A Python package to process & model ChEMBL data.

Overview

insilico: A Python package to process & model ChEMBL data.

PyPI version License: MIT

ChEMBL is a manually curated chemical database of bioactive molecules with drug-like properties. It is maintained by the European Bioinformatics Institute (EBI), of the European Molecular Biology Laboratory (EMBL) based in Hinxton, UK.

insilico helps drug researchers find promising compounds for drug discovery. It preprocesses ChEMBL molecular data and outputs Lapinski's descriptors and chemical fingerprints using popular bioinformatic libraries. Additionally, this package can be used to make a decision tree model that predicts drug efficacy.

About the package name

The term in silico is a neologism used to mean pharmacology hypothesis development & testing performed via computer (silicon), and is related to the more commonly known biological terms in vivo ("within the living") and in vitro ("within the glass".)

Installation

Installation via pip:

$ pip install insilico

Installation via cloned repository:

$ git clone https://github.com/konstanzer/insilico
$ cd insilico
$ python setup.py install

Python dependencies

For preprocessing, rdkit-pypi, padelpy, and chembl_webresource_client and for modeling, sklearn and seaborn

Basic Usage

insilico offers two functions: one to search the ChEMBL database and a second to output preprocessed ChEMBL data based on the molecular ID. Using the chemical fingerprint from this output, the Model class creates a decision tree and outputs residual plots and metrics.

The function process_target_data saves the chemical fingerprint and, optionally, molecular descriptor plots to a data folder if plots=True.

When declaring the model class, you may specify a test set size and a variance threshold, which sets the minimum variance allowed for each column. This optional step may eliminate hundreds of features unhelpful for modeling. When calling the decision_tree function, optionally specify max tree depth and cost-complexity alpha, hyperparameters to control overfitting. If save=True, the model is saved to the data folder.

from insilico import target_search, process_target_data, Model

# return search results for 'P. falciparum D6'
result = target_search('P. falciparum')

# returns a dataframe of molecular data for CHEMBL2367107 (P. falciparum D6)
df = process_target_data('CHEMBL2367107')

model = Model(test_size=0.2, var_threshold=0.15)

# returns a decision tree and metrics (R^2 and MAE) & saves residual plot
tree, metrics = model.decision_tree(df, max_depth=50, ccp_alpha=0.)

# returns split data for use in other models
X_train, X_test, y_train, y_test = model.split_data()

Advanced option: Use optional 'fp' parameter to specify fingerprinter

Valid fingerprinters are "PubchemFingerprinter" (default), "ExtendedFingerprinter", "EStateFingerprinter", "GraphOnlyFingerprinter", "MACCSFingerprinter", "SubstructureFingerprinter", "SubstructureFingerprintCount", "KlekotaRothFingerprinter", "KlekotaRothFingerprintCount", "AtomPairs2DFingerprinter", and "AtomPairs2DFingerprintCount".

df = process_target_data('CHEMBL2367107', plots=False, fp='SubstructureFingerprinter')

Contributing, Reporting Issues & Support

Make a pull request if you'd like to contribute to insilico. Contributions should include tests for new features added and documentation. File an issue to report problems with the software or feature requests. Include information such as error messages, your OS/environment and Python version.

Questions may be sent to Steven Newton ([email protected]).

References

Bioinformatics Project from Scratch: Drug Discovery by Chanin Nantasenamat

Owner
Steven Newton
"Nobody can do it all but everybody can do something." -Sylvia Earle, marine biologist (Mission-Blue.org)
Steven Newton
Pytorch implementation of "Forward Thinking: Building and Training Neural Networks One Layer at a Time"

forward-thinking-pytorch Pytorch implementation of Forward Thinking: Building and Training Neural Networks One Layer at a Time Requirements Python 2.7

Kim Heecheol 65 Oct 06, 2022
Betafold - AlphaFold with tunings

BetaFold We (hegelab.org) craeted this standalone AlphaFold (AlphaFold-Multimer,

2 Aug 11, 2022
PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021.

PAML PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021. (Continuously updating ) Int

15 Nov 18, 2022
Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

Human-Level Control through Deep Reinforcement Learning Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning. This imp

Devsisters Corp. 2.4k Dec 26, 2022
GeneDisco is a benchmark suite for evaluating active learning algorithms for experimental design in drug discovery.

