NNAISENSE.
A mini library for Policy Gradients with Parameter-based Exploration, with reference implementation of the ClipUp optimizer from NNAISENSE.

PGPElib A mini library for Policy Gradients with Parameter-based Exploration [1] and friends. This library serves as a clean re-implementation of the

Comments
  • Problem running demo

    Problem running demo

    Hi!

    When I try to run the demo with the command below. python $SECSE/run_secse.py --config demo/phgdh_demo_vina.ini

    It generates pandas.errors.EmptyDataError: No columns to parse from file, what should I do to solve it? Thank you!

    Here is the output

    **************************************************************************************** 
          ____    _____    ____   ____    _____ 
         / ___|  | ____|  / ___| / ___|  | ____|
         \___ \  |  _|   | |     \___ \  |  _|  
          ___) | | |___  | |___   ___) | | |___ 
         |____/  |_____|  \____| |____/  |_____|
    /home/bruce/Downloads/Softwares/Anaconda/envs/secse/lib/python3.7/site-packages/pandas/core/generic.py:2882: UserWarning: The spaces in these column names will not be changed. In pandas versions < 0.14, spaces were converted to underscores.
     method=method,
    Table 'G-001' already exists.
    
    ******************************************************************
    Input fragment file: /home/bruce/Work/CADD/SECSE/code/demo/demo_1020.smi
    Target grid file: /home/bruce/Work/CADD/SECSE/code/demo/PHGDH_6RJ3_for_vina.pdbqt
    Workdir: /home/bruce/Work/CADD/SECSE/code/res/
    
    
    ************************************************** 
    Generation  0 ...
    Step 1: Docking with Autodock Vina ...
    /home/bruce/Work/CADD/SECSE/code/secse/evaluate/ligprep_vina_parallel.sh /home/bruce/Work/CADD/SECSE/code/res/generation_0 /home/bruce/Work/CADD/SECSE/code/demo/demo_1020.smi /home/bruce/Work/CADD/SECSE/code/demo/PHGDH_6RJ3_for_vina.pdbqt 20.9 -10.4 3.0 20.0 20.0 25.0 10
    find /home/bruce/Work/CADD/SECSE/code/res/generation_0/sdf_files -name "*sdf" | xargs -n 100 cat > /home/bruce/Work/CADD/SECSE/code/res/generation_0/docking_outputs_with_score.sdf
    Docking time cost: 0.12 min.
    Step 2: Ranking docked molecules...
    9 cmpds after evaluate
    The evaluate score cutoff is: -9.0
    9 final seeds.
    
    ************************************************** 
    Generation  1 ...
    Step 1: Mutation
    No rule class:  B-001
    No rule class:  G-003
    No rule class:  G-004
    No rule class:  G-005
    No rule class:  G-006
    No rule class:  G-007
    No rule class:  M-001
    No rule class:  M-002
    No rule class:  M-003
    No rule class:  M-004
    No rule class:  M-005
    No rule class:  M-006
    No rule class:  M-007
    No rule class:  M-008
    No rule class:  M-009
    No rule class:  M-010
    No rule class: G-002
    Step 2: Filtering all mutated mols
    sh /home/bruce/Work/CADD/SECSE/code/secse/growing/filter_parallel.sh /home/bruce/Work/CADD/SECSE/code/res/generation_1 1 demo/phgdh_demo_vina.ini 10
    Filter runtime: 0.00 min.
    Traceback (most recent call last):
     File "/home/bruce/Work/CADD/SECSE/code/secse/run_secse.py", line 80, in <module>
       main()
     File "/home/bruce/Work/CADD/SECSE/code/secse/run_secse.py", line 65, in main
       workflow.grow()
     File "/home/bruce/Work/CADD/SECSE/code/secse/grow_processes.py", line 208, in grow
       self._filter_df = pd.read_csv(os.path.join(self.workdir_now, "filter.csv"), header=None)
     File "/home/bruce/Downloads/Softwares/Anaconda/envs/secse/lib/python3.7/site-packages/pandas/util/_decorators.py", line 311, in wrapper
       return func(*args, **kwargs)
     File "/home/bruce/Downloads/Softwares/Anaconda/envs/secse/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 586, in read_csv
       return _read(filepath_or_buffer, kwds)
     File "/home/bruce/Downloads/Softwares/Anaconda/envs/secse/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 482, in _read
       parser = TextFileReader(filepath_or_buffer, **kwds)
     File "/home/bruce/Downloads/Softwares/Anaconda/envs/secse/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 811, in __init__
       self._engine = self._make_engine(self.engine)
     File "/home/bruce/Downloads/Softwares/Anaconda/envs/secse/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 1040, in _make_engine
       return mapping[engine](self.f, **self.options)  # type: ignore[call-arg]
     File "/home/bruce/Downloads/Softwares/Anaconda/envs/secse/lib/python3.7/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 69, in __init__
       self._reader = parsers.TextReader(self.handles.handle, **kwds)
     File "pandas/_libs/parsers.pyx", line 549, in pandas._libs.parsers.TextReader.__cinit__
    pandas.errors.EmptyDataError: No columns to parse from file
    
