Artificial Intelligence (AI) is no longer a futuristic concept. It has now become a practical tool that is used across numerous industries to speed up processes and make them more efficient for the smooth functioning of businesses. AI is a field of science that involves the simulation of human intelligence processes by machines, especially computer systems. It emphasizes on creating intelligent machines that work and react like humans. The advances in artificial intelligence sphere have successfully propagated into various areas such as speech recognition, computer vision, and natural language processing. This novel trend has paved the way for a wave of academic publications in the domain of AI-powered drug discovery. The pharmaceutical industry is leveraging AI to revolutionize the drug development process and brighten the future of AI. Let us now analyze how AI is used in drug discovery.
In general, AI can be applied in drug discovery in the following areas:
- Virtual screening.
- Knowledge discovery and hypothesis generation.
- Target identification.
- Compound generation.
- Predicting the outcomes of clinical trials.
- Personalized medicine.
- Real-world evidence analysis.
- Understanding disease mechanisms.
- Generating novel drug candidates.
On a more specific level, AI is used in the drug discovery domain in the following areas:
1. AI reduces drug discovery timelines
As we know, drug discovery is a lengthy and expensive process. Patients are often required to wait a decade to get access to medication that will help them. This results in inefficiency. With cancer and autoimmune diseases being on the rise, there is certainly a need for improvement in the field of pharmaceutical research and drug development. These challenges have paved way for Exscientia, an AI-driven drug discovery company has signed a three-year partnership with Celgene, a biopharma company. This partnership will help Exscientia to apply its AI technology to drug research and aid Celgene in speeding up the development of new drugs in the areas of autoimmunity and oncology. This will be achieved using Exscientia’s drug discovery platform called Centaur Chemist, which has proved its capabilities on other projects and has also demonstrated its ability to reduce drug discovery timelines by three quarters. This platform analyzes large databases for predicting the drug compounds that will have the desired effect without major side effects. Cutting-edge technologies such as artificial intelligence help reduce the cost, speed, and risks involved in the drug development process. AI-powered programs speed up data analysis. AI aids in the affordable development of drugs and increased success rates for pharmaceutical companies.
2. Augmented intelligence for enhancing computational chemistry
The best application of artificial intelligence to drug discovery is an offshoot of augmented intelligence, a term coined by IBM. Augmented intelligence refers to applying AI methods using machine learning and big data to improve computational chemistry and other non-AI algorithms and information. Augmented intelligence combines the best tools of both AI and computational chemistry and shines in cases where data is limited or inconsistent. Artificial intelligence and machine learning tools can be combined with other tools to build a drug design platform. The Quantum Molecular Design (QMD) workflow of Cloud Pharmaceuticals combines such tools. It is a multi-algorithm augmented intelligence-based drug discovery platform where drugs are designed, based on binding affinity and desirable drug properties. The three major steps of QMD are:
- First, based on the protein target. Bayesian reasoning algorithm is used to build and illuminate ‘hot spots’ in the wide chemical space.
- To identify target molecules, a multi-objective heuristic search is done across the pre-identified chemical space.
- The identified molecular hit list is evaluated with a medicinal chemistry expert system.
Through the successful deployment of QMD, the activity can be successfully measured by deciding on the method to use based on the available data, the level of accuracy needed, and the suitability of a specific algorithm.
3. AI helps design better drugs
More than 450 medicines have been withdrawn from the worldwide market post-approval in the last century. This was done as a result of adverse reactions, with liver toxicity being the most common side-effect. But the metabolism of compounds by organs such as the liver is complex and difficult to anticipate. This is the problem which machine learning helps solve. The data needed to help that process is already available. AI algorithms will help identify potential toxicities and will help predict the reaction of a candidate molecule to physical and chemical environments and also helps drug developers understand how a molecule behaves in various tissues in the human body.
Benefits of applying AI to drug discovery
- AI does not rely on predetermined targets as far as drug discovery is concerned. Hence, subjective bias and existing knowledge is not a factor in the process of drug development.
- AI makes use of the latest advances in computing and biology to develop state-of-the-art algorithms. It increases processing power and reduces processing costs. It has immense potential to level the playing field.
- It has the predictive power to define meaningful interactions in a drug screen. It helps carefully design parameters and minimizes the potential for false positives.
- AI has the potential to move drug screening from the bench to a virtual lab where the results can be obtained quickly, and promising targets can be shortlisted. Thus, extensive experimental input and manpower hours can be shortened.
Difference between traditional and AI-based drug discovery methods
- Traditional methods are target-driven, and AI methods are data-driven.
- Traditional methods work for easily druggable targets that have a well-defined structure, whereas AI methods have complex algorithms that can extract meaningful information from large datasets.
- Traditional methods are extremely limited due to the limited knowledge of cellular pathways and the complex nature of cellular interactions. AI-based methods identify compounds that could bind to ‘undruggable targets’ (the structures that are not defined).
Conclusion
The pharmaceutical industry is all set to strengthen its internal capabilities by centralizing the previously segregated data sources, investing in infrastructure, and hiring data scientists. Using artificial intelligence in the pharmaceutical industry is projected to bring in billions of dollars in funding in the near future as it has the potential to transform the drug development process.
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