Evolution of AI in Drug Discovery – A Rich History
Drug discovery is a complex and time-consuming process that involves identifying and developing new medications. Traditionally, this process has been conducted through a combination of experimental approaches, such as high-throughput screening and animal testing, which can be costly, time-intensive, and may not always yield successful results. The drug discovery function can greatly benefit from leveraging artificial intelligence (AI). The application of AI in drug discovery addresses the challenges of data complexity, resource optimization, and time efficiency, making it a powerful tool to enhance the productivity and success rate of the drug development process.
It is no surprise that AI has been applied in drug discovery for quite a while. The use of artificial intelligence (AI) in drug discovery has evolved over the past few decades, with advancements in technology, increased computational power, and a growing understanding of the potential applications of AI in the field of life sciences.
AI continues to advance in drug discovery. Researchers are exploring novel AI applications, including generative models for de novo drug design, explainable AI for better interpretability, and reinforcement learning for optimization of drug development processes.
Selected Use Cases of AI in Drug Discovery
The Convergence of Biology and Technology
From target identification through preclinical development, AI and machine learning technologies are demonstrating significant impact on timelines, costs, and success rates across the discovery pipeline. Though narrow applications of rules-based informatics and predictive modeling have assisted discovery for years, rapid advances in big data, computing power, and algorithms are enabling far more expansive and revolutionary use cases.
The future
As data generation and collection grows exponentially across industries, better algorithms arise, and computing power expands, the use cases for AI in drug discovery will push far beyond current capabilities. The vision for the future is integrated, automated, and autonomous AI: closed-loop discovery driven end-to-end by artificial intelligence with limited human input needed. This could transform every facet of the pipeline.
The vision
While reaching the vision of fully autonomous, explainable AI across discovery seems many years away still, the rapid pace of progress implies accelerated drug R&D could become reality sooner than anticipated. The most visionary potential of AI in drug discovery is the capability to vastly shorten timelines, lower costs, and improve success by simulating key parts of, or the entirety of, the preclinical pipeline in silico using AI systems.
While simulating all biomolecular science may remain impossible, expert systems capturing enough knowledge and insight to replicate expert researchers could arise. These and augmented human-AI teams seem poised to take discovery to new peaks.
Risks and challenges
Despite the promise, there are risks and challenges inherent in advancing AI across drug discovery that must be addressed:
- Potential bias and unfairness perpetuated by models
- Lack of transparency into model reasoning
- Difficulty understanding failure modes
- Inability to discover novel unforeseen solutions
- Overreliance on narrow systems and loss of human expertise
Preemptive focus must be spent on explainable, ethical AI to build trust in and maximize impact of AI technology. But the outlook remains upbeat overall – with rigorous data curation, validation, monitoring and mitigation, AI can usher in a new era of discovery while responsibly elevating human capability.
Conclusion: The convergence of biology and technology
In summary, AI marks a new phase in biological research, where exponentially growing computing power converges with mountains of complex biomedical data to uncover patterns and insights impossible for lone scientists. Narrow AI now accelerates pipelines; as algorithms and applications advance, autonomous systems could take on discovery feats once solely imaginable by humans. This technology infusion expands the discovery possibility space – and access to its reaches - catalyzing innovation that promises revolutionary new medicines.
Tenthpin's Report AI in Life Sciences
This report gives an overview of the development and current status of AI in the Life Sciences industry, offer insights into the different categories of artificial intelligence, and conclude the report with practical case studies showing how Life Sciences companies are already generating business value.
