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Drug Design with Machine Learning

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Encyclopedia of Complexity and Systems Science

Definition of the Subject

The process of drug discovery has the goal to identify lead chemicals that have a significant activity against a selected biological target. A disease state may be the result of changes in the structure and function of cell‐signaling receptors, enzymes, hormone receptors, or other functional proteins. The drug target is a protein whose activity is modulated by its interaction with a chemical compound, and thus may control a disease. The lead compounds identified in the drug discovery step are optimized in the drug development phase that results in a small number of chemicals that are evaluated in human clinical trials. The first priority in drug development is to increase the biological activity of a lead compound while preserving its drug‐like properties. The lead compound is expanded into a chemical library that conserves the structure responsible for the biological activity (pharmacophore) and adds chemical groups...

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Abbreviations

Bayesian classifier:

Bayes' theorem of conditional probability is a method of statistical inference that represents the basis of several classification machine learning models used in drug design and chemoinformatics to classify libraries of compounds into active and inactive chemicals. A Bayesian classifier considers each structural feature or descriptor independent of the other descriptors, and the probability that a compound is active is proportional to the ratio of active to inactive compounds that have the same structural feature or have the same value for that descriptor. The final probability that a compound is active is computed as the product of all descriptor‐based probabilities. Structural descriptors that are real numbers are usually binned prior to their evaluation with a Bayesian classifier.

Decision tree:

A decision tree is a sequence of rules applied to selected structural descriptors. The training phase comprises the selection of the structural descriptors that are evaluated, the order in which the rules are applied, and the decision taken at each leaf. Usually, each rules evaluates a descriptor (≥ or < than a threshold) and splits the objects into two or more populations. Then each population is selected and the splitting procedure is performed with a new rule, until a stopping condition is met (for example, when all objects in the population belong to the same class). The prediction phase starts from the root node and evaluates each rule on a pathway determined by the outcome (true or false) of the previous rule. When a leaf is reached the algorithm predicts the class of the object (classification trees) or the numerical value of a property (regression trees).

k‑nearest neighbors:

k‑nearest neighbors (k‑NN) is a supervised learning algorithm that predicts the property of an object based on a local interpolation model. In classification, the class of a new object is predicted based on the majority vote of its k nearest neighbors. In regression, the property value for a new object is predicted as an average value of the property values for its k nearest neighbors.

Lazy learning:

Lazy learning is a memory based local learning that defers the computation until a prediction is requested for an object. The first step is to insert the query object into the space of the training objects, and to identify the training objects located in a set neighborhood. The predicted property of the query object is based on an interpolation of the properties of the objects situated in the neighborhood.

Machine learning:

Machine learning is an important field of artificial intelligence, and includes a diversity of methods and algorithms that extract rules and functions from large datasets, such as decision trees, lazy learning, k‑nearest neighbors, Bayesian methods, Gaussian processes, support vector machines, and kernel algorithms. Machine learning algorithms extract information from experimental data by computational and statistical methods and generate a set of rules, functions or procedures that allow them to predict the properties of novel objects that are not included in the learning set.

Quantitative structure‐activity relationships:

Quantitative structure‐activity relationships (QSAR ) represent regression models that define quantitative correlations between the chemical structure of molecules and their physical properties (boiling point, melting point, aqueous solubility), chemical properties and reactivities (chromatographic retention, reaction rate), or biological activities (cell growth inhibition, enzyme inhibition, lethal dose). The fundamental hypotheses of QSAR are that similar chemicals have similar properties, and that small structural changes result in small changes in property values. The general form of a QSAR equation is \( P(i)=f(\mathbf{SD}_{i}) \), where P(i) is a physical, chemical, or biological property of compound i, \( \mathbf{SD}_{i} \) is a vector of structural descriptors of i, and f is a mathematical function such as linear regression, partial least squares, artificial neural networks, or support vector machines. A QSAR model for a property P is based on a dataset of chemical compounds with known values for the property P, and a matrix of structural descriptors computed for all chemicals. The learning (training) of the QSAR model is the process of determining the optimum parameters of the regression function f. After the training phase, a QSAR model may be used to predict the property P for novel compounds that are not present in the learning set of molecules.

Support vector machines:

Support vector machines (SVM) are a class of supervised machine learning methods based on the structural risk minimization and the statistical learning theory of Vapnik. SVM may be applied to data classification and regression, using selected objects (support vectors) to generate the SVM model. Nonlinear classification problems are transformed into linear classification problems by using kernel functions that combine the input space into a higher‐dimensional feature space in which a hyperplane may discriminate the classes. An SVM classification model computes a maximum margin hyperplane that separates the classes in the feature space. The maximal margin hyperplane maximizes the distance to the hyperplane of the closest patterns from the two classes. An SVM regression model builds a regression tube with the property that all objects inside the tube do not contribute to the overall error of the model. The shape of the regression tube is determined by selected objects (support vectors) situated outside the tube.

Structural descriptor:

A structural descriptor (SD) is a numerical value computed from the chemical structure of a molecule, which is invariant to the numbering of the atoms in the molecule. Structural descriptors may be classified as constitutional (counts of molecular fragments, such as rings, functional groups, or atom pairs), topological indices (computed from the molecular graph), geometrical (volume, surface, charged‐surface), quantum (atomic charges, energies of molecular orbitals), and molecular field (such as those used in CoMFA, CoMSIA, or CoRSA).

Structure‐activity relationships:

Structure‐activity relationships (SAR) represent classification models that can discriminate between sets of chemicals that belong to different classes of biological activities, usually active/inactive towards a certain biological receptor. The general form of a SAR equation is \( C(i) = f(\mathbf{SD}_{i}) \), where C(i) is the activity class of compound i (active/inactive, inhibitor/non‐inhibitor, ligand/non‐ligand), \( \mathbf{SD}_{i} \) is a vector of structural descriptors of i, and f is a classification function such as k‑nearest neighbors, linear discriminant analysis, random trees, random forests, Bayesian networks, artificial neural networks, or support vector machines.

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  1. Department of Biochemistry and Molecular Biology, University of Texas, Medical Branch, Galveston, USA

    Ovidiu Ivanciuc

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  1. RAMTECH LIMITED, 122 Escalle Lane, Larkspur, CA, 94939, USA

    Robert A. Meyers Ph. D. (Editor-in-Chief) (Editor-in-Chief)

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Ivanciuc, O. (2009). Drug Design with Machine Learning. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_135

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