# Quantum vs. AI for Drug Discovery

### AI hype vs. reality

In recent years, Artificial Intelligence (AI) has enabled significant strides in drug discovery, utilizing machine learning algorithms to predict molecular interactions and optimize drug candidates.

However, AI solutions come with their own limitations.

### Data limitations diminish AI applicability and diversity

AI relies heavily on large datasets to train its models. This limitation restricts its applicability to drug targets that have sufficient and relevant training data, leaving out novel or less-studied targets.

Quantum-Aided Drug Design (QuADD) is not bound by the constraints of training data. Using physics-based calculations with no need for parametrization, it can be confidently applied to any drug target with a known 3D structure.

This broad applicability is crucial for identifying and optimizing drug candidates for a wider range of targets, including those that are novel or have limited existing data.

### Black box nature undermines explainability and confidence

One of the significant drawbacks of AI in drug discovery is its ‘black box’ nature. The complex algorithms can produce results without providing a clear understanding of how those results were derived, making it difficult for chemists to trust and validate the outcomes.

Quantum-Aided Drug Design (QuADD) uses physics-based models that offer explainable outcomes. Chemists can understand the reasoning behind the outcomes, leading to higher confidence in the accuracy of the results.

### Quantum-Aided Drug Design (QuADD): Leapfrogging AI Limitations

- **Applicability**
  - **AI**: Targets limited by training data set.
  - **QuADD**: Can be applied to any drug target with a known 3D structure.
  
  **Why it matters:** Even the latest AI solutions are limited to targets with extensive data for training, while quantum-based solutions can be used for a wide range of targets, including those that are novel or less studied.
  
  **Bottom line results:** Apply to any target

- **Exploration Scalability**
  - **AI**: Training data is often limited to thousands of compounds.
  - **QuADD**: Can search a chemical space of 10^30, the largest explorable today.
  
  **Why it matters:** By searching a space that is orders of magnitude larger, QuADD offers a greater likelihood of finding the ideal molecule compared to AI models that are limited in their ability to sample a vast chemical space.
  
  **Bottom line results:** Discover hard-to-find molecules

- **Diversity**
  - **AI**: Limited to training data diversity.
  - **QuADD**: Can explore diverse chemical structures.
  
  **Why it matters:** AI’s reliance on training data means it may miss diverse and novel structures, whereas QuADD’s vast search space includes diverse chemical scaffolds, increasing the chances of discovering unique drug candidates.
  
  **Bottom line results:** Generate a diverse candidate library

- **Explainability**
  - **AI**: Black box.
  - **QuADD**: Explainable physics-based model.
  
  **Why it matters:** Scientists can assess the candidates suggested by QuADD based on their physical interactions between the ligand and the pocket, whereas AI requires synthesis prior to understanding.
  
  **Bottom line results:** Minimize time and cost

- **Viability**
  - **AI**: Prone to generating unfeasible results.
  - **QuADD**: Realistic results based on physical principles.
  
  **Why it matters:** AI models can sometimes generate chemically implausible compounds (“hallucinations”) or are ineffective due to overfitting or biases in the training data.
  
  **Bottom line results:** Guarantee viable outcomes

- **Reproducibility**
  - **AI**: Variability due to training data.
  - **QuADD**: Consistent results based on quantum principles.
  
  **Why it matters:** QuADD provides reproducible results as it is not influenced by the variability of training data, ensuring consistent and reliable outcomes.
  
  **Bottom line results:** Ensure reliable results

### See how quantum computing can revolutionize your drug discovery efforts.

[Schedule a Discovery Meeting](https://polarisqb.com/contact-us/)