Machine Learning Methods
Since a few years we see a remarkable hype about machine learning and artificial intelligence in all sectors triggered by a successful renaissance of convolutional neural networks in the field of image recognition. In just a few years before 2020 about 200 startup companies have been founded applying 'artificial intelligence' for drug discovery. This development is fueled by large amounts of money invested by pharmaceutical companies being afraid of missing the departing train. In some cases QSAR approaches, which are established and applied since two decades, are now renamed to artificial intelligence and successfully commercialized as expensive panacea for the future. At the same time new promising approaches arise, e.g. the usage of deep neural networks to quickly and accurately estimate quantum chemical energies and forces.
Machine learning methods fall into two broad categories, unsupervised and supervised ones. Unsupervised methods take properties of the data as input information and infere that there are multiple different classes without knowing anything else from the data. Or they may arrange objects in multiple dimensions according to perceived object similarity in a linear or non-linear fashion. These unsupervised learning algorithms have a wide range of applications and solve real world problems such as anomaly detection, document grouping, patient segmentation, or finding compounds with common properties. The following methods fall into the unsupervised learning category.
Principal Component Analysis
The Principal Component Analysis (PCA) is a widely used technique to convert a large number of highly correlated variables into a smaller number of less correlated variables. De-facto it does a coordinate transformation and re-positions the first axis of the coordinate system such that it perceives the maximum possible variance of the multidimensional data. Then the second axis is positioned orthogonal to the first one such that it perceives the maximum possible variance of the remaining dimensionality. The third axis is again positioned orthogonal to the first two also maximizing the perceived variance of the remaining data and so forth. In reality, to not overvalue those dimension with the highest numerical values, DataWarrior normalizes and centers the values of every input dimension before applying the coordinate transformation.
Often the first dimensions (or components) of a PCA cover much of the variability of the data. This is because in reality many dimensions of multi-dimensional data are often highly correlated. In chemical data sets, for instance, carbon atom count, ring count, molecular surface, hetero atom count are all highly correlated with the molecular weight and therefore almost redundant information. Often the first two or three dimensions taken from a PCA can be used to position the objects on a plane or in space such that clusters of similar objects can easily be recognized in the view.
Since many chemical descriptors are nothing else then binary or numerical vectors, one can consider the n dimensions of these vectors as n parameters of the data set. Therefore, the binary and SkeletonSpheres descriptors can be selected as parameters to the PCA. This allows to visualise chemical space, if the first calculated components are assigned to the axes of a coordinate system. In the example above a dataset of a few thousand Cannabinoid receptor antagonists from the ChEMBL database was used to calculate the first three principal components from the SkeletonSpheres descriptor. These were assigned to the axes of a 3D-view. The dataset was clustered with the same descriptor joining compounds until cluster similarity reached 80%, forming more than 300 clusters. Marker colors were set to represent cluster numbers. The distinct areas of equal color are evidence of the chemical space separation that can be achieved by a PCA, even though the dataset consists of rather diverse structures.
t-SNE Visualization
T-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for visualization developed by Laurens van der Maaten and Geoffrey Hinton. It is a nonlinear dimensionality reduction technique well-suited for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions. Specifically, it models each high-dimensional object by a two- or three-dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability.
L.J.P. van der Maaten. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research 15 (2014), 3221-3245.
Typical visualization of a 2-dimensional t-SNE plot with about 20.000 data points
The t-SNE calculation used in DataWarrior is based on the Java code developed in 2016 by Leiff Jonsson at Delft University of Technology. The source code is publicly available at https://github.com/lejon/T-SNE-Java.
If you choose many input dimensions, especially if you select a chemical descriptor as input, than DataWarrior may use a principal component analysis (PCA) to reduce the dimensionality to a lower number before applying the t-SNE algorithm. The parameter defines the maximum number of dimensions passed to the t-SNE algorithm. If your selected source columns contain more dimensions than this parameter, than a PCA is performed. Please note that a chemical descriptor alone typically contains 512 or even 1024 dimensions.
DataWarrior uses the Barnes-Hut implementation with a default perplexity value of 20.0 and a maximum of 1000 iterations. In addition to the number maximum number of t-SNE input dimensions, the perplexity value and the number of iterations can be modified. A good overview of what these parameters mean and how they influence the outcome is available from Martin Wattenberg et al.
UMAP Visualization
UMAP (Uniform Manifold Approximation and Projection) is dimension reduction algorithm that is often used as alternative to t-SNE because of various advantages. It was first described by L. McInnes, J. Healy, and J. Melville in https://doi.org/10.48550/arXiv.1802.03426. UMAP is summarized by the authors as "UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning."
