Revolutionizing Drug Discovery: AstraZeneca's Data and AI Success Story

Table of Contents:

1. Introduction
2. Delivering Life-Changing Medicines at AstraZeneca
3. Challenges Faced by AstraZeneca
4. The Architecture of AstraZeneca's Drug Discovery Platform
5. Outcomes and Lessons Learned
6. The Future of AstraZeneca's Research and Development
7. Virtual Screening and Molecular Design
8. ADME and TOX Predictions
9. Binding Prediction and Free Energy Perturbation Calculations
10. Synthesis Prediction
11. Conclusion

Introduction

In this article, we will explore how AstraZeneca, a global biopharmaceutical company, has leveraged data science and artificial intelligence (AI) to revolutionize their research and development (R&D) process. We will delve into the challenges they faced, the architecture of their drug discovery platform on AWS, the outcomes they achieved, and the lessons they learned along the way. Moreover, we will explore specific applications of AI, such as virtual screening, ADME and TOX predictions, binding prediction, and synthesis prediction, that have accelerated the identification of potential new medications. AstraZeneca's journey exemplifies how technology can significantly impact the delivery of life-changing medicines to patients.

Delivering Life-Changing Medicines at AstraZeneca

AstraZeneca is a global biopharmaceutical company focused on the discovery, development, and commercialization of prescription medicines. With operations in over a hundred countries, their medicines are used by millions of patients worldwide. The company is dedicated to turning science into medicines quickly, cost-effectively, and with a higher probability of success. To achieve this, AstraZeneca has embraced data science and AI, which have become essential tools in the R&D process.

Challenges Faced by AstraZeneca

In the highly competitive pharmaceutical industry, companies are constantly striving to improve collaboration, make data-driven decisions, and enable faster development of new therapeutics. However, identifying the right cloud technology to support these goals can be challenging. AstraZeneca recognized the need for a cloud solution that would accelerate their transformation and help them unlock the potential of health data. To address this, AWS introduced AWS for Health, an offering that provides a range of services and solutions tailored to the needs of the life sciences industry.

The Architecture of AstraZeneca's Drug Discovery Platform

To overcome the challenges they faced, AstraZeneca embarked on a journey to build a robust drug discovery platform that combines data and AI. The platform was built on AWS, utilizing managed services and microservices-based architectures. A key focus was breaking down data silos and creating a centralized data hub of chemistry data. By leveraging AWS services such as Amazon Aurora and Amazon OpenSearch, AstraZeneca was able to store, analyze, and search vast amounts of scientific data effectively. They also employed machine learning models and deep reinforcement learning algorithms to enhance virtual screening, ADME and TOX predictions, binding prediction, and synthesis prediction.

Outcomes and Lessons Learned

The implementation of AstraZeneca's drug discovery platform on AWS yielded significant outcomes. They experienced a reduction in drug discovery time and hosted over 200 data and AI endpoints. Furthermore, they learned valuable lessons throughout the process. Collaboration between IT teams and chemists proved essential, and a product-focused approach with agile delivery methodologies played a crucial role in their success. Going forward, AstraZeneca plans to Scale their current successes, involve more users, and explore new opportunities to leverage technology in research.

The Future of AstraZeneca's Research and Development

With the foundations of their drug discovery platform in place, AstraZeneca is excited about the future. They aim to work more closely with their scientists to unleash the power of technology in research. Scaling their successes to reach more users and exploring new areas is a priority. By continuing to embrace innovation and leverage AI throughout the R&D process, AstraZeneca is committed to enhancing the delivery of life-saving medicines to patients worldwide.

Virtual Screening and Molecular Design

Virtual screening is a key component of AstraZeneca's drug discovery process. They have partnered with OpenEye, an AWS Partner, to utilize the OpenEye Orion platform. With virtually unlimited compute resources on AWS, computational chemists can perform searches across large libraries of small molecules to identify potential drug-target interactions efficiently. Through deep reinforcement learning and de novo design, the AI ideation platform suggests new chemical structures that satisfy specific molecular profiles. These innovative approaches accelerate the identification of Novel compounds and enable scientists to explore a broader range of options.