    opened by BW15061999 17
  • Question about running the demo code

    Question about running the demo code

    Hi authors,

    I have tried to run your demo code in README.md, but got some errors.

    Command

    python /home/xxx/workspace/off-SECSE/secse/run_secse.py --config ./config.ini
    

    Output

     **************************************************************************************** 
           ____    _____    ____   ____    _____ 
          / ___|  | ____|  / ___| / ___|  | ____|
          \___ \  |  _|   | |     \___ \  |  _|  
           ___) | | |___  | |___   ___) | | |___ 
          |____/  |_____|  \____| |____/  |_____|
    
    ******************************************************************
    Input fragment file: /home/xxx/workspace/off-SECSE/fy-run/demo001/ligand.smi
    Target grid file: /home/xxx/workspace/off-SECSE/fy-run/demo001/receptor.pdbqt
    Workdir: /home/xxx/workspace/off-SECSE/fy-run/demo001/
    
    Step 1: Docking with Autodock Vina ...
    /home/xxx/workspace/off-SECSE/secse/evaluate/ligprep_vina_parallel.sh /home/xxx/workspace/off-SECSE/fy-run/demo001/generation_0 /home/xxx/workspace/off-SECSE/fy-run/demo001/ligand.smi /home/t-yafan/workspace/off-SECSE/fy-run/demo001/receptor.pdbqt 20.9 -10.4 3.0 20.0 20.0 25.0 10
    find /home/xxx/workspace/off-SECSE/fy-run/demo001/generation_0/sdf_files -name "*sdf" | xargs -n 100 cat > /home/xxx/workspace/off-SECSE/fy-run/demo001/generation_0/docking_outputs_with_score.sdf
    Docking time cost: 0.11 min.
    Step 2: Ranking docked molecules...
    9 cmpds after evaluate
    The evaluate score cutoff is: -9.0
    9 final seeds.
    
     ************************************************** 
    Generation  1 ...
    Step 1: Mutation
    Traceback (most recent call last):
      File "/home/xxx/workspace/off-SECSE/secse/run_secse.py", line 70, in <module>
        main()
      File "/home/xxx/workspace/off-SECSE/secse/run_secse.py", line 55, in main
        workflow.grow()
      File "/home/xxx/workspace/off-SECSE/secse/grow_processes.py", line 159, in grow
        header = mutation_df(self.winner_df, self.workdir, self.cpu_num, self.gen)
      File "/home/xxx/workspace/off-SECSE/secse/growing/mutation/mutation.py", line 166, in mutation_df
        mutation = Mutation(5000, workdir)
      File "/home/xxx/workspace/off-SECSE/secse/growing/mutation/mutation.py", line 29, in __init__
        self.load_common_rules()
      File "/home/xxx/workspace/off-SECSE/secse/growing/mutation/mutation.py", line 50, in load_common_rules
        c.execute(sql)
    sqlite3.OperationalError: no such table: B-001
    

    It seems that the file secse/growing/mutation/rules_demo.db is missing in the repo. How can I fix it?

    Thanks!

    opened by fyabc 5
  • All dockings do not work because there's no gridding process.

    All dockings do not work because there's no gridding process.

    Hi, I was trying out the repo when I realised that neither the autodock nor glide is able to run because there was no gridding process, resulting in no grid files. >.<

    opened by yipy0005 3
Releases(v1.1.0)
TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction TSDF++ is a novel multi-object TSDF formulation that can encode mult

ETHZ ASL 130 Dec 29, 2022
A blender add-on that automatically re-aligns wrong axis objects.