As t-SNE, UMAP can be used visualize multi-dimensional space, which in the case of DataWarrior can be chemical space. Thus, all chemical descriptors, which are vectors, can be used as input dimensions in addition to or without any other numerical columns. DataWarrior uses the UMAP implementation for Java by Sean A. Irvine and Richard Littin.
2D-UMAP visualization of >3000 molecules using the SkeletonSpheres descriptor, colored by structure classes
The algorithm's parameters, which are available to the user, are as follows:
Nearest neighbors Larger values result in more global views of the manifold, while smaller values result in more local data being preserved. Generally in the range of 3 to 1000. The reported default is 15, but it seems that in the context of molecules larger values often give better results.
Target dimensions is typically 2 or 3 to provide easy visualization, but can reasonably be set to any integer value in the range of 2 to 100.
Minimum distance influences the desired separation between close points in the embedding space. Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even disperse of points. The value should be smaller than 1.0, which is the algorithm's spread value that determines the scale at which embedded points will be spread out. The reported default value is 0.1, but larger values seem to achieve better local resolution. Therefore, we use a default of 0.5.
Metric controls how distance is computed in the ambient space of the input data. By default, UMAP uses an euclidian metric.
Self-Organizing Maps
A Self-Organizing Map (SOM) also called Kohonen Map is a popular and robust artificial neural network algorithm to organize objects based on object similarity. Typically, this organization happens in 2-dimensional space. For this reason SOMs can be used well to visualize the similarity relationsships of hundreds or thousands of objects of any kind. Historically, small SOMs (e.g. 10*10) were used in cheminformatics to cluster compounds by assigning similar compounds to the same neuron. A typical visualization showed the grid with every grid cell colored to represent number of cluster members or an averidge property value. In order to visualize individual compounds of the chemical space DataWarrior uses many more neurons and adds a sub-neuron resolution explained in the following.
Similar to the PCA, the SOM algorithm uses multiple numerical object properties,
e.g. of a molecule, which together define an object specific n-dimensional vector.
The vector may either consist of numerical column values
or - as with the PCA - it may be a chemical descriptor. These object describing vectors are called
input vectors, because their values are used to train a 2-dimensional grid of equally sized,
but independent reference vectors (also called neurons).
The goal of the training is to incrementally optimize the values of the reference vectors
such that eventually
- every input vector has at least one similar counterpart in the set of reference vectors.
- adjacent reference vectors in the rectangular grid are similar to each other.
Cannabinoid receptor antagonists on a SOM with 50x50 neurons
The Cannabinoid receptor antagonists, which have been used in the PCA example, were
arranged on a SOM considering the SkeletonShperes descriptor as similarity criterion.
The background colors of the view visualize neighborhood similarity of adjacent neurons
in landscape inspired colors. Blue to green colors indicate valleys of similar neurons,
while yellow to orange areas show ridges of more abrupt changes of the chemical space
between adjacent neurons. Colored markers are located above the background landscape
on top of those neurons, which represent the compound descriptors best. This way very
similar compounds huddle in same valleys, while slightly different cluster are separated
by yellow ridges.
Please note that DataWarrior uses sub-neuron-resolution to position object on
the final SOM. After assigning an object to the closest reference vector, any object's
position is fine-tuned by the object's similarity to the adjacent neurons.
Comparing the SOM to the PCA concerning their ability to visualize chemical space, SOMs have these advantages: SOMs use all available space, while PCAs leave parts of the available area or space empty. The SOM takes and translates exact similarity values, while the PCA uses two or three principal components only to separate objects and, therefore, may miss some distinction criteria. High compound similarity is translated well into close topological neighborhood on the SOM. However, vicinity on the SOM does not necessarily imply object similarity, because close neurons may be separated by ridges and represent different areas of the chemical space, especially if the number of reference vectors is low. One may also check how well individual objects match the reference vectors they are assigned to. For this purpose DataWarrior calculates for every row a SOM_Fit value, which is the square root of the dissimilarity between the row's input vector and the reference vector the row is assigned to. As a rule of thumb, SOM_Fit values below 0.5 are a good indication of a well separating SOM.
The SOM algorithm is a rather simple and straightforward one. First the input vectors
are determined and normalized to avoid distortions of different numerical scales.
The number of values stored in each input vector equals the number of numerical columns
selected or the number of dimensions of the chosen descriptor.
Then a grid of n*n reference vectors is initialized with random numbers.
The reference vectors have the same dimensionality as the input vectors.
