Systemic Evolutionary Chemical Space Exploration for Drug Discovery

Last update: Dec 16, 2022

Overview

SECSE

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

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
Give execution permissions to the SECSE directory
chmod -R +X path/to/SECSE
Input fragments: a tab split .smi file without header. See demo here.
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
Run SECSE
python $SECSE/run_secse.py --config path/to/config
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

CentOS / RHEL
yum install parallel
Ubuntu / Debian
sudo apt install parallel
From source: https://www.gnu.org/software/parallel/

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.

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

0 Mar 16, 2021

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

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

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

58 Dec 21, 2022

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

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

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

7 Aug 2, 2022

[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

48 Nov 21, 2022

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

56 Jan 1, 2023

Comments

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

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.

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)

v1.1.0(Jul 15, 2022)

Source code(tar.gz)
Source code(zip)
v1.0.0(Feb 24, 2022)

Source code(tar.gz)
Source code(zip)

Owner

GitHub Repository

A project to build an AI voice assistant using Python . The Voice assistant interacts with the humans to perform basic tasks.

AI_Personal_Voice_Assistant_Using_Python A project to build an AI voice assistant using Python . The Voice assistant interacts with the humans to perf

1 Oct 30, 2021

[ICCV 2021] Deep Hough Voting for Robust Global Registration

Deep Hough Voting for Robust Global Registration, ICCV, 2021 Project Page | Paper | Video Deep Hough Voting for Robust Global Registration Junha Lee1,

10 Dec 02, 2022

Differential rendering based motion capture blender project.

TraceArmature Summary TraceArmature is currently a set of python scripts that allow for high fidelity motion capture through the use of AI pose estima

4 May 27, 2022

The codes and related files to reproduce the results for Image Similarity Challenge Track 2.

89 Jan 02, 2023

Code for "Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks", CVPR 2021

Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks This repository contains the code that accompanies our CVPR 20

161 Dec 20, 2022

CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields

CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields Paper | Supplementary | Video | Poster If you find our code or paper useful, please

26 Nov 29, 2022

Bringing Characters to Life with Computer Brains in Unity

AI4Animation: Deep Learning for Character Control This project explores the opportunities of deep learning for character animation and control as part

5.5k Jan 04, 2023

Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

6 Nov 25, 2022

Source code for our paper "Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures"

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures Code for the Multiplex Molecular Graph Neural Network (M

59 Dec 10, 2022

Repository aimed at compiling code, papers, demos etc.. related to my PhD on 3D vision and machine learning for fruit detection and shape estimation at the university of Lincoln

PhD_3DPerception Repository aimed at compiling code, papers, demos etc.. related to my PhD on 3D vision and machine learning for fruit detection and s

2 Oct 06, 2022

Systemic Evolutionary Chemical Space Exploration for Drug Discovery

Related tags