Revolutionizing Genetic Medicine with AI: Deep Genomics' Groundbreaking Platform

Table of Contents

1. Introduction
2. Understanding Genetic Diseases
   • 2.1. Genetic Basis of Disease
   • 2.2. Prevalence and Cost
3. The Complexity of Biology and the Role of AI
   • 3.1. The Limitations of Human Understanding
   • 3.2. AI as a Tool for Better Understanding
4. Deep Genomics' AI Drug Discovery Platform
   • 4.1. The Focus on Predicting Molecular Biology
   • 4.2. The Four Steps of the Drug Development Process
   • 4.3. Leveraging AI in Target Discovery
   • 4.4. The Role of RNA Splicing in Genetic Diseases
5. Understanding RNA Splicing
   • 5.1. The Central Dogma of Molecular Biology
   • 5.2. The Process of RNA Splicing
   • 5.3. The Impact of Mutations on Splicing
6. Building AI Models for RNA Splicing Prediction
   • 6.1. Encoding Pre-Messenger RNA Sequences
   • 6.2. Using Neural Networks for Predictions
   • 6.3. Accuracy and Performance of the Models
   • 6.4. Predicting the Impact of Mutations
7. Discovering Therapeutic Candidates with AI
   • 7.1. Designing Steric Blocking Oligonucleotides
   • 7.2. Fine-Tuning Models for Compound Design
   • 7.3. Validation and Efficacy testing
8. Case Study: DG12P1 for Wilson's Disease
   • 8.1. Understanding Wilson's Disease
   • 8.2. Predicting the Disease Mechanism
   • 8.3. Designing DG12P1 as a Therapeutic Candidate
   • 8.4. Accelerating Drug Development Timeline
9. Expanding the Scope of Target Discovery
   • 9.1. The Wide Universe of Splicing Mutations
   • 9.2. The Potential Impact of AI Technology
10. Conclusion

Article

Introduction

Hello everyone! Today, I will be discussing the exciting field of discovering genetic medicines with the AI drug discovery platform at Deep Genomics. At Deep Genomics, we believe that AI can revolutionize the way we understand and develop effective therapies for genetic diseases. In this article, I will dive deep into the world of genetic diseases, the complexity of biology, our AI-driven drug discovery platform, and how we use AI to predict RNA splicing and discover therapeutic candidates. Let's get started!

1. Understanding Genetic Diseases

Genetic diseases are conditions that result from abnormalities in a person's genetic material, such as DNA or RNA. These diseases can have a significant impact on an individual's health and quality of life. To address genetic diseases, it is crucial to understand their genetic basis and prevalence.

2.1 Genetic Basis of Disease

Approximately 65% of people will experience a disease with a genetic basis at some point in their lives. Furthermore, serious genetic disorders affect roughly 8 million newborns each year. The burden of genetic disease is not limited to individuals but also extends to their families, both emotionally and financially. Dealing with a birth affected by a genetic disorder can cost a family up to $5 million in the United States Healthcare system.

2.2 Prevalence and Cost

The high prevalence and associated costs of genetic diseases emphasize the urgency of finding effective therapies within a short timeframe. The traditional approach to drug development is often slow and may not adequately address the complexity of genetic diseases. This is where AI and Deep Genomics' drug discovery platform come into play.

3. The Complexity of Biology and the Role of AI

Biology is a complex field that often exceeds the limits of human understanding. The intricate mechanisms underlying genetic diseases require a deep understanding of underlying biology to develop effective drugs. Fortunately, AI provides an opportunity to improve our understanding and accelerate the drug discovery process.

3.1 The Limitations of Human Understanding

The complexity of biology makes it difficult for humans to fully comprehend the underlying mechanisms that drive genetic diseases. With an immense number of possible interactions and variations, finding effective therapies becomes a daunting task. That's where AI comes in.

3.2 AI as a Tool for Better Understanding

AI can analyze immense amounts of data and identify Patterns and relationships that may not be apparent to humans. At Deep Genomics, we have developed an AI-driven drug discovery platform that can predict molecular and cell biology, enabling us to better understand genetic diseases and find effective therapies.

4. Deep Genomics' AI Drug Discovery Platform

Deep Genomics focuses on developing a drug discovery platform that leverages AI to accurately predict molecular and cell biology. By integrating AI into every step of the drug development process, from target discovery to pre-clinical and clinical research, we can accelerate the process and achieve robust scientific results.

4.1 The Focus on Predicting Molecular Biology

At Deep Genomics, our primary focus is on developing AI models that accurately predict molecular biology. We have developed over 30 predictors in our AI workbench, covering various aspects of biology, including gene expression, Transcription rate, translation efficiency, ribosome binding, microRNA binding, and more.

4.2 The Four Steps of the Drug Development Process

The drug development process typically involves four essential steps: target discovery, identifying lead compounds, pre-clinical research, and clinical research. By leveraging AI predictions, we can significantly enhance each of these steps, making the drug development process faster and more efficient.