Then a couple of thousand times the following procedure is repeated:
In input vector is selected randomly. That reference vector is located, which
is most similar to the input vector. This reference vector and the reference vectors in
its circular neighborhood are modified to make them a little more similar to
the selected input vector. The amount of the modification decreases with increasing
distance from the central reference vector and it decreases with the number of
modification rounds already done. This way a coarse grid structure is formed quickly,
while the more local manifestation of fine-granular details takes the majority of the
optimization cycles.
Parameters used: The columns selected here define the similarity criterion between objects or table rows. All values are normalized by the variance of the column. The normalized row values of all selected columns form the vector, which is used to calculate an euclidian similarity to other row's vectors. If a chemical descriptor is selected here, the SOM uses respective chemical similarities and, thus, can be used to visualize chemical space.
Neurons per axis: This defines the size of the SOM. As a rule of thumb the total number of neurons (square the chosen value) should about match the number of rows. A highly diverse dataset will require some more neurons, while a highly redundant combinatorial library will be fine with less.
Neighbourhood function: This selects the shape of the curve, which defines how the factor for neuron modification factor decreases with distance from the central neuron. The typical shape is a one, which usually causes smooth SOMs.
Grow map during optimization: Large SOMs take quite some time for the algorithm to finish. One way of reducing the time is to start out with a much smaller SOM and double the size of each axis three time during the optimization. If this option is checked, the map starts with one eightht of the defined neurons per axis.
Create unlimited map: If this option is checked, then the left edge neurons of the grid are considered to be connected to the respective right edge neurons and top edge neurons are considered to be connected to bottom edge neurons. This effectively converts the rectangular area to the surface of a torus, which has no edges anymore and, therefore, avoids edge effects during the optimization phase.
Fast best match finding: A large part of the calculation time is spent on finding the most similar neuron to a randomly picked input vector. If this option is checked, then best matching neurons are cached for every input vector and are used in later as best match searches as starting point. This assumes that a path with steadily increasing similarity exists from the previous best matchin neuron to the current one.
Show vector similarity landscape in background: After finishing the SOM algorithm, DataWarrior creates a 2-dimensional view displaying the objects arranged by similarity. If this option is checked, a background picture is automatically generated, which shows the neighbourhood similarity of the SOMs reference vectors in colors inspired by landscape. Blue, cyan, green, yellow, orange and red reflect an increasing dissimilarity between adjacent neurons. Markers in the same blue or green valley belong to rather similar objects.
Use pivot table: This option allows to pivot the data on the fly. Imagine a dataset where gene expression values have been determined for a number of genes in some different tissues. The results are stored in three columns with gene names, expression values, and tissue types. For you would select Tissue Type and for Gene Name. DataWarrior would then convert the table layout to yield one Tissue Type column and one additional column with expression values for every gene name. The generated SOM would then show similarities between tissue types considering expression values of multiple genes.
Save file with SOM vectors: Use this option to save all reference vectors to a file once the optimization is done. Such a file can be used later to map objects from a different dataset to the same map, provided that they contain the properties or descriptor used to train the SOM. One might, for instance, train a large SOM from many diverse compounds containing both, mutagenic and non-mutagenic structures. If one would use the SOM file to later map a commercial screening library, one could merge both files and show toxic and non toxic areas by coloring the background of a 2D-view accordingly. The foreground might show the compounds of the screening library distributed in toxic and non-toxic regions.
Assessing A Machine Learning Method's Predictivity
The unsupervised methods above were used to reduce the dimensionality of data, to classify or cluster rows based on feature commonalities, or simply to visualize the data space in two dimensions. This section now uses supervised machine learning methods, to build and train models from existing molecules and their properties. These models shall then be used to predict the same properties for new molecules. For this purpose DataWarrior uses a support vector machine (SVM), the k-nearest neighbours (kNN) approach, a partial least squares (PLS) regression, or the Power-PLS, which is a PLS modification that supports non-linear relationships.
Common to these methods is that they all build a model from given numerical X- and Y-values. Later this model can be used to predict Y-values from new X-values. X-values in this regard are multiple alpha-numerical or purely numerical values that describe the object and the Y-value (also called label) is an object property that is either used for model learning or is predicted by the final model for objects that where not used during training. In the case of drug discovery the object might be a molecule, X-values might be some structure descriptors and the Y-value might be some experimental value, e.g. a physico-chemical property or a biological effect.