Auto Align A blender add-on that automatically re-aligns wrong axis objects. Usage There are three options available in the 3D Viewport Sidebar It

29 Nov 25, 2022
My take on a practical implementation of Linformer for Pytorch.

Linformer Pytorch Implementation A practical implementation of the Linformer paper. This is attention with only linear complexity in n, allowing for v

Peter 349 Dec 25, 2022
This repository contains code from the paper "TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network"

TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network This repository contains code from the paper "TTS-GAN: A Transformer-based Tim

Intelligent Multimodal Computing and Sensing Laboratory (IMICS Lab) - Texas State University 108 Dec 29, 2022
A PyTorch implementation of EfficientDet.

A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights

Ross Wightman 1.4k Jan 07, 2023
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

WarpDrive is a flexible, lightweight, and easy-to-use open-source reinforcement learning (RL) framework that implements end-to-end multi-agent RL on a single GPU (Graphics Processing Unit).

Salesforce 334 Jan 06, 2023
Natural Intelligence is still a pretty good idea.

Human Learn Machine Learning models should play by the rules, literally. Project Goal Back in the old days, it was common to write rule-based systems.

vincent d warmerdam 641 Dec 26, 2022
zeus is a Python implementation of the Ensemble Slice Sampling method.

zeus is a Python implementation of the Ensemble Slice Sampling method. Fast & Robust Bayesian Inference, Efficient Markov Chain Monte Carlo (MCMC), Bl

Minas Karamanis 197 Dec 04, 2022
SphereFace: Deep Hypersphere Embedding for Face Recognition

SphereFace: Deep Hypersphere Embedding for Face Recognition By Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj and Le Song License SphereFa

Weiyang Liu 1.5k Dec 29, 2022
MonoScene: Monocular 3D Semantic Scene Completion

MonoScene: Monocular 3D Semantic Scene Completion MonoScene: Monocular 3D Semantic Scene Completion] [arXiv + supp] | [Project page] Anh-Quan Cao, Rao

298 Jan 08, 2023
AI assistant built in python.the features are it can display time,say weather,open-google,youtube,instagram.

AI assistant built in python.the features are it can display time,say weather,open-google,youtube,instagram.

AK-Shanmugananthan 1 Nov 29, 2021
A small library for doing fluid simulation with neural networks.

Neural Fluid Fields This is a small library for doing fluid simulation with neural fields. Check out our review paper, Neural Fields in Visual Computi

Towaki 23 Jun 23, 2022
TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020)

TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020) About The goal of our research problem is illustrated below: give

59 Dec 09, 2022
[CVPR2021] DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets

DoDNet This repo holds the pytorch implementation of DoDNet: DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datase

116 Dec 12, 2022
Pytorch implementation of PCT: Point Cloud Transformer

PCT: Point Cloud Transformer This is a Pytorch implementation of PCT: Point Cloud Transformer.

Yi_Zhang 265 Dec 22, 2022
Reproduce partial features of DeePMD-kit using PyTorch.

DeePMD-kit on PyTorch For better understand DeePMD-kit, we implement its partial features using PyTorch and expose interface consuing descriptors. Tec

Shaochen Shi 8 Dec 17, 2022
A tool to prepare websites grabbed with wget for local viewing.

makelocal A tool to prepare websites grabbed with wget for local viewing. exapmples After fetching xkcd.com with: wget -r -no-remove-listing -r -N --p

5 Apr 23, 2022
Leveraging OpenAI's Codex to solve cornerstone problems in Music

Music-Codex Leveraging OpenAI's Codex to solve cornerstone problems in Music Please NOTE: Presented generated samples were created by OpenAI's Codex P

Alex 2 Mar 11, 2022
Official implementation of "Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform", ICCV 2021

Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform This repository is the implementation of "Variable-Rate Deep Image C

Myungseo Song 47 Dec 13, 2022
Rainbow: Combining Improvements in Deep Reinforcement Learning

Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning [1]. Results and pretrained models can be found in the releases. DQN [2] Double

Kai Arulkumaran 1.4k Dec 29, 2022