Transforming Drug Discovery with Computational Methods

The recent pandemic has highlighted the critical need for rapid and cost-efficient development of new medications. However, creating new drugs—whether small molecules, biologics, macrocycles, or degraders—is a lengthy and costly endeavor, usually requiring more than 12 years and over $2B in investment.

At Cadence Molecular Sciences, we are leveraging the combined strengths of OpenEye's 25 years in developing software for drug discovery and Cadence's deep computational expertise to accelerate the innovative development of scientific software.

This blog post explores how Cadence Molecular Sciences uses computational methods to improve drug discovery, highlighting the advancements in technology and processes transforming the pharmaceutical industry. During his talk at CadenceLIVE 2024 in Silicon Valley, Geoff Skillman, MD, PhD, VP, Research and Development, Cadence Molecular Sciences, discussed the advantages of these methods and offered practical insights into their application in various stages of drug development.

The Challenges of Traditional Drug Discovery

Drug discovery is very risky; only one in ten thousand molecules makes it through the entire process, and nine out of ten candidate molecules fail in clinical trials. High attrition rates are the primary contributor to the time and cost of drug discovery. The high failure rate across all stages of drug discovery is due to a combination of factors: insufficient understanding of human biology, inadequate characterization of therapeutic targets, and the complexities associated with predicting drug delivery, efficacy, and toxicity.

Traditional drug discovery has predominantly relied on physical experimentation, including high-throughput screening (HTS), where thousands of compounds are tested against a biological target; lead optimization, which involves refining the most promising compounds to enhance their efficacy and safety; and clinical trials, where human tests are conducted to ascertain a drug's effectiveness and safety.

Harnessing Computational Techniques

Computational methods have revolutionized drug discovery, reducing experimental cost and time across all stages of the process. Researchers can now predict how molecules will engage with biological targets by employing advanced algorithms, artificial intelligence (AI), and physics-based models. This approach has enabled researchers to discover a wider array of molecules and has significantly reduced the reliance on wet laboratory experiments, saving time and effort in moving potential life-saving therapeutics from idea to reality.

The Power of Computational Methods

Computational methods provide accurate models of biological processes that enable researchers to swiftly and precisely identify potential drug candidates. They enhance speed by allowing analysis of vast datasets significantly faster than human researchers can, which accelerates the discovery phase; they improve accuracy through predictive models that increase the likelihood of identifying viable drug candidates, ultimately reducing the number of unsuccessful experiments; and they are cost-effective, as they substantially lower research and development costs by minimizing the need for lab experimentation. 

The Role of Simulations

Simulations play a crucial role in computational drug discovery by allowing researchers to construct virtual models of biological systems, improving our understanding of how various compounds interact with their targets without the need for physical experiments.

Moreover, molecular simulations grant valuable insights into drug interactions at a molecular level, including binding affinity (the strength of the interaction between the protein and the drug molecule) and understanding the dynamic behavior of molecules through time and space, such as when crossing the cell membrane.

Tackling Toxicity with Structural Toxicology

A significant hurdle in drug discovery is the early identification of potential toxic effects. To tackle this challenge, structural toxicology employs computational to predict possible toxicity before a molecule enters clinical trials. By utilizing these techniques, researchers can reduce the need for both animal testing and human clinical trials, thereby addressing both ethical concerns and reducing costs.

How Cadence Enhances Drug Discovery

At Cadence Molecular Sciences, we leverage an array of advanced technologies, including cloud-scale computing, GPU acceleration, and molecular simulations, to revolutionize the drug discovery process. These tools enable us to efficiently process large datasets, which helps our clients identify new therapeutic targets and refine drug candidates.

A critical component of our approach is AI, which enhances drug discovery by analyzing extensive datasets to unveil patterns and make predictions beyond human capability. By applying AI, we can identify potential drug targets by sifting through biological data, predict interactions between new compounds and biological targets using machine learning (ML) models, and suggest modifications to enhance the efficacy and safety of drug designs.

Orion: The SaaS Platform for Drug Discovery

One of the standout tools in Cadence's portfolio is Orion, a software-as-a-service (SaaS) platform designed specifically for drug discovery. Its key features include:

• Real-Time Data Processing: Orion can process massive datasets in real-time, providing instant feedback to researchers.
• Scalability: The platform can scale to accommodate large-scale projects, making it ideal for enterprise-level research.
• Collaboration: Orion supports collaboration by allowing multiple researchers to work on the same project simultaneously. 

GPU Optimization for Enhanced Performance

Graphics processing units (GPUs) are essential for running complex simulations and AI algorithms. Cadence has optimized its software to leverage the power of GPUs, resulting in:

• Faster Simulations: GPUs can process data much faster than traditional CPUs, accelerating the discovery process.
• Improved Accuracy: Enhanced computational power allows for more detailed simulations, improving the accuracy of predictions.
• Cost Savings: GPU optimization can significantly lower research costs by reducing simulation time.

Real-World Applications

Integrating computational methods in drug discovery is not just theoretical; it has real-world applications and success stories. For example, pharmaceutical giants such as Pfizer and GSK have relied upon Cadence software to advance the discovery of new therapies. Using Cadence software has led to:

• Accelerated Time to Market: By leveraging computational methods, GSK has accelerated the drug development timeline.
• Enhanced Efficacy: Computational models have helped identify compounds with higher efficacy, leading to more effective treatments.
• Cost Savings: Using simulations and AI has significantly reduced research and development costs.

Integrating AI with Physics

At Cadence, we have integrated AI with physics-based models, leading to significant advancements in drug discovery. This hybrid approach offers:

• Enhanced Predictive Power: Combining AI with physics-based models can improve the accuracy of predictions.
• Better Understanding: Physics-based models provide a deeper understanding of the mechanisms underlying drug interactions.
• Efficient Workflows: Integrating AI with physics streamlines workflows, making the drug discovery process more efficient.

The Future of Drug Discovery

The future of drug discovery is on the brink of a transformation, thanks to integrating computational methods with traditional techniques. Breakthroughs in AI, ML, and cloud computing are set to significantly enhance the efficiency and accuracy of drug discovery processes, leading to shorter timelines and reduced costs.

Key trends to watch for include the growth of personalized medicine, where AI assists in creating treatments tailored to individual genetic profiles; the use of automated laboratories, utilizing robotics and AI to streamline various aspects of drug discovery; and the development of collaborative platforms, such as SaaS solutions like Orion, which will increasingly facilitate cooperation and innovation within the pharmaceutical industry.

Improving drug discovery through computational methods is not just a possibility; it's a reality at Cadence Molecular Sciences. We are changing how drugs are discovered and developed by employing advanced technologies like AI, cloud-scale computing, and molecular simulations. This approach speeds up the discovery process, cuts costs, and enhances the likelihood of success. If you're interested in learning more about how our solutions can benefit your organization, we invite you to explore our offerings and see how we can help you achieve your drug discovery goals.

The future of drug discovery is here, and it's powered by computational methods. Join us on this exciting journey and discover the therapeutics of tomorrow, today.

If you missed the detailed CadenceLIVE Silicon Valley 2024 presentation, you can sign up at the CadenceLIVE On-Demand site to access this session and other exciting presentations.