The search for new medications and therapies in the medical field is an ongoing battle against illness and aging. As technology advances, generative AI is emerging as a game changer in drug discovery. To accelerate and reduce the costs associated with drug discovery, generative AI is used to create new molecules with desired properties. In this blog, we will define the role of Generative AI in Drug Discovery. Join me as we explore!
What is Generative AI?
Generative AI systems are those that can generate new data while also analyzing existing data. Generative AI is widely used to generate text, music, images, and computer code. Fundamentally, generative AI uses neural networks, which are machine learning models trained on massive datasets. During training, these models detect contextual and pattern-based data.
A generative AI trained on drug descriptions, for instance, could create a new one. Given details, an image-generative AI could generate new chemical structures. In areas like drug discovery, where there is a lot to learn, this is helpful.
What is the role of Generative AI in Drug Discovery?
- Molecule Generation: Generative artificial intelligence (AI) models, particularly those based on generative adversarial networks (GANs) and recurrent neural networks (RNNs), can generate new molecular structures that meet predefined criteria. This ability to produce a huge variety of different molecules opens up new avenues for discovering potential therapeutic candidates that traditional approaches may have overlooked.
- Drug Design Optimization: The use of generic artificial intelligence (AI) to modify existing compounds shows potential for improving drug design. Artificial intelligence (AI) algorithms can use differences in molecular structures to identify modifications that improve a drug’s safety, efficacy, and specificity. This improves the process of developing new drugs.
- De Novo Drug Design: De novo drug design is one of the most fascinating uses of generative AI in drug discovery. This entails creating brand-new molecules from the ground up to target particular illnesses. AI models can predict molecular structures that are likely to display desirable properties because they can be trained on enormous databases of known drugs and their properties.
Advantages of Generative AI in Drug Discovery
- Accelerated drug discovery process: In drug discovery, generative AI significantly expedites the process by allowing scientists to quickly comb through a large chemical space. This efficiency reduces the time between initial discovery and clinical trials by hastening the identification of possible drug candidates.
- Cost savings: Traditional drug discovery methods, which have high failure rates and require extensive experimentation, can cost billions of dollars. In drug discovery, generative AI reduces the need for costly lab work while increasing the probability of success, resulting in significant cost savings.
- Targeting rare diseases and personalized medicine: When it comes to rare diseases with limited drug target information, generative AI can help by extrapolating knowledge from existing datasets to suggest potential therapeutic molecules. Moreover, the technology is highly compatible with personalized medicine, which adjusts treatments for each patient according to their genetic composition.
Future of Generative AI in Drug Discovery
We are only starting to see the applications of generative AI in drug discovery. As long as AI systems advance at a rapid pace, they will have a significant impact on pharmaceutical research and development.
In the future, generative AI models will be able to efficiently explore larger and more complex chemical spaces in search of new treatment candidates. They will also improve at predicting molecular properties.
Beyond merely molecular design, generative AI may also aid in optimizing the complete end-to-end drug development process. AI can analyze data, manage lab robots, automate experiment design, and even draft regulatory submissions.
FAQ’S
Q1. How is drug discovery aided by generative AI?
Ans. Generative AI predicts molecular properties, creates new applications for approved medications, and quickly creates optimized molecular structures for new and existing drugs.
Q2. Generative AI in the production of drugs: what is it?
Ans. Drug manufacturing processes do not directly use generative AI. It is used earlier in the process of developing new drugs to create, refine, and assess new molecular structures before production.
Q3. Which medication was the first one that AI found?
Ans. Exscientia’s AI system created the first drug candidate found solely through AI that went through human clinical trials in 2022.
