Quantitative Structure-Activity Relationship (QSAR): A Machine Learning Approach to Understanding Chemical Activity

In this video, we will delve into the world of quantitative structure-activity relationship (QSAR), also known as Q SAR. QSAR is a technique that applies machine learning algorithms to learn the relationship between the chemical structure and biological activity of interest. This process can be visualized through the cartoon illustration of the entire workflow of the QSAR process, which we have drawn in an iPad.

The QSAR process begins with the collection of a data set of molecules. In this example, we have only two molecules: molecule one and molecule two. However, in a practical setting, there will be much more than two molecules, ranging from a hundred to a thousand or even more than that. Each molecule is subjected to calculation of its molecular descriptors, which essentially describe the physical chemical properties that distinguish one molecule from another.

The molecular descriptors are calculated based on the presence and absence of particular molecular features. For instance, in this example, we see that the value is one and zero, representing a binary representation of the presence and absence of a particular molecular feature. This process results in the collection of molecular descriptors for all molecules in the data set, which constitute the data frame that also serves as the data set.

The x-descriptors correspond to the molecular descriptors, while the y-variable corresponds to the biological activity that we want to predict. In this example, a value of 1 indicates that the molecule is active, whereas a value of 0 indicates that it is inactive. This data set will be used to train a machine learning model.

The machine learning model will learn the relationship between the chemical structure and biological activity. Once trained, it can make predictions about molecules with given molecular descriptors. For instance, in this example, the molecule has a value of one, which corresponds to its being active. The QSAR model will be able to provide insights on which features are important for predicting biological activity.

In addition to predicting biological activity, the QSAR model will also provide information on which features are important. This information is crucial for biologists and chemists in designing future molecules with more robust properties. By understanding the relationships between chemical structures and biological activities, scientists can design new compounds that exhibit desired properties.

The process of QSAR is not only about predicting biological activity but also about gaining insights into the underlying mechanisms of molecular interactions. This knowledge can be used to develop new drugs or therapies, leading to improved treatments for diseases. The best way to learn data science is by doing it, and we hope you've enjoyed this journey through the world of QSAR.

As always, if you're finding value in this video, please give it a thumbs up and subscribe if you haven't yet done so. Hit on the notification bell to be notified of our next video, and don't forget to enjoy the journey.

"WEBVTTKind: captionsLanguage: enin this video we're going to be talking about quantitative structure activity relationship or otherwise known by this acronym of qsar qsar is a technique that applies machine learning in order to learn the relationship between the chemical structure and the biological activity of our interest in this schematic diagram that i've drawn in a ipad it shows the cartoon illustration of the entire workflow of the qsar process which entails the collection of a data set of molecules in this example we have molecules one and molecules two and in a practical setting there will be much more than two molecules there could be a hundred a thousand or even more than that each of the molecule as shown here which are the chemical structures will be subjective to calculation of their molecular descriptors which will essentially describe the physical chemical properties that distinguish one molecule from the other in this example we see that it has a value of one and zero which is a binary representation of the presence and absence of a particular molecular feature a collection of the molecular descriptors for all of the molecule in the data set will constitute this data frame which also is the data set the x descriptors that you see here corresponds to the molecular descriptors the y variable corresponds to the biological activity that we want to predict in this example a value of 1 will indicate that the molecule is active while a value of 0 will indicate that it is inactive this data set will be used to train a machine learning model the machine learning model will be able to learn the relationship between the chemical structure and the biological activity so that in a future scenario a molecule with a given molecular descriptors will be applied to the predictive model and the model will make a prediction whether the molecule is active or inactive in this example the molecule has a value of 1 which corresponds to it being active so aside from being able to predict the biological activity the model will be able to provide insights on which features are important and such information will be important for biologists and chemists in their design of future molecules in order to have more robust properties and there you have it a quick explanation of the qsar process and if you're finding value in this video please give it a thumbs up subscribe if you haven't yet done so hit on the notification bell in order to be notified of the next video and as always the best way to learn data science is to do data science and please enjoy the journeyin this video we're going to be talking about quantitative structure activity relationship or otherwise known by this acronym of qsar qsar is a technique that applies machine learning in order to learn the relationship between the chemical structure and the biological activity of our interest in this schematic diagram that i've drawn in a ipad it shows the cartoon illustration of the entire workflow of the qsar process which entails the collection of a data set of molecules in this example we have molecules one and molecules two and in a practical setting there will be much more than two molecules there could be a hundred a thousand or even more than that each of the molecule as shown here which are the chemical structures will be subjective to calculation of their molecular descriptors which will essentially describe the physical chemical properties that distinguish one molecule from the other in this example we see that it has a value of one and zero which is a binary representation of the presence and absence of a particular molecular feature a collection of the molecular descriptors for all of the molecule in the data set will constitute this data frame which also is the data set the x descriptors that you see here corresponds to the molecular descriptors the y variable corresponds to the biological activity that we want to predict in this example a value of 1 will indicate that the molecule is active while a value of 0 will indicate that it is inactive this data set will be used to train a machine learning model the machine learning model will be able to learn the relationship between the chemical structure and the biological activity so that in a future scenario a molecule with a given molecular descriptors will be applied to the predictive model and the model will make a prediction whether the molecule is active or inactive in this example the molecule has a value of 1 which corresponds to it being active so aside from being able to predict the biological activity the model will be able to provide insights on which features are important and such information will be important for biologists and chemists in their design of future molecules in order to have more robust properties and there you have it a quick explanation of the qsar process and if you're finding value in this video please give it a thumbs up subscribe if you haven't yet done so hit on the notification bell in order to be notified of the next video and as always the best way to learn data science is to do data science and please enjoy the journey\n"