As generative AI evolves, it moves beyond deciphering human language to mastering the intricate languages of biology and chemistry. Think of DNA as a detailed script, a 3-billion-letter sequence that guides our body’s functions and growth. Similarly, proteins, the essential components of life, have their language, including a 20 amino acid alphabet. In chemistry, the molecules also have a unique dialect, like constructing words, sentences, or paragraphs using grammar rules. Molecular grammar dictates how atoms and substructures combine to form molecules or polymers. Just as language grammar defines the structure of sentences, molecular grammar describes the structure of molecules.
As generative AI, such as large language models (LLMs), demonstrate its ability to decode the language of molecules, new avenues for efficient drug discovery are emerging. Several pharmaceutical companies are increasingly using this technology to drive innovation in drug development. The McKinsey Global Institute (MGI) estimates generative AI could create $60 billion to $110 billion annually in economic value for the pharmaceutical industry. This potential is primarily due to its ability to enhance productivity by speeding up the identification of potential new drug compounds and accelerating their development and approval processes. This article explores how generative AI is changing the pharmaceutical industry by acting as a catalyst for rapid advancements in drug discovery. However, to appreciate generative AI’s impact, it is essential to understand the traditional drug discovery process and its inherent limitations and challenges.
Challenges of Traditional Drug Discovery
The traditional drug discovery process is a multi-stage endeavor, often time-consuming and resource-intensive. It begins with target identification, where scientists pinpoint biological targets involved in a disease, such as proteins or genes. This step leads to target validation, which confirms that manipulating the target will have therapeutic effects. Next, researchers engage in lead compound identification to find potential drug candidates that can interact with the target. Once identified, these lead compounds undergo lead optimization, refining their chemical properties to enhance efficacy and minimize side effects. Preclinical testing then assesses the safety and effectiveness of these compounds in vitro (in test tubes) and in vivo (in animal models). Promising candidates are evaluated in three clinical trial phases to assess human safety and efficacy. Finally, successful compounds must gain regulatory approval before being marketed and prescribed.
Despite its thoroughness, the traditional drug discovery process has several limitations and challenges. It is notoriously time-consuming and costly, often taking over a decade and costing billions of dollars, with high failure rates, particularly in the clinical trial phases. The complexity of biological systems further complicates the process, making it difficult to predict how a drug will behave in humans. Moreover, the intense screening can only explore a limited fraction of the possible chemical compounds, leaving many potential drugs undiscovered. High attrition rates also hampered the process, where many drug candidates fail during late-stage development, leading to wasted resources and time. Additionally, each stage of drug discovery requires significant human intervention and expertise, which can slow down progress.
How Generative AI Changes Drug Discovery
Generative AI addresses these challenges by automating various stages of the drug discovery process. It accelerates target identification and validation by rapidly analyzing vast amounts of biological data to more precisely identify and validate potential drug targets. In the lead compound discovery phase, AI algorithms can predict and generate new chemical structures likely to interact effectively with the target. The ability of generative AI to explore a vast number of leads makes the chemical exploration process highly efficient. Generative AI also enhances lead optimization by simulating and predicting the effects of chemical modifications on lead compounds. For instance, NVIDIA collaborated with Recursion Pharmaceuticals to explore over 2.8 quadrillion combinations of small molecules and targets in just a week. This process could have taken approximately 100,000 years to achieve the same results using the traditional methods. By automating these processes, generative AI significantly reduces the time and cost required to bring a new drug to market.
Moreover, generative AI-driven insights make preclinical testing more accurate by identifying potential issues earlier in the process, which helps lower attrition rates. AI technologies also automate many labor-intensive tasks, enabling researchers to focus on higher-level strategic decisions and scaling the drug discovery process.
Case Study: Insilico Medicine’s First Generative AI Drug Discovery
A biotechnology company, Insilico Medicine, has used generative AI to develop the first drug for idiopathic pulmonary fibrosis (IPF), a rare lung disease characterized by chronic scarring that leads to irreversible lung function decline. By applying generative AI to omics and clinical datasets related to tissue fibrosis, Insilico successfully predicted tissue-specific fibrosis targets. Employing this technology, the company designed a small molecule inhibitor, INS018_055, which showed potential against fibrosis and inflammation.
In June 2023, Insilico administered the first dose of INS018_055 to patients in a Phase II clinical trial. This drug’s discovery marked a historic moment as the world’s first anti-fibrotic small molecule inhibitor was discovered and designed using generative AI.
The success of INS018_055 validates the efficiency of generative AI in accelerating drug discovery and highlights its potential to tackle complex diseases.
Hallucination in Generative AI for Drug Discovery
As generative AI advances drug discovery by enabling the creation of novel molecules, it is essential to be aware of a significant challenge these models could face. The generative models are prone to a phenomenon known as hallucination. In the context of drug discovery, hallucination refers to the generation of molecules that appear valid on the surface but lack actual biological relevance or practical utility. This phenomenon presents several dilemmas.
One major issue is chemical instability. Generative models can produce molecules with theoretically favorable properties, but these compounds may be chemically unstable or prone to degradation. Such “hallucinated” molecules might fail during synthesis or exhibit unexpected behavior in biological systems.
Moreover, hallucinated molecules often lack biological relevance. They might fit with chemical targets but fail to interact meaningfully with biological targets, making them ineffective as drugs. Even if a molecule appears promising, its synthesis could be prohibitively complex or costly, as hallucination does not account for practical synthetic pathways.
The validation gap further complicates the issue. While generative models can propose numerous candidates, rigorous experimental testing and validation are crucial to confirm their utility. This step is essential to bridge the theoretical potential and practical application gap.
Various strategies can be employed to mitigate hallucinations. Hybrid approaches combining generative AI with physics-based modeling or knowledge-driven methods can help filter hallucinated molecules. Adversarial training, where models learn to distinguish between natural and hallucinated compounds, can also improve the quality of generated molecules. By involving chemists and biologists in the iterative design process, the effect of hallucination can also be reduced.
By addressing the challenge of hallucination, generative AI can further its promise in accelerating drug discovery, making the process more efficient and effective in developing new, viable drugs.
The Bottom Line
Generative AI changes the pharmaceutical industry by speeding up drug discovery and reducing costs. While challenges like hallucination remain, combining AI with traditional methods and human expertise helps create more accurate and viable compounds. Insilico Medicine demonstrates that generative AI has the potential to address complex diseases and bring new treatments to market more efficiently. The future of drug discovery is becoming more promising, with generative AI driving innovations.