Evident pitfalls for applying machine learning methods in drug discovery for the prediction
of biological effects of chemical structures are manyfold:
In fields outside of drug discovery it is common to assess the predictivity of a machine learning method in two steps. First a random subset of data is taken to train a model from X- and Y-data. Then, the model is used to predict Y-values for the remaining data. The correlation between predicted and known Y-values gives a measure for the model's predictivity for new data. This works well, if the data used is evenly distributed in X-value space and if new unknown objects can also be placed within the boundaries of the X-value training space. For typical drug discovery datasets both conditions are not given. Molecules tend to come in clusters of very similar structures and new molecules are often very different from the ones used before. Therefore, a random cross-validation from molecules with known properties results in over-optimistic correlations, because a random selection ensures that every test set molecule has few very similar ones in the training set, originating from the same cluster. More realistic results are achieved, if molecules are ordered along the time axis, e.g. using the synthesis date. If then all molecules before a certain date are used for model training and the newer ones for model validation, one may hope that obtained correlation values can be repeated with the next set of molecules being synthesized for the same project.
DataWarrior offers a convenient way to quickly assess, whether a certain machine learning method may achieve a reasonable predictivity for a given property, using a specific descriptor in a certain chemical space. For this it uses a time-based model validation as decribed above. The dataset is divided into ten fractions along the time axis. A model is build with the first fraction and used to predict the property of the second fraction's molecules. Then a second model is built from the first two fractions, which is then applied to predict the third fraction. This continues until the nineth model is built from the first nine fractions and used to predict the tenth and last fraction. Correlation plots are generated for all nine models, which show known versus predicted Y-values along with the calculated correlation coefficient. The correlation plots and coefficients provide a solid basis to decide, whether it makes sense to employ the chosen descriptor and method to predicts the property for new molecules.
To start the assessment for a given dataset first make sure that you have generated the descriptor, which you intend to use and then select from the menu. The following dialog opens.
Structure column and Descriptor: Here you select the descriptor type of the chemical structure column that serves as independent variable to build the machine learning model.
Value column: This selects the dependent variable, i.e. the property that is used to train the model and that shall be predicted later.
Prediction method: This is the machine learning method that is used. DataWarrior supports four well known methods. The kNN regression method is a simple, but robust method that locates the most similar neighbors and creates a weighted mean value as prediction. The PLS (partial least squares) method is basically a multi-linear regression, which expects a linear dependency of the predicted variable and normality of the data. The Power-PLS method is a PLS with a preceeding Box Cox transformation, which opens the PLS for nolinear relationsships and non normal distributed data. The SVM (support vector machine) is a robust mathematical approach for non linear data that was believed for a long time to outperform any multi-layered neural networks. More detailled descriptions of the methods are beyond the scope of this manual, but can be easily found on the internet.
Method parameters: After selecting a method this text field contains default parameter settings for the method. Typically, these are not touched. However, if you know, what you are doing, you may fine-tune the method by changing individual parameters here.
Time, date, or ID-column: As described above, all rows are assigned to the ten data fractions according to their position on the time axis. Thus, here a column should be selected that ideally contains the creation or measurement date of the value. Often this is not available and the synthesis date of the molecule or even its ID can be used as a proxy.
Use random fractions instead of time based ones: This option causes random assignments of rows to the ten fractions neglecting any selected time column. Since random cross-validations produce over-optimistic correlations for chemical property predictions, this option should be used with care.
After clicking the dialog closes, the data is devided into ten fractions, nine prediction models are generated and used to predict the dependent variable. A view is created that shows nine correlation plots, one for every model.
Looking at these plots it is up to the scientist to decide, whether the achieved correlation is sufficiently high to serve a a useful guidence in a drug discovery project.
Predicting Missing Values
If a data set with molecules contains numerical values for some of the molecules, then DataWarrior can easily predict the missing values using a supervised machine learning method and a chosen descriptor of the molecule. While it is easy to generate numbers this way, it is advisable before predicting values to assess, whether the chosen machine learning method within the given molecular structure space is able to achieve a predictivity that is sufficiently above random guessing, which is described in the previous section.
In order to predict missing values, simply select from the menu. A dialog opens that lets you choose a chemical structure column, a descriptor, a column that has missing numerical values, and a machine learning method. For a descriptor to show up in the list, it must have been calculated before and it must be a vector, i.e. it must be one of these: FragFp, PathFp, ShpereFp, SkeletonSpheres. As described in the previous section, every machine learning method has some parameters, that influence the algorithm. DataWarrior uses default parameters, which have been proven to work reasonably well, but if you know what you are doing, you may experiment with the parameters by adapting them in the dialog.
When the dialog is closed by pressing , DataWarrior builds a model form the selected descriptor and existing values in the chosen value column. Then it applies the model to fill all empty cells in the values column with predicted values using the model and the structure descriptors of the respective rows.
Continue with Chemical Structures...