PHAKISO - Pharmacokinetics In Silico

Release Notes


This is the official website of YMLL, a machine learning library, and PHAKISO, a Windows software based on YMLL. YMLL contains algorithms that are essential for performing a Quantitative Structure Pharmacokinetics Relationship (QSPkR) experiment. PHAKISO provides a graphical user interface to the algorithms in YMLL so that a QSPkR model can be developed and validated easily with just a few mouse clicks.

YMLL contains different modules which interact with one another to help develop a QSPkR model. The modules in YMLL are Dataset, DataLoad, DataSave, DatasetSplit, DatasetCluster, DiversityMetric, Outlier, Machine, DescriptorFilter, DescriptorSelection, Scale, DistanceMeasurer, PerformanceMeasurer, Reporter, ObjectiveFunction and Trainer. Each module defines a standard interface to interact with other modules. The standardization of a module’s interface enables different algorithms in the same module to work seamlessly with those in other modules and allow new algorithms to be easily added. For example, to conduct a simple QSPkR experiment, we simply link the Dataset, DataLoad, Machine, PerformanceMeasurer and Reporter modules together. These modules will load a dataset into memory and pass to a machine learning algorithm to develop a QSPkR model. The prediction capability of the QSPkR model is then gauged and reported to the user. The programmer can choose different algorithms from the three different modules and the different algorithms are guaranteed to work with one another since they have to conform to the standard interface that is defined by their module.

Both YMLL and PHAKISO are coded in C++. The source codes are currently not available because of certain proprietary algorithms that were developed by the BIDD group. However, precompiled libraries of YMLL for various systems and the executable for PHAKISO are available freely on this website for non-commercial uses.


YMLL Features

Machine Learning Methods
- Multiple linear regression
- Logistic regression
- Partial least squares
- Linear discriminant analysis
- C4.5 decision tree
- C4.5 decision rules
- k nearest neighbour
- Feedforward backpropagation neural network (Own implementation, AnnieNN, TorchMLP)
- Probabilistic neural network
- Support vector machine (SVMStar, SVMlight, LibSVM, SVMTorch)
- Sphere discriminant (experimental)
- Multiple linear regression
- Principal component regression
- Partial least squares
- Continuum power regression
- Continuum regression
- Feedforward backpropagation neural network
- General regression neural network
- Support vector regression (SVMlight, LibSVM, SVMTorch)


Dataset Input/Output Formats
- Comma separated value (CSV)
- Microsoft Excel
- Extensible markup language (XML)
- SVMlight
- Torch
- Weka (ARFF)


Dataset Clustering Algorithms
- Nearest neighbour
- Furthest neighbour
- Centroid
- Group average
- Median
- Ward
- Flexible
- Method proposed by Darko Butina
- K means
- Flexible K means


Dataset Outliers Detection Algorithms
- Hadi
- Iterative R
- Iterative Z
- Median


Statistical Molecular Design Algorithms
- Removal-until-done
- D-optimal
- Sphere exclusion
- Maximum dissimilarity
- Every N datum
- Random


Dataset Diversity Measurement Methods
- Average nearest neighbour
- Mean intermolecular dissimilarity
- Cumulative property distribution


Descriptors Scaling Algorithms
- Autoscale
- Range scale (Normalization) (0 to 1, -1 to 1)
- Log scale (natural log, base 10)
- Mean scale
- Variance scale


Descriptor Selection Algorithms
Filter methods
- Discrimination score
Wrapper methods
- Forward selection
- Backward elimination
- Stepwise regression
- Sequential floating forward selection
- Generalized simulated annealing
- Genetic algorithm
- Reverse elimination tabu search
- Recursive feature elimination


Validation Methods
- Training set
- Testing set
- Leave-one-out
- k-fold cross-validation
- Bootstrap
- Y-randomization


Model Performance Measurement Methods
- Sensitivity
- Specificity
- Concordance
- Matthews correlation coefficient
- Cohen Kappa coefficient
- Error rate
- Absolute error rate
- Relative error rate
- Correlation coefficient (r)
- Coefficient of determination (r2)
- Adjusted coefficient of determination (r2adj)
- Mean absolute error (MAE)
- Mean square error (MSE)
- Root mean square error (RMSE)
- Pearson correlation coefficient
- Pearson r2
- Spearman rho
- Average fold error
- Standard deviation
- F ratio
- F statistics
- Model sum of squares
- Residual sum of squares


Activation functions for feedforward backpropagation neural network
- Linear
- Logistic
- Hyperbolic tangent
- Gaussian
- Sigmoid (0 to 1, -1 to 1)
- Logarithm
Data distance/similarity measurement
- Euclidean distance
- Manhattan distance
- Soergel distance
- Gaussian distance
- Quadratic distance
- Tophat distance
- Triangular distance
- Tanimoto coefficient
- Dice coefficient
- Cosine coefficient
- Pearson correlation coefficient


PHAKISO Features

Standard Features
- Measurement of dataset diversity (screenshot)
- Determination of compound clusters in dataset (screenshot)
- Determination of outliers in dataset (screenshot)
- Statistical molecular design (screenshot)
- Y-randomization of dataset
- Scaling of descriptors (screenshot)
- Objective descriptor selection (Filter methods) (screenshot)
- Subjective descriptor selection (Wrapper methods) (screenshot)
- Construction of a QSPkR model (screenshot)
- Optimization of parameters for machine learning methods (screenshot)
- Assess prediction capability of QSPkR models on other datasets (screenshot)
- Validation of QSPkR model (screenshot)


Additional Features (Not available in YMLL)
- Display information on descriptors (mean, standard deviation, minimum and maximum values, etc) (screenshot)
- Automatic filling in of values for descriptors with missing values (screenshot)
- Principal component analysis (screenshot)






What's  New

  • 2006 October 12 - Bug fix for YMLL
  • 2006 October 12 - Bug fix for PHAKISO
  • 2006 August 25 - Bug fix for YMLL
  • 2006 August 25 - Bug fix for PHAKISO
  • 2006 April 24 - YMLL version 1.0 released
  • 2006 April 24 - PHAKISO version 0.5 released

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Last updated: 05/08/06.