ADME and TOX Predictions

Understanding how potential drugs will perform in the human body is critical to the drug discovery process. AstraZeneca has developed the Predictive Insights platform to host and serve machine learning models for ADME (absorption, distribution, metabolism, and excretion) and TOX (toxicity) predictions. By analyzing vast amounts of data, scientists can make accurate predictions about the safety and efficacy of potential drugs. The platform leverages scalable APIs, Amazon Athena, and Amazon OpenSearch to provide scientists with Timely insights and reduce the overall lead optimization time.

Binding Prediction and Free Energy Perturbation Calculations

Accurately predicting how potential drugs Bind to target proteins is vital in drug discovery. AstraZeneca uses Schrodinger's AWS Partner Solution to perform Free Energy Perturbation (FEP) calculations. The platform utilizes powerful Amazon EC2 P3 instances with high-performance GPUs to accelerate these computationally intensive calculations. By enhancing the accuracy of binding predictions, AstraZeneca reduces lead optimization time and increases the overall success rate of potential drug candidates.

Synthesis Prediction

Chemical synthesis plays a crucial role in drug discovery. Predicting the feasibility and efficiency of chemical synthesis is essential to streamline the drug development process. AstraZeneca has developed machine learning models hosted on Amazon EKS to predict whether chemical synthesis is possible and to propose the best synthesis route for a given molecule. By leveraging historical reaction data, the models provide scientists with valuable insights, enabling them to make informed decisions and optimize the synthesis process.

Conclusion

AstraZeneca's journey in leveraging data science and AI to transform their R&D processes showcases the immense potential of technology in the pharmaceutical industry. By breaking down data silos, implementing advanced algorithms, and utilizing the scalability and reliability of AWS, AstraZeneca has accelerated the identification of potential life-changing medicines. The outcomes they have achieved, along with the valuable lessons learned, serve as an inspiration for other companies seeking to harness the power of technology in drug discovery. With a product-focused approach and a commitment to innovation, AstraZeneca is poised to make even greater strides in delivering life-changing medicines to patients worldwide.

FAQ

Q: How has AstraZeneca leveraged data science and AI in their drug discovery process? A: AstraZeneca has integrated data science and AI across various stages of their drug discovery process. They have applied virtual screening techniques, utilized machine learning models for ADME and TOX predictions, performed binding prediction calculations with the help of deep reinforcement learning algorithms, and used machine learning to predict the feasibility of chemical synthesis.

Q: What are the benefits of AstraZeneca's drug discovery platform on AWS? A: AstraZeneca has experienced a significant reduction in drug discovery time and hosted over 200 data and AI endpoints. By leveraging AWS's managed services and microservices-based architectures, they have improved the accuracy and efficiency of their research and development processes.

Q: How has AstraZeneca addressed the challenges of data silos and collaboration in their drug discovery platform? A: AstraZeneca has built a centralized data hub on AWS, which integrates data from various sources and eliminates data silos. They have also implemented scalable APIs, allowing for seamless collaboration and data sharing among researchers and scientists.

Q: What are the future plans of AstraZeneca in research and development? A: AstraZeneca aims to work more closely with scientists and scale their current successes to reach more users and explore new areas. They are committed to leveraging technology and innovation to enhance the delivery of life-saving medicines to patients worldwide.

Q: How has AstraZeneca improved the accuracy of binding predictions in their drug discovery process? A: AstraZeneca has employed Schrodinger's FEP+ platform, which utilizes powerful Amazon EC2 P3 instances with high-performance GPUs. This allows for more accurate binding predictions, resulting in better lead optimization and increased success rates for potential drug candidates.

Q: What is the role of machine learning in synthesis prediction? A: Machine learning models trained on historical reaction data are used to predict the feasibility and efficiency of chemical synthesis. This helps scientists make informed decisions and optimize the synthesis process, ultimately saving time and resources.

Q: How has AstraZeneca achieved faster and more efficient drug discovery using AWS? A: By leveraging AWS's scalable infrastructure and managed services, AstraZeneca has accelerated their drug discovery process. They have reduced drug discovery time, improved collaboration, and utilized advanced AI algorithms to identify potential drug candidates more quickly and accurately.

Resources:

• AstraZeneca
• AWS for Health
• OpenEye
• Schrodinger
• Amazon EC2
• Amazon Aurora
• Amazon OpenSearch
• Amazon Athena
• Amazon ElastiCache
• Amazon EKS
• AWS Spot Instances
• Amazon DocumentDB
• chemTraX
• AWS Partner Network