AI can help in drug discovery in four major ways: access to new biology, improved chemistry, better success rates, and a quicker & cheaper discovery process. This in turn will help address many challenges and drawbacks in the traditional Research & Development methods.

AI-native drug discovery companies such as Daten & Wissen offer software services to many Pharmaceutical companies. We use target discovery and validation using graphs and small-molecule design using Neural Networks.

Using AI in traditional drug discovery is still in its early stage, but it is already showing improvements when layered into a traditional process. AI-enabled processes can effectively speed up & improve individual steps, reducing the costs of running expensive experiments.

AI algorithms are used to transform most discovery tasks such as molecule design and testing so physical experiments are only conducted when results are required to be validated.

The lack of advanced technologies present in pharma companies limits the drug development process, making it a time-consuming and expensive task. AI can recognize hit and lead compounds and provides quick validation of the drug target as well as optimize the design of the drug structure.

However, with all the possible advantages, even AI has to face some significant challenges, such as the scale, diversity, growth, and uncertainty of data. Traditional Machine Learning tools are not able to deal with the data sets available for drug development as they consist of millions of data. There is a quantitative structure-activity relationship (QSAR)-based computational model that can quickly predict large numbers of compounds.

Although QSAR-based models are faster in predicting large datasets, they also have some limitations such as small training sets, experimental data errors, and lack of experimental validations. To overcome these challenges, modern AI methods can be implemented for maximum efficacy and safety evaluations of drug molecules.

It is an essential step to assign the correct target while developing the drug for an overall successful treatment. Numerous proteins are also involved in the development of any disease and some are even overexpressed. Hence for performing the selective targeting of disease, it is necessary to predict the structure of the target protein for designing the drug molecule.

AI can predict the 3D structure of the protein and thus can be used for general structure-based drug discovery. This design is similar to the chemical environment of the target protein site, which helps predict the effect of a compound on the target before synthesis or production begins.

There is an AI tool, AlphaFold, which was used to predict the 3D target protein structure of a lab-based drug and resulted in an accuracy of about 60%.

Manual quality control tests are required for drug-based products to maintain batch-to-batch consistency. This process is very inefficient and cannot be applied to all the processes, hence there is a need for AI. A ‘Quality by Design’ approach is taken by Pharma companies to mandate the final quality of the pharmaceutical product.

Implementing AI for regulating in-line manufacturing processes is used to achieve the desired standard of the product. Data Mining and knowledge discovery techniques can be used for making complex decisions and providing intelligent quality control.

AI can be used in the Pharmaceutical field, especially its assisted technology such as Nanorobots. It comprises mainly integrated circuits, sensors, and secure backup of data. These nanorobots are programmed to avoid a collision, detect and attach, target identification, and excretion from the body.

Nanomedicines are a part of modern AI that is used for the diagnosis, treatment, and monitoring of complex diseases, such as Cancer, HIV, Asthma, and other inflammatory diseases. Nanoparticle-modified drug delivery has become important in the field of therapeutics and diagnostics in recent times because of their enhanced efficacy and treatment.

AI can help Pharmaceutical companies develop better and more effective drugs with enhanced screening, testing, and quality check. Although, there are still some established companies that may be susceptible to leveraging modern methods of AI. Adapting traditional drug discovery processes with modern methods of AI can make processes faster and more vividly operational.

Below given are some steps that can be applied by Pharma Companies to adopt AI:

Companies need to develop strategies and procedures to identify specific use cases that are aligned with discovery programs. You should identify a small number of use cases that can be dependable on modern AI methods. These use cases that majorly depend on AI should co-relate to the company's R&D or Financial strategy.

Before initiating the development of the platform, attain a minimum sufficient analysis of the solution that can be used to extract valuable insights from your data in a specific context. If the insights are sufficiently valuable, you can invest in further development of the tool with a friendlier user interface.

Daten & Wissen is a leading AI company that helps businesses achieve their technological goals. We have a team of experts that are on board the wave of new technologies of AI, ML and Neural Networks. No matter what are your business needs, we can always fulfill them by applying our advanced AI methods.