One of the major objectives of drug discovery is to identify bioactive molecular structures that could interfere with molecular processes, allowing the possibility of curing a disease.
Drug design, also known as rational drug design, is the strategy of creating new molecules with a particular functionality by predicting how the molecule’s structure will affect its behavior through physical models.
A technique pharmaceutical companies have been using in the drug discovery process is structure-activity relationship (SAR) analysis.
Indeed, it can help you study the relationships between the chemical structure of a molecule and its biological activity.
However, the latest rational drug discovery technique consists of not only analyzing the relationships in the molecule but also building a model that can predict the potency or biological activity of the response variable in relation to a predictor variable.
This method is called quantitative structure-activity relationship or QSAR, and it has allowed researchers to quantify relationships linking chemical structure and pharmacological activity.
QSAR methods have been used to predict the biological activity of new compounds or to optimize the activity of known compounds with molecular modifications.
They can help you better understand biochemical systems involving a molecule so you can develop better drugs while avoiding unwanted side effects.
This is very important because new rational drugs must provide more significant benefits than dangers to be approved for the market.
However, analyzing those patterns and relationships is a problem because of the large amount of data and features the data set would need to handle.
It would also be hard to analyze which features should be interpreted to build the model, so you would need many resources. For those reasons, artificial intelligence is needed in the analysis and systematization of large data sets.
To build a great QSAR model using artificial intelligence, a good quality data set is needed. These are some of the variables that could affect drug designing:
- Structure variables: Shear rate, particle diameters…
- Particle features variables: Adhesive strength, resistance…
- ADME properties: absorption, distribution, metabolism, and excretion. In some cases, other properties like toxicity can be used.
After building the data set, you would need to analyze that data and build the model to design or optimize rational drugs.
Below, you can see the benefits that these machine learning techniques can provide in drug designing processes:
DISCOVER STRUCTURE PATTERNS
Study the surface properties, molecular volumes, or molecular interactions.
IDENTIFY BEHAVIOUR INFLUENCES
Relate the orientation of the molecule to its characteristics.
ANTICIPATE THE CHARACTERISTICS
Develop a predictive model capable of predicting the behavior of a molecule in accordance with its design.
IMPROVE DRUG DESIGN
Get better results and design better medicines while reducing costs.
Artificial intelligence for drug discovery has been applied for decades and will continue to be an important part of drug creation because techniques like neural networks are more effective than traditional clustering and multivariate analysis at predicting biological activities and building SAR/QSAR models.
That is why AI techniques are being used to build SAR/QSAR predictive models. It has been demonstrated to outperform traditional methods in predicting the activity of a molecule from its physicochemical descriptors while simultaneously reducing costs.
It also allows you to perform a feature or input selection to extract the features that affect the most to your model.
To be precise, the method used in recent years to build predictive QSAR models is using neural networks.
That is because ANNs do not require any prior model of how input and output are connected, so they can quickly adapt to non-linear relations and analyze linear ones.
Despite the benefits these practices offer, it is difficult for researchers to have the knowledge it would take to use them. And that is where easy to use tools such as Neural Designer can help you.
Neural Designer is a machine learning software that implements neural networks to analyze data sets. As such, it can be used for very different purposes, including rational drug design.
It allows you to use artificial intelligence approaches without the need to program, thanks to its easy-to-use interface. In addition, it can find complex relations between different variables and manage large amounts of information.
It can also provide you with statistical methods to study the characteristics of the molecules, reduce the dimensionality of the data set, design the predictive model, and test it against real cases to assess its quality.
In this example, you can see the neural network the software has created for predicting the adhesive strength of a nanoparticle.
This article concludes that by using machine learning, you will be able to produce better results in less time while also being more cost-efficient.
- Batool, M., Ahmad, B., & Choi, S. (2019). A structure-based drug discovery paradigm. International journal of molecular sciences, 20(11), 2783.
- Rognan, D. (2007). Chemogenomic approaches to rational drug design. British journal of pharmacology, 152(1), 38-52.