4.3 Leveraging AI in Target Discovery

Target discovery is a crucial step in the drug development process. Deep Genomics' AI platform enables us to accurately predict the impact of genetic mutations on splicing, allowing us to identify potential therapeutic targets. Our AI systems guide us in selecting experiments and compounds to test, streamlining the target discovery process.

4.4 The Role of RNA Splicing in Genetic Diseases

RNA splicing plays a critical role in genetic diseases. By understanding the splicing process and predicting the impact of mutations on splicing, we can develop targeted therapies that address the underlying causes of genetic diseases. Deep Genomics' AI models excel at predicting the impact of mutations on splicing, providing valuable insights for drug development.

5. Understanding RNA Splicing

RNA splicing is a fundamental process that enables the expression of genes from DNA templates. This process involves the removal of introns and joining of exons to create a mature mRNA molecule. Mutations in the splicing process can lead to the production of non-functional proteins, causing genetic diseases.

5.1 The Central Dogma of Molecular Biology

The central dogma of molecular biology describes how genetic information flows from DNA to RNA to protein. DNA is transcribed into pre-mRNA, which is then processed through splicing, leading to the formation of mature mRNA. This mature mRNA serves as a template for protein synthesis.

5.2 The Process of RNA Splicing

RNA splicing involves the removal of introns, non-coding regions, and the joining of exons, coding regions, to generate a functional mRNA molecule. Although introns are not part of the final protein, they contain crucial regulatory information that guides the splicing process. Mutations in introns or exons can disrupt the splicing machinery, leading to the production of non-functional proteins.

5.3 The Impact of Mutations on Splicing

Identifying mutations that affect splicing is challenging due to the vast size of introns and the low occurrence of mutations that cause splicing changes. However, Deep Genomics' AI models can accurately predict the impact of mutations on splicing, even with limited experimental data. These predictions guide us in understanding the functional impact of mutations and designing effective therapies.

6. Building AI Models for RNA Splicing Prediction

Deep Genomics has developed powerful AI models for predicting RNA splicing. These models leverage neural networks and specific encoding techniques to accurately predict splicing outcomes. By incorporating long-range context and extensive training data, our models can effectively predict the impact of mutations on splicing.

6.1 Encoding Pre-Messenger RNA Sequences

To train the AI models, we encode pre-mRNA sequences as a four-Channel input representing the four DNA bases: A, C, G, and T. Each position in the sequence is assigned a value based on its corresponding DNA base. This encoding allows the neural network to learn the patterns and relationships between sequence elements and splicing outcomes.

6.2 Using Neural Networks for Predictions

Deep Genomics' AI models utilize neural networks, specifically dilated convolutions, to analyze the encoded pre-mRNA sequences. These neural networks can consider up to 16,000 base pairs of context at each position, enabling accurate predictions of splicing outcomes. The models learn the biology of splicing and can correctly predict the impact of mutations on splicing without extensive mutation-specific training data.

6.3 Accuracy and Performance of the Models

The AI models developed by Deep Genomics achieve high accuracy and performance in predicting splicing outcomes. In tests on held-out datasets, the models demonstrate a false positive rate as low as 5% and a sensitivity of over 90% in identifying splice junctions. These results outperform traditional approaches and other neural network models, positioning Deep Genomics' AI models as powerful tools for predicting splicing impacts.

6.4 Predicting the Impact of Mutations

Deep Genomics' AI models can accurately predict the impact of mutations on splicing without prior knowledge of the mutation. By incorporating extensive training data and long-range context, the models can predict the outcome of mutations located thousands of base pairs away from the affected exon. This predictive capability allows us to identify pathogenic mutations and design targeted therapies without the need for comprehensive mutation-specific training data.

7. Discovering Therapeutic Candidates with AI

Deep Genomics utilizes its AI-driven drug discovery platform to identify and design therapeutic candidates for genetic diseases. By leveraging the predictions of splicing impacts, we can develop steric blocking oligonucleotides (SBOs) that restore normal splicing patterns and generate functional proteins. These SBOs hold the potential to become effective treatments for genetic diseases.

7.1 Designing Steric Blocking Oligonucleotides

Steric blocking oligonucleotides are short sequences of modified RNA designed to Bind to specific locations on mRNA. By binding to these sites, SBOs can block the activity of regulatory elements and influence splicing outcomes. Deep Genomics' AI platform aids in the design of SBOs by predicting their binding efficacy and the desired impact on splicing.

7.2 Fine-Tuning Models for Compound Design

Deep Genomics uses a combination of experimental data and AI models to fine-tune the design and efficacy of SBOs. By incorporating features specific to SBOs, such as thermodynamic properties and target location, the models can correlate predicted effects with experimental results. This approach reduces the number of experiments required and accelerates the discovery of effective therapeutic candidates.

7.3 Validation and Efficacy Testing

To validate and measure the efficacy of therapeutic candidates, Deep Genomics conducts extensive lab experiments. Using cell culture techniques and quantitative measurements, such as qPCR, the efficacy of SBOs is assessed. This experimental validation ensures that the predicted effects of SBOs Align with the desired outcomes.

