Reaction SMILES-AA mapping via language modelling

Related tags

Deep Learningrxnaamapper
Overview

rxn-aa-mapper

Reactions SMILES-AA sequence mapping

setup

conda env create -f conda.yml
conda activate rxn_aa_mapper

In the following we consider on examples provided to show how to use RXNAAMapper.

generate a vocabulary to be used with the EnzymaticReactionBertTokenizer

Create a vocabulary compatible with the enzymatic reaction tokenizer:

create-enzymatic-reaction-vocabulary ./examples/data-samples/biochemical ./examples/token_75K_min_600_max_750_500K.json /tmp/vocabulary.txt "*.csv"

use the tokenizer

Using the examples vocabulary and AA tokenizer provided, we can observe the enzymatic reaction tokenizer in action:

from rxn_aa_mapper.tokenization import EnzymaticReactionBertTokenizer

tokenizer = EnzymaticReactionBertTokenizer(
    vocabulary_file="./examples/vocabulary_token_75K_min_600_max_750_500K.txt",
    aa_sequence_tokenizer_filepath="./examples/token_75K_min_600_max_750_500K.json"
)
tokenizer.tokenize("NC(=O)c1ccc[n+]([C@@H]2O[[email protected]](COP(=O)(O)OP(=O)(O)OC[[email protected]]3O[C@@H](n4cnc5c(N)ncnc54)[[email protected]](O)[C@@H]3O)[C@@H](O)[[email protected]]2O)c1.O=C([O-])CC(C(=O)[O-])C(O)C(=O)[O-]|AGGVKTVTLIPGDGIGPEISAAVMKIFDAAKAPIQANVRPCVSIEGYKFNEMYLDTVCLNIETACFATIKCSDFTEEICREVAENCKDIK>>O=C([O-])CCC(=O)C(=O)[O-]")

train the model

The mlm-trainer script can be used to train a model via MTL:

mlm-trainer \
    ./examples/data-samples/biochemical ./examples/data-samples/biochemical \  # just a sample, simply split data in a train and a validation folder
    ./examples/vocabulary_token_75K_min_600_max_750_500K.txt /tmp/mlm-trainer-log \
    ./examples/sample-config.json "*.csv" 1 \  # for a more realistic config see ./examples/config.json
    ./examples/data-samples/organic ./examples/data-samples/organic \  # just a sample, simply split data in a train and a validation folder
    ./examples/token_75K_min_600_max_750_500K.json

Checkpoints will be stored in the /tmp/mlm-trainer-log for later usage in identification of active sites.

Those can be turned into an HuggingFace model by simply running:

checkpoint-to-hf-model /path/to/model.ckpt /tmp/rxnaamapper-pretrained-model ./examples/vocabulary_token_75K_min_600_max_750_500K.txt ./examples/sample-config.json ./examples/token_75K_min_600_max_750_500K.json

predict active site

The trained model can used to map reactant atoms to AA sequence locations that potentially represent the active site.

from rxn_aa_mapper.aa_mapper import RXNAAMapper

config_mapper = {
    "vocabulary_file": "./examples/vocabulary_token_75K_min_600_max_750_500K.txt",
    "aa_sequence_tokenizer_filepath": "./examples/token_75K_min_600_max_750_500K.json",
    "model_path": "/tmp/rxnaamapper-pretrained-model",
    "head": 3,
    "layers": [11],
    "top_k": 1,
}
mapper = RXNAAMapper(config=config_mapper)
mapper.get_reactant_aa_sequence_attention_guided_maps(["NC(=O)c1ccc[n+]([C@@H]2O[[email protected]](COP(=O)(O)OP(=O)(O)OC[[email protected]]3O[C@@H](n4cnc5c(N)ncnc54)[[email protected]](O)[C@@H]3O)[C@@H](O)[[email protected]]2O)c1.O=C([O-])CC(C(=O)[O-])C(O)C(=O)[O-]|AGGVKTVTLIPGDGIGPEISAAVMKIFDAAKAPIQANVRPCVSIEGYKFNEMYLDTVCLNIETACFATIKCSDFTEEICREVAENCKDIK>>O=C([O-])CCC(=O)C(=O)[O-]"])

citation

@article{dassi2021identification,
  title={Identification of Enzymatic Active Sites with Unsupervised Language Modeling},
  author={Dassi, Lo{\"\i}c Kwate and Manica, Matteo and Probst, Daniel and Schwaller, Philippe and Teukam, Yves Gaetan Nana and Laino, Teodoro},
  year={2021}
  conference={AI for Science: Mind the Gaps at NeurIPS 2021, ELLIS Machine Learning for Molecule Discovery Workshop 2021}
}
Owner
GitHub Repository
Code for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss"

PurNet Project for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss" Abstract Image-based salie

Jinming Su 4 Aug 25, 2022
Cupytorch - A small framework mimics PyTorch using CuPy or NumPy

CuPyTorch CuPyTorch是一个小型PyTorch，名字来源于： 不同于已有的几个使用NumPy实现PyTorch的开源项目，本项目通过CuPy支持

Xingkai Yu 23 Aug 17, 2022
IRON Kaggle project done while doing IRONHACK Bootcamp where we had to analyze and use a Machine Learning Project to predict future sales

IRON Kaggle project done while doing IRONHACK Bootcamp where we had to analyze and use a Machine Learning Project to predict future sales. In this case, we ended up using XGBoost because it was the o

1 Jan 04, 2022
PyTorch implementation of ''Background Activation Suppression for Weakly Supervised Object Localization''.

