Systemic Evolutionary Chemical Space Exploration for Drug Discovery

Related tags

Deep Learningdeep-learningde-novo-drug-designchemical-space-explorationfragment-based-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

Songbai Liu 0 Mar 16, 2021
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

Vladislav Kurenkov 4 Dec 14, 2021
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

null 0 Jan 16, 2022
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

Yue Yu 58 Dec 21, 2022
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

Qiao Liu 50 Dec 18, 2022
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

Layne_Huang 7 Nov 14, 2022
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

Shugang Zhang 7 Aug 2, 2022
[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

Daochen Zha 48 Nov 21, 2022
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

NNAISENSE 56 Jan 1, 2023
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)
Owner
GitHub Repository
Deep universal probabilistic programming with Python and PyTorch

Getting Started | Documentation | Community | Contributing Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notab

7.7k Dec 30, 2022
GitHub repository for "Improving Video Generation for Multi-functional Applications"

Improving Video Generation for Multi-functional Applications GitHub repository for "Improving Video Generation for Multi-functional Applications" Pape

Bernhard Kratzwald 328 Dec 07, 2022
Residual Dense Net De-Interlace Filter (RDNDIF)

Residual Dense Net De-Interlace Filter (RDNDIF) Work in progress deep de-interlacer filter. It is based on the architecture proposed by Bernasconi et

Louis 7 Feb 15, 2022
How to Become More Salient? Surfacing Representation Biases of the Saliency Prediction Model

How to Become More Salient? Surfacing Representation Biases of the Saliency Prediction Model

Bogdan Kulynych 49 Nov 05, 2022
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning

BEAS Blockchain Enabled Asynchronous and Secure Federated Machine Learning Default Network Configuration: The default application uses the HyperLedger

Harpreet Virk 11 Nov 20, 2022
BOVText: A Large-Scale, Multidimensional Multilingual Dataset for Video Text Spotting

BOVText: A Large-Scale, Bilingual Open World Dataset for Video Text Spotting Updated on December 10, 2021 (Release all dataset(2021 videos)) Updated o

weijiawu 47 Dec 26, 2022
a delightful machine learning tool that allows you to train, test and use models without writing code

igel A delightful machine learning tool that allows you to train/fit, test and use models without writing code Note I'm also working on a GUI desktop

Nidhal Baccouri 3k Jan 05, 2023
Official repo for BMVC2021 paper ASFormer: Transformer for Action Segmentation

ASFormer: Transformer for Action Segmentation This repo provides training & inference code for BMVC 2021 paper: ASFormer: Transformer for Action Segme

42 Dec 23, 2022
[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data (NeurIPS 2021) This repository will provide the official PyTorch implementa

Liming Jiang 238 Nov 25, 2022
Implementation of paper "Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal"

Patch-wise Adversarial Removal Implementation of paper "Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal

4 Oct 12, 2022
Dynamical movement primitives (DMPs), probabilistic movement primitives (ProMPs), spatially coupled bimanual DMPs.

Movement Primitives Movement primitives are a common group of policy representations in robotics. There are many different types and variations. This

DFKI Robotics Innovation Center 63 Jan 06, 2023
Python Blood Vessel Topology Analysis

Python Blood Vessel Topology Analysis This repository is not being updated anymore. The new version of PyVesTo is called PyVaNe and is available at ht

6 Nov 15, 2022
Implementation of Artificial Neural Network Algorithm

Artificial Neural Network This repository contain implementation of Artificial Neural Network Algorithm in several programming languanges and framewor

Resha Dwika Hefni Al-Fahsi 1 Sep 14, 2022
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

Shugang Zhang 7 Aug 02, 2022
TensorFlow ROCm port

Documentation TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, a

ROCm Software Platform 622 Jan 09, 2023
Neural-net-from-scratch - A simple Neural Network from scratch in Python using the Pymathrix library

A Simple Neural Network from scratch A Simple Neural Network from scratch in Pyt

Youssef Chafiqui 2 Jan 07, 2022
Safe Control for Black-box Dynamical Systems via Neural Barrier Certificates

Safe Control for Black-box Dynamical Systems via Neural Barrier Certificates Installation Clone the repository: git clone https://github.com/Zengyi-Qi

Zengyi Qin 3 Oct 18, 2022
Official code for NeurIPS 2021 paper "Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN"

Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN Official code for NeurIPS 2021 paper "Towards Scalable Unpaired Virtu

68 Dec 21, 2022
RAMA: Rapid algorithm for multicut problem

RAMA: Rapid algorithm for multicut problem Solves multicut (correlation clustering) problems orders of magnitude faster than CPU based solvers without

Paul Swoboda 60 Dec 13, 2022
Analysing poker data from home games with friends

Poker Game Analysis Analysing poker data from home games with friends. Not a lot of data is collected, so this project is primarily focussed on descri

Stavros Karmaniolos 1 Oct 15, 2022