State of Retrosynthesis in Machine Learning era (Part 1 - A brief synopsis)

Welcome to the debut edition of our technology blog series, "State of Retrosynthesis in the Machine Learning Era" (Part-1), where we embark on a journey to explore the transformative impact of machine learning on retrosynthesis in the field of chemistry. As chemists endeavor to craft new organic compounds, retrosynthesis emerges as a pivotal tool, unraveling the complexity of molecular design. While retrosynthesis has long been a guiding principle, the integration of Machine Learning techniques has injected a fresh wave of innovation into this age-old concept. In this series, we will navigate the evolving landscape of retrosynthesis, delving into its two fundamental steps: single-step retrosynthesis prediction and multistep pathway prediction.

Single-Step Retrosynthesis

Majorly classified into three different subclasses?

1. Template-Based Methods:

• Conceptual Framework: Leveraging substructure recognition algorithms to identify pre-defined reaction templates (encoded as SMILES, SMARTS, or other graph representations) within the target molecule.
• Merits: Interpretability: Explicit mapping between templates and reactions fosters trust and understanding. Efficiency: Pre-defined templates expedite retrosynthesis, particularly for well-studied functional groups and transformations. Accuracy: Extensive training data and curated templates lead to impressive precision and recall for specific reaction types.
• Demerits: Limited Generality: Can struggle with novel reactions or functionalities outside the training set. Reduced Flexibility: Rigid adherence to templates might miss alternative and potentially superior pathways. Scalability Bottleneck: Expanding the template library requires domain expertise and substantial effort.

2. Template-Free Methods:

• Conceptual Framework: Employing deep learning architectures (e.g., graph convolutional neural networks) to analyse large datasets of reactions and learn implicit reaction rules directly from the data.
• Merits: Embracing Novelty: Handle unseen reactions and functional groups with greater ease. Unveiling the Unconventional: Potential to discover novel reaction pathways missed by template-based methods. Algorithmic Agility: Adaptable to diverse chemical spaces and readily incorporates new reaction data.
• Demerits: Black-Box Nature: Lack of explicit template-reaction mapping can hinder interpretability and trust. Computational Demands: Training and inference can be resource-intensive, especially for complex models. Specificity Trade-off: Accuracy might not be as high for specific reaction types compared to well-tuned template-based approaches.

3. Semi-Template Based Methods:

• Conceptual Framework: Bridging the gap by utilizing both pre-defined templates and machine learning. Templates guide initial retrosynthesis steps, while ML models refine and explore alternative pathways.
• Merits: Leveraging Synergies: Combines the interpretability and efficiency of templates with the flexibility and novelty-handling of template-free methods. Promising Potential: Emerging field with active research, offering a future path to versatile and accurate retrosynthesis prediction.
• Demerits: Early Stage of Development: Not as mature as the other two methods, potentially lacking robustness and generalizability. Balancing Act: Finding the optimal balance between template reliance and ML exploration remains a challenge.

• Multi-Step Retrosynthesis

Previously, we focused on single-step methods, but real-world molecules often require more complex approaches. Multi-step retrosynthesis algorithms break down intricate molecules into readily available starting materials through a series of chemical reactions.

Here in the picture above you can see the general methodology for the implementation of multi-step retrosynthesis the major steps involved in multi-step retrosynthesis are:

• Select the most promising node: This refers to the molecule or fragment that the system will focus on at each step of the retrosynthesis process. The system can use a selection policy or user interaction to choose the most promising node.
• Expand the node: This involves breaking down the selected node into smaller fragments using known chemical reactions. The system can use an expansion policy to identify possible fragmentation pathways.
• Update all values along the pathway: This likely refers to updating any relevant properties or features associated with the molecule or fragments, such as their energies, reactivities, or probabilities of success.
• Discard improbable reactions: This step involves filtering out any fragmentation pathways that are deemed unlikely to be successful based on chemical feasibility or other criteria. The system can use a filter or reranker, or user interaction, to perform this filtering.
• Monte Carlo Tree Search: This method explores different expansion possibilities randomly, prioritizing promising paths over time.
• A* Search: This algorithm efficiently searches for the best expansion path by considering both cost (steps taken) and estimated distance to the goal (commercially available molecules).

Stay tuned!

We'll delve deeper into these specific algorithms and explore their inner workings in upcoming blogs. Keep learning and exploring the exciting world of machine learning-powered retrosynthesis!

https://boltzmann.co/post/fZLrLg422wpmbXChIXqv

Author: joel brelson