Developing drugs is incredibly challenging, and requires a fair bit of capital investment. In fact, it is believed that conducting research, developing a brand-new drug, and introducing it into the market can cost companies anywhere between 1.3 billion USD to 2.9 billion USD.
As the ageing members of our society are prone to a range of diseases such as cardiovascular disease, cancer and dementia, it is crucial for companies to develop drugs that can significantly reduce the impact of these diseases.Unfortunately, as the Covid-19 pandemic has taught us, the process of drug discovery and development is still one with many obstacles along the way.
Though we have the luxury of global connectedness, there was no accounting for the fact that a novel virus can spread rapidly and cause mayhem, well before any drug can even be researched. Issues like placebo effects and slow clinical trials also make it much harder for companies to develop drugs at a faster rate.
So, what is the solution?
Drug discovery services can be sped up by leveraging modern technologies such as deep learning and virtual screening. Deep learning is based on the neural networks of humans, which means that these machines are capable of learning and understanding complex patterns. In fact, as these machines continue to evolve, they can interpret data much faster than human beings. This can significantly speed up the drug discovery process and make it easier for companies to come out with new drugs that can reduce the impacts of cardiovascular diseases, degenerative diseases, and even cancer.
Additionally, deep learning technology can also be used to study the cellular modules of diseases, thereby allowing us to understand newer diseases far faster than any scientist in a lab can. In order to make the process even more efficient, many drug discovery companies are skipping straight to virtual screenings, avoiding assays altogether.
One of the biggest challenges in the field of biology is being able to predict the folded, static structures of proteins (on the basis of their sequence blueprints). However, using deep learning can help identify the same, and even get rid of one of the biggest challenges of the field of biology.
How can you use deep learning in drug discovery?
Drug discovery is basically a process that requires adequate screening of vast chemical libraries for activity. These are screened against a specific phenotype or target molecule. When it comes to adopting virtual screening, a lab can enjoy a more efficient process in total. The new workflow will not be different from the way a data scientist or any deep learning practitioner in other fields works.
Some of the general processes would include filtering, familiar training, test, and validation splits. These processes are mainly used to train and evaluate the model. This is basically done before the machine can deploy an unseen virtual small molecule library. Based on the screening process, there would be certain hits in the library that indicate matches. These will then be checked for the chemical, cellular, and/or model organism assays and then will be entered in the correct clinical trials.
A dataset for virtual screening can be quite vast. In fact, it can be made of a database of chemical and molecular properties. These properties can correspond to specific molecules that have a documented activity for a given target.
There are two main approaches that can be used for successful virtual screening. These are known as ligand based and structure based. The former process identifies the molecular and chemical properties of specific molecules as the data or inputs. It then lets you know whether the compounds will have a specific behaviour when interacting with a target. The second approach requires structure information pertaining to the drug target and the small molecule. It then puts the information in a simulation and lets you know if they will bind.
To know more about how virtual screening can enhance drug discovery services, visit the Biosys website.