8. Case Study: DG12P1 for Wilson's Disease

One notable success of Deep Genomics' AI-driven drug discovery platform is the discovery of DG12P1 as a lead candidate for the treatment of Wilson's disease. Wilson's disease is a genetic disorder that impairs the body's ability to remove copper, leading to potential life-threatening organ damage. DG12P1 specifically targets a mutation known as MET 645 ARGE, restoring the proper splicing patterns and functional protein expression.

8.1 Understanding Wilson's Disease

Wilson's disease affects approximately one in every 30,000 people worldwide and requires a precise understanding of the underlying genetic mutation to develop effective treatments. The MET 645 ARGE mutation causes exon 6 in the ATP7B gene to be Skipped, resulting in non-functional protein expression.

8.2 Predicting the Disease Mechanism

Deep Genomics' AI platform accurately predicted the disease-causing mechanism of the MET 645 ARGE mutation. By analyzing a vast database of mutations and their impact on splicing, the AI system identified the specific splicing change caused by the mutation, providing valuable insights for target discovery.

8.3 Designing DG12P1 as a Therapeutic Candidate

With the knowledge of the splicing defect caused by the MET 645 ARGE mutation, Deep Genomics designed DG12P1, a steric blocking oligonucleotide, to target and restore exon 6 inclusion. Through computational design and experimental validation, DG12P1 proved to be a lead candidate for the treatment of Wilson's disease. Its ability to restore proper splicing patterns and functional protein expression makes it a potential breakthrough therapy.

8.4 Accelerating Drug Development Timeline

The use of AI in target discovery, compound design, and efficacy testing significantly accelerates the drug development timeline. Deep Genomics compressed the timeline for developing DG12P1 to approximately two years, providing a faster path from discovery to investigational new drug declaration. This accelerated timeline is a testament to the power of AI in revolutionizing the drug development process.

9. Expanding the Scope of Target Discovery

Deep Genomics' AI-driven drug discovery platform offers immense potential for expanding the scope of target discovery beyond a single mutation. The universe of splicing mutations is vast, and each point on the scatter plot represents a unique drug program. By leveraging AI technology, Deep Genomics aims to address a wide range of genetic diseases and make a substantial impact on human health.

9.1 The Wide Universe of Splicing Mutations

The universe of splicing mutations is vast, encompassing a multitude of genes and mutations. Deep Genomics' AI platform can accurately predict the impact of splicing mutations, enabling the discovery of potential drug programs. By analyzing vast databases of mutations and utilizing AI predictions, we can identify therapeutic targets with precision and efficiency.

9.2 The Potential Impact of AI Technology

The integration of AI technology into drug discovery has the potential to transform the field. The ability to predict splicing impacts, design therapeutic candidates, and accelerate the drug development process heralds a new era of precision medicine. Deep Genomics' AI-driven drug discovery platform showcases the power of AI in addressing genetic diseases.

10. Conclusion

In conclusion, Deep Genomics' AI drug discovery platform holds immense promise in the field of genetic medicine. By leveraging AI predictions, we can accurately predict splicing impacts, design therapeutic candidates, and accelerate the development of effective therapies. Through our work in understanding genetic diseases and developing targeted treatments, we aim to make a significant impact on human health. With the power of AI, the future of genetic medicine looks brighter than ever.

🌟 Highlights:

• Deep Genomics' AI drug discovery platform revolutionizes the understanding and development of genetic medicines.
• Genetic diseases impact a significant portion of the population, demanding effective therapies.
• Biology's complexity exceeds human understanding, but AI provides an opportunity for improved comprehension.
• Deep Genomics' AI models accurately predict splicing impacts, driving target discovery and compound design.
• Case study: DG12P1 as a lead candidate for treating Wilson's disease showcases the power of AI in drug development.

✨ FAQ:

Q: How robust are Deep Genomics' AI methods for predicting the impact of mutations on splicing? A: Deep Genomics' AI models have demonstrated high accuracy and performance in predicting splicing impacts. The models incorporate extensive training data and long-range context to achieve robust results.

Q: Can Deep Genomics predict the side effects of compounds on different parts of the sequence? A: Yes, Deep Genomics' AI models can predict the effects of compounds on different regions of the sequence. The models analyze the entire mRNA sequence, allowing accurate predictions of compound effects.

Q: How many instances did Deep Genomics use to train its AI models? A: The AI models were trained using approximately one million examples, each consisting of 5,000 base pairs. The model's predictive power was assessed on held-out datasets to ensure generalization performance.

Q: Has Deep Genomics tested its compounds on known mutations associated with Wilson's disease? A: Deep Genomics focuses on designing therapeutic candidates specifically for the MET 645 ARGE mutation in Wilson's disease. The potential treatment targets other genetic diseases are part of a growing pipeline.

Q: Can Deep Genomics predict drugs significantly better than its training set? A: Deep Genomics' AI models can accurately predict the effects of compounds based on the sequence content. The models rank compounds by predicted effects, allowing for efficient screening and identification of potential therapeutic candidates.