afikhan Goto Github PK

a_python_2d_qsar_model

Based on Support Vector Machines (SVM), Morgan's circular Fingerprints to predict PIC50 values

amlp

Approaching (Almost) Any Machine Learning Problem

application-of-logistic-regression-svm-and-knn-classifiers-using-gridsearch-for-model-tuning

This notebook explains on working with Classification algorithms Logistic Regression, Support Vector Machine and KNN using GridSearch for model tuning on iris dataset..

blood-brain-barrier

Predicting the ability of chemical species to cross the blood−brain barrier (BBB) is an active field of research for development and mechanistic understanding in the pharmaceutical industry. Here, we report the BBB permeability of a large data set of compounds by incorporating molecular solvation energy descriptors computed by the 3D-RISM-KH molecular solvation theory.

chemobinder

chemoinformatics

chemoinformatics-1

Chemoinformatics projects

chemx

Chemical Database Expander. For a given target compound, it generates a virtual chemical bank of analogues by replacing the substructures of target compound with those found in other synthetic molecules.

cheto

CheTo - Chemical Topic Modeling

code

Compilation of R and Python programming codes on the Data Professor YouTube channel.

deepchem

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

deeppurpose

A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)

descriptor-selection-for-predicting-interfacial-thermal-resistance-by-machine-learning-methods

espsim

Scoring of shape and ESP similarity with RDKit

estrogen-receptor-alpha-qsar

Repository of codes and data for Estrogen Receptor Alpha QSAR modeling

kallisto

Efficiently calculate 3D-atomic/molecular features for quantitative structure-activity relationship approaches.

lib-invent

machine-leaning-in-examples

Contains notebooks which can explain machine learning problem on examples

machine-learning

machine-learning-1

Código Python, Jupyter Notebooks, archivos csv con ejemplos para los ejercicios del Blog aprendemachinelearning.com y del libro Aprende Machine Learning en Español

machine-learning-classification-regression-twitter-buzz

Buzz Prediction on Twitter: Buzz Prediction on Twitter Project Description: There are two different datasets for Regression and Classification tasks. Right-most column in both the datasets is a dependent variable i.e. buzz. Data description files are also provided for both the datasets. Deciding which dataset is for which task is part of the project. Read data into Jupyter notebook, use pandas to import data into a data frame. Preprocess data: Explore data, check for missing data and apply data scaling. Justify the type of scaling used. Regression Task: Apply all the regression models you've learned so far. If your model has a scaling parameter(s) use Grid Search to find the best scaling parameter. Use plots and graphs to help you get a better glimpse of the results. Then use cross-validation to find average training and testing score. Your submission should have at least the following regression models: KNN regressor, linear regression, Ridge, Lasso, polynomial regression, SVM both simple and with kernels. Finally, find the best regressor for this dataset and train your model on the entire dataset using the best parameters and predict buzz for the test_set. Classification Task: Decide about a good evaluation strategy and justify your choice. Find best parameters for the following classification models: KNN classification, Logistic Regression, Linear Support Vector Machine, Kernelized Support Vector Machine, Decision Tree. Which model gives the best results? Buzz Prediction on Twitter Project Description: Use same datasets as Project 2. Run all the models only on 10% data. Use code given in Project 2 for sampling. Preprocess data: Explore data and apply data scaling. Regression Task: Apply any two models with bagging and any two models with pasting. Apply any two models with adaboost boosting Apply one model with gradient boosting Apply PCA on data and then apply all the models in project 2 again on data you get from PCA. Compare your results with results in project 2. You don't need to apply all the models twice. Just copy the result table from project 2, prepare similar table for all the models after PCA and compare both tables. Does PCA help in getting better results? Apply deep learning models covered in class Classification Task: Apply four voting classifiers - two with hard voting and two with soft voting Apply any two models with bagging and any two models with pasting. Apply any two models with adaboost boosting Apply one model with gradient boosting Apply PCA on data and then apply all the models in project 2 again on data you get from PCA. Compare your results with results in project 2. You don't need to apply all the models twice. Just copy the result table from project 2, prepare similar table for all the models after PCA and compare both tables. Does PCA help in getting better results? Apply deep learning models covered in class

machine-learning-deep-learning-resources

Completely free access list of resources to learn machine learning and deep learning👨🏻‍💻🚀

mdbenchmark

Quickly generate, start and analyze benchmarks for molecular dynamics simulations.

mdqsar-imatinib

Scripts used to build the dataset used for the MD QSAR analysis.

ml-cookbook

Machine learning notebooks and code used for demonstration purposes

ml-multiclass-classification-with-gridsearchcv-and-10-fold-cross-validation

ML MultiClass Classification with GridSearchCV and 10 Fold Cross-Validation ( Regularised Logistic Regression, KNN, SVM,

mmpdb

A package to identify matched molecular pairs and use them to predict property changes.

molecular-descriptors

see README.md

molecular-mechanics-and-3d-descriptors

mordred

a molecular descriptor calculator