GeneDisco is a benchmark suite for evaluating active learning algorithms for experimental design in drug discovery.

22 Dec 12, 2022
Implementation for "Domain-Specific Bias Filtering for Single Labeled Domain Generalization"

DSBF Introduction This repository contains the implementation code for paper: Domain-Specific Bias Filtering for Single Labeled Domain Generalization

ScottYuan 7 Jan 05, 2023
Fast methods to work with hydro- and topography data in pure Python.

PyFlwDir Intro PyFlwDir contains a series of methods to work with gridded DEM and flow direction datasets, which are key to many workflows in many ear

Deltares 27 Dec 07, 2022
Code implementation from my Medium blog post: [Transformers from Scratch in PyTorch]

transformer-from-scratch Code for my Medium blog post: Transformers from Scratch in PyTorch Note: This Transformer code does not include masked attent

Frank Odom 27 Dec 21, 2022
Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX

CQL-JAX This repository implements Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX (FLAX). Implementation is built on

Karush Suri 8 Nov 07, 2022
Robust & Reliable Route Recommendation on Road Networks

NeuroMLR: Robust & Reliable Route Recommendation on Road Networks This repository is the official implementation of NeuroMLR: Robust & Reliable Route

4 Dec 20, 2022
Real time sign language recognition

The proposed work aims at converting american sign language gestures into English that can be understood by everyone in real time.

Mohit Kaushik 6 Jun 13, 2022
Degree-Quant: Quantization-Aware Training for Graph Neural Networks.

Degree-Quant This repo provides a clean re-implementation of the code associated with the paper Degree-Quant: Quantization-Aware Training for Graph Ne

35 Oct 07, 2022
An implementation of paper `Real-time Convolutional Neural Networks for Emotion and Gender Classification` with PaddlePaddle.

简介 通过PaddlePaddle框架复现了论文 Real-time Convolutional Neural Networks for Emotion and Gender Classification 中提出的两个模型,分别是SimpleCNN和MiniXception。利用 imdb_crop

8 Mar 11, 2022
A Fast and Accurate One-Stage Approach to Visual Grounding, ICCV 2019 (Oral)

One-Stage Visual Grounding ***** New: Our recent work on One-stage VG is available at ReSC.***** A Fast and Accurate One-Stage Approach to Visual Grou

Zhengyuan Yang 118 Dec 05, 2022
[SDM 2022] Towards Similarity-Aware Time-Series Classification

SimTSC This is the PyTorch implementation of SDM2022 paper Towards Similarity-Aware Time-Series Classification. We propose Similarity-Aware Time-Serie

Daochen Zha 49 Dec 27, 2022
Code to reproduce results from the paper "AmbientGAN: Generative models from lossy measurements"

AmbientGAN: Generative models from lossy measurements This repository provides code to reproduce results from the paper AmbientGAN: Generative models

Ashish Bora 87 Oct 19, 2022
Implementation of Research Paper "Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation"

Zero-DCE and Zero-DCE++(Lite architechture for Mobile and edge Devices) Papers Abstract The paper presents a novel method, Zero-Reference Deep Curve E

Tauhid Khan 15 Dec 10, 2022
keyframes-CNN-RNN(action recognition)

keyframes-CNN-RNN(action recognition) Environment: python=3.7 pytorch=1.2 Datasets: Following the format of UCF101 action recognition. Run steps: Mo

4 Feb 09, 2022
Reproduction of Vision Transformer in Tensorflow2. Train from scratch and Finetune.

Vision Transformer(ViT) in Tensorflow2 Tensorflow2 implementation of the Vision Transformer(ViT). This repository is for An image is worth 16x16 words

sungjun lee 42 Dec 27, 2022
Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies

An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks Novel and high-performance medical ima

14 Dec 18, 2022