A technique widely used by pharmaceutical companies in the drug discovery process is called structure-activity relationship (SAR) analysis.
Machine learning has demonstrated to outperform traditional methods in predicting the activity of a molecule from its physicochemical descriptors.
The general protocol for constructing SAR models for drug discovery consists of several modular steps involving chemoinformatics and machine learning techniques. Several variables are considered:
The first step is deriving the chemical features and properties from chemical structures.
Second, feature selection is performed to identify the most relevant properties and reduce the dimensionality of the feature vector.
Finally, in the learning phase, a neural network model is applied to discover a function that can achieve an optimal mapping.
The next diagram summarizes this process.
The recent success of machine learning in the biomedical field provides an opportunity to develop tools for automatically extracting task-specific representations of chemical structures.
Study the surface properties, molecular volumes or molecular interactions.
Relate the orientation of the molecule to their characteristics.
Develop a predictive model capable of predicting the behavior of a molecule in accordance to their design.
Get better results and design better medicines while reducing the costs.
Neural Designer is a machine learning tool that implements neural networks for the analysis of data sets.
It can find complex relations between different variables and manage large amount of information.
The information that it receives is arranged in a data set which contains the information about the structure of the molecules.
Neural Designer also provides you with the statistical methods to study the characteristics of the molecules, reduce dimensionality of the data set, design the predictive model and test it against real cases to assess its quality.