Predict Activity of Short Antimicrobial Peptides (QSAR models) Quantitative Structure–activity Relationship Models
| Obj. | Heading |
|---|---|
| 1 | General Information |
| 2 | Purpose |
| 3 | Scope |
| 4 | Acronyms and/or definitions |
| 5 | Project Overview |
| 6 | Functional Requirements and user Impact |
| 7 | Data Resource |
| 8 | Lazypredict |
Predict activity of short antimicrobial peptides.
will be building a machine learning model to predict antimicrobial peptides
Antimicrobial resistance is an urgent and global health problem as existing,
drugs are becoming ineffective against the treatment of antimicrobial infections.
Particularly, we will be retrieving 2 datasets consisting of antimicrobial peptides (positive set) and non-antimicrobial peptides (negative set).
Then, I will be computing some peptide features to quantitatively describe peptides followed by model building.
finally model interpretation where we shed light on the key important features important for predicting antimicrobial peptides.
| Modules | Discription |
|---|---|
| Conda | In which we install packages like python, Our working Environments |
| Pfeature | Pfeature allow us to compute properties of Amino Acid which will be crucial to Quantify the Molecular properties of peptides |
| Jupyter NoteBook/ Colab | Note book |
| CD-Hit from bioconda | A library allows us to fit or out any Redundancy in Peptide Sequence, meaning that peptide that are Much Similar will be removed, So We will get non-redundant and a unique sub set of Peptides that will be using in Molecular sequence |
| Python | For Programing |
| Random Forest classifier | modeling |
| Matplotlip | Graph visualization |
| LazyPredict | Auto ML Library |
I will be computing some peptide features to quantitatively describe peptides followed by model building
Calculate Features Using P features
Featuring most to least featured amino acid
A research paper
In order to apply Quickly compare >30 ML algorithms we used:
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Lazy Predict Helps build a lot of basic models without much code and helps understand which models works better without any parameter tuning.
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There are two classes, LazyClassifier and LazyRegressor, respectively for classifier and regressor.
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We can import the classifier class if your problem is classification, and import regressor if you have a regression problem.
Data split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state =42, stratify=y)
Defines and builds the lazyclassifier clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=matthews_corrcoef)
models_train,predictions_train = clf.fit(X_train, X_train, y_train, y_train)
models_test,predictions_test = clf.fit(X_train, X_test, y_train, y_test)