The AI-enabled approach in the drug discovery industry is growing fast at the rate of 40% per year. AI has a transformative potential that encourages pharma companies to plan a future where they can use AI in drug discovery more often. The pharma companies also need to determine where AI can provide value and use it accordingly in drug discovery.
AI is a new and advanced technology that is used in drug discovery, but to use that the pharma company needs to make a necessary transformation in the drug discovery process. In order to take full advantage of the AI-enabled drug discovery process, companies need to make investments in data, technology and new skills.
During the last decade, artificial intelligence or AI has found its way into the drug discovery world. Companies that provide drug discovery services or biotech companies for that matter have been known to use AI-enabled approaches recently for drug discovery.
Scope of AI
There are four ways in which artificial intelligence (AI) can contribute to the field of small-molecule drug discovery. Firstly, it can provide access to new areas of biology. Secondly, it can enhance or introduce novel chemical processes. Thirdly, it can increase the success rates of drug discovery.
Fourthly, it can accelerate and streamline the discovery process, making it more cost-effective. By overcoming various challenges and limitations in traditional research and development, AI has the potential to revolutionize drug discovery.
Each AI application brings a unique perspective to drug discovery teams, and in some instances, can transform established workflows. Because these technologies can be applied to various discovery contexts and biological targets, it is essential to comprehend and differentiate between different use cases.
Benefits of Using AI in Drug Discovery
Now that we have discussed in brief the new AI-enabled approach for drug discovery, it is time to see the many benefits that using this AI in drug discovery has. It has been discovered that AI can deliver value in small-molecule drug discovery.
There are four main benefits of using AI in drug discovery including access to new biology, improved and novel chemistry, better success rates, and quicker and cheaper discovery processes.
These benefits can be further elaborated as follows:
- The AI-enabled approach allows the mapping of novel disease pathways, protein or drug and poly-pharmacological interactions based on AI analysis.
- The AI analytics of phenotypic results help to identify new targets and leads.
- With the AI-based approach, you can understand protein interactions, function, and drug ability using protein structure prediction.
- The AI-enabled approach also helps you to identify novel binding sites and protein interactions through the modeling of protein motion.
- The AI-enabled approach gives you the ability to explore a vast array of chemical space and conduct molecular dynamics to screen for small molecule hits.
- The AI analysis of molecule structure and experimental data allows us to predict small molecule structure or activity relationships.
- Optimal synthetic data can be predicted by AI mining of literature and internal data.
- You can select drug leads based on AI-predicted pharmacokinetic and pharmacodynamic properties.
Final Thoughts
When considering the implementation of AI, it is crucial to make informed decisions about where to apply the technology and be explicit about the changes you hope to achieve. This includes determining whether you wish to use AI to optimize the current discovery process or to adopt an AI-first model that transforms the entire discovery program.
It is essential to define the desired outcomes, whether it involves explicit cost and time savings, the identification of new targets, or progress in previously unresolved diseases.
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