ARTICLE 1: The Role of AI in Drug Discovery
This article is part of a three-article series on AI in drug discovery. You can explore the full series
Article 1: The Role of AI in Drug Discovery
- Introduction
- How is AI Used in Drug Discovery?
- What Was the First Drug Discovered by AI?
Article 2: AI’s Impact on the Pharmaceutical Industry
- Large Pharmaceutical Companies Incorporating AI into Drug Discovery
- Changing the Landscape: Pharma Companies Growing Fast Thanks to AI Drug Discovery
Article 3: The Future of AI in Drug Development & CymitQuimica’s Contribution
- Advantages of Using AI in Drug Development
- The Future of AI in Drug Development
- CymitQuimica’s Contribution to Drug Discovery
The fusion of AI and drug discovery is an exciting frontier, offering a paradigm shift in pharmaceutical innovation.
The integration of Artificial Intelligence (AI) into drug discovery is transforming the pharmaceutical landscape by accelerating target identification, molecular design, and optimization. This article explores how AI is being applied, the platforms and models leading the innovation, and what to expect in the future.
At CymitQuimica, we remain committed to support research projects by providing an innovative and quality service that connects the needs of laboratories and manufacturers of chemical and biological products, essential for advancing pharmaceutical discoveries.
How is AI Used in Drug Discovery?
AI is revolutionizing drug discovery by improving efficiency in multiple stages of drug development. The combination of different technologies within AI (Machine Learning, Deep Learning and Generative AI) contributes to make drug development simpler, cost-effective, and faster.
Researchers take advantage of the AI capacity to analyse massive amounts of biological, chemical, and clinical data, predict outcomes more accurately, and generate new drug candidates.
A significant contribution of AI to drug discovery is the screening and optimization of potential compounds. Traditionally, compound libraries consisted of physical collections of molecules tested experimentally to identify interactions with biological targets, such as proteins. With advancements in AI, these libraries have evolved into vast digital databases used for virtual screening. AI analyzes these libraries to predict how compounds will interact with a target, accelerating the early stages of drug discovery and improving the efficiency of lead compound development.
In addition to compound screening, AI enhances multiple stages of the drug development process, optimizing efficiency from early discovery to post-market analysis.
Key applications include:
- Target Identification and Validation: AI technologies, such as Machine Learning and natural language processing, enable the rapid analysis of genetic and proteomic data to identify potential drug targets. AI can uncover disease mechanisms, predict novel biomarkers, and refine therapeutic target discovery by analysing vast amounts of biological data (Mak & Pichika, 2019).
- Drug Design and Optimization: AI-driven generative models allow for the design of novel drug-like molecules and the optimization of lead compounds. By predicting the molecular interactions and pharmacokinetic properties of compounds, AI enhances the precision of drug design and the likelihood of identifying promising candidates (Pal et al., 2023). AI can also aid in predicting potential toxicological issues early in the development process, reducing the risk of late-stage failures.
- Preclinical Development: In silico models powered by AI predict the efficacy and safety profiles of drug candidates, thus minimizing the need for extensive animal testing. These predictive models speed up the preclinical phase by identifying the most promising drug candidates for further clinical testing (Chakraborty et al., 2023).
- Clinical Trials: AI’s role in clinical trials is taking more relevance, from optimizing patient recruitment by analysing electronic health records (EHR) to analysing trial data in real-time. AI systems can adapt the trial protocols based on ongoing results, which can help improve trial efficiency and patient outcomes (Editorial, 2023). AI is also used in personalized medicine to predict how patients will respond to specific therapies.
- Post-Market Surveillance: AI’s ability to analyse real-world data from patient populations can help identify adverse drug reactions (ADRs) and monitor long-term drug efficacy. By providing continuous feedback after a drug reaches the market, AI improves patient safety and ensures better post-market management (Mak & Pichika, 2019).
What Was the First Drug Discovered by AI?
The first AI-discovered drug to enter clinical trials was DSP-1181, a serotonin 5-HT1A receptor agonist developed by Exscientia in collaboration with Sumitomo Dainippon Pharma. This drug, designed for obsessive-compulsive disorder (OCD), was developed in just 12 months, compared to the typical 4–5 years required using conventional methods.
DSP-1181’s rapid development was enabled by AI models that analysed millions of potential molecules, optimizing their efficacy, safety, and drug-likeness. Since then, AI has been used to design multiple drug candidates, some of which have advanced to clinical trials, showing AI’s potential to improve pharmaceutical R&D.
Continue reading the series: