Artificial Neural Networks (ANN) are becoming the tool of choice for many industries due to their ability to replicate the brain’s way of functioning. This includes taking incomplete or unstructured information and making sense of it by detecting the underlying patterns and similarities. It also means connecting the dots and even creating new ideas by rearranging existing information.

The network functions by taking the input in the first layer of neurons, propagating it forward through the hidden layers and regulating it by comparing the expected output with the real one.

What are Neural Networks?

Neural networks are a set of algorithms, they are designed to mimic the human brain, that is designed to recognize patterns. They interpret data through a form of machine perception by labeling or clustering raw input data.

Let’s take a moment to consider the human brain. Made up of a network of neurons, the brain is a very complex structure.

It’s capable of quickly assessing and understanding the context of numerous different situations. Computers struggle to react to situations in a similar way. Artificial Neural Networks are a way of overcoming this limitation.

Why the name neural network?

The neural network algorithm for solving complex learning problems is inspired by the human brain.

Our brains are a huge network of processing elements. It contains a network of billions of neurons.

In our brain, a neuron receives input from other neurons. Inputs are combined and sent to the next neuron.

The artificial neural network algorithm is built on the same logic.

Drug Design

Finding the optimal formula for a specific drug usually takes years of research, trial and error and expensive testing. Not all steps can be eliminated to cut costs significantly, however ANNs can help. Pre-formulation is an essential step in the design of new substances. Having some information related to the properties and behavior of some mixtures and feeding these into the neural network as inputs, trains the system and makes it able to predict the outcomes from other similar substances. As early as the year 2000, some research institutes embraced this approach and had been able to accurately predict substance behavior, in the case of polymers.

Optimization of Pharmaceutic Formulas

It’s not enough to design new drugs; sometimes these have to be optimized for different parameters such as absorption in the bloodstream or active substance release. This idea has already been used for aspirin, ketoprofen, and diclofenac.

Using ANN for optimization solves the problem of multi-objective simultaneous optimization, which is incredibly necessary in the case of drugs which combine more than one active substance and the beneficial effects have to be considered in close relationship with adverse side effects.

In Vitro-In Vivo Correlations

The most critical success indicator in the pharmaceutical industry is the real world replicability of the results predicted by the studies. Neural networks are a great tool for these studies due to their ability to look at multiple variables at the same time. The in vitro predictions of ANN are usually very close to the in vivo results.

One example where this method proved its capacity was focusing on timing the drug clearance time from the patient’s body through the liver function. Another study focused on evaluating the absorption of the active substance in a drug for those with the illness and a healthy control group.

Conclusion -

The idea of using artificial neural networks in the pharmaceutical industry is not a new one. In fact, judging by the number of studies dating back to the mid-90s to early 2000s, it is an idea that has always been around, it has just yet to attention. The current explosion of uses and talk around ANN is putting the necessary emphasis on this tool and stimulating interest from the medical and business communities.

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