Background Activation Suppression for Weakly Supervised Object Localization PyTorch implementation of ''Background Activation Suppression for Weakly S

35 Jan 06, 2023
Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation"

DSP Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation". Accepted by ACM Multimedia 2021. Authors

20 Oct 24, 2022
Supervised Contrastive Learning for Downstream Optimized Sequence Representations

SupCL-Seq 📖 Supervised Contrastive Learning for Downstream Optimized Sequence representations (SupCS-Seq) accepted to be published in EMNLP 2021, ext

Hooman Sedghamiz 18 Oct 21, 2022
Implementing DeepMind's Fast Reinforcement Learning paper

Fast Reinforcement Learning This is a repo where I implement the algorithms in the paper, Fast reinforcement learning with generalized policy updates.

Marcus Chiam 6 Nov 28, 2022
Vehicle Detection Using Deep Learning and YOLO Algorithm

VehicleDetection Vehicle Detection Using Deep Learning and YOLO Algorithm Dataset take or find vehicle images for create a special dataset for fine-tu

Maryam Boneh 96 Jan 05, 2023
Vision transformers (ViTs) have found only limited practical use in processing images

CXV Convolutional Xformers for Vision Vision transformers (ViTs) have found only limited practical use in processing images, in spite of their state-o

Cloudwalker 23 Sep 10, 2022
EZ graph is an easy to use AI solution that allows you to make and train your neural networks without a single line of code.

EZ-Graph EZ Graph is a GUI that allows users to make and train neural networks without writing a single line of code. Requirements python 3 pandas num

1 Jul 03, 2022
PyTorch implementation of spectral graph ConvNets, NIPS’16

Graph ConvNets in PyTorch October 15, 2017 Xavier Bresson http://www.ntu.edu.sg/home/xbresson https://github.com/xbresson https://twitter.com/xbresson

Xavier Bresson 287 Jan 04, 2023
In this work, we will implement some basic but important algorithm of machine learning step by step.

WoRkS continued English 中文 Français Probability Density Estimation-Non-Parametric Methods(概率密度估计-非参数方法) 1. Kernel / k-Nearest Neighborhood Density Est

liziyu0104 1 Dec 30, 2021
Near-Optimal Sparse Allreduce for Distributed Deep Learning (published in PPoPP'22)

Near-Optimal Sparse Allreduce for Distributed Deep Learning (published in PPoPP'22) Ok-Topk is a scheme for distributed training with sparse gradients

Shigang Li 9 Oct 29, 2022
This code is an implementation for Singing TTS.

MLP Singer This code is an implementation for Singing TTS. The algorithm is based on the following papers: Tae, J., Kim, H., & Lee, Y. (2021). MLP Sin

Heejo You 22 Dec 23, 2022
A tool for calculating distortion parameters in coordination complexes.

OctaDist Octahedral distortion calculator: A tool for calculating distortion parameters in coordination complexes. https://octadist.github.io/ Registe

OctaDist 12 Oct 04, 2022
A Python module for parallel optimization of expensive black-box functions

blackbox: A Python module for parallel optimization of expensive black-box functions What is this? A minimalistic and easy-to-use Python module that e

Paul Knysh 426 Dec 08, 2022
CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K

CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K Our dataset VSD4K includes 6 popular categories: game, sport, dance, vlog, interview and city.

96 Jul 05, 2022
Hierarchical Time Series Forecasting with a familiar API

scikit-hts Hierarchical Time Series with a familiar API. This is the result from not having found any good implementations of HTS on-line, and my work

Carlo Mazzaferro 204 Dec 17, 2022
CryptoFrog - My First Strategy for freqtrade

cryptofrog-strategies CryptoFrog - My First Strategy for freqtrade NB: (2021-04-20) You'll need the latest freqtrade develop branch otherwise you migh

Robert Davey 137 Jan 01, 2023
LiDAR R-CNN: An Efficient and Universal 3D Object Detector

LiDAR R-CNN: An Efficient and Universal 3D Object Detector Introduction This is the official code of LiDAR R-CNN: An Efficient and Universal 3D Object

TuSimple 295 Jan 05, 2023