Whitepaper:


Artificial Intelligence's Potential Role in the Revolution of Drug Discovery

September 10-12, 2019
Hynes Convention Center,
Boston, MA

Cell & Gene Therapy Bioprocessing & Commercialization

September 10-12, 2019
Boston Convention & Exhibition Center (BCEC)
Boston, MA

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Modern drug discovery is a complicated process that requires identification of disease targets, bioactive substances that act upon those targets, and finally optimization of those drug candidates. Each stage of the process is time consuming and costly with no guarantee of success. The average drug requires $2.5 billion to reach FDA approval and only a tenth of all drugs that reach phase I clinical trial ultimately succeed. Artificial intelligence (AI) promises to revolutionize this costly and inefficient discovery process.

AI is a broad term that describes smart, human-like, behaviour by computers or machines. The use of AI guided robotic process automation for applications such as high-throughput screening has the potential to increase efficiency and the use of AI to interrogate annotated datasets is a burgeoning field, with the ability to improve
target identification, drug design and lead optimization. The most common form of
AI in drug discovery is machine learning, algorithms and statistical models that can
perform a specific task, often prediction orientated, without explicit programming of the
algorithms on how to achieve their aim. A prerequisite for AI in drug discovery is well
annotated datasets.


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