Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models (2407.12186v1)

Abstract: Synthesizability in generative molecular design remains a pressing challenge. Existing methods to assess synthesizability span heuristics-based methods, retrosynthesis models, and synthesizability-constrained molecular generation. The latter has become increasingly prevalent and proceeds by defining a set of permitted actions a model can take when generating molecules, such that all generations are anchored in "synthetically-feasible" chemical transformations. To date, retrosynthesis models have been mostly used as a post-hoc filtering tool as their inference cost remains prohibitive to use directly in an optimization loop. In this work, we show that with a sufficiently sample-efficient generative model, it is straightforward to directly optimize for synthesizability using retrosynthesis models in goal-directed generation. Under a heavily-constrained computational budget, our model can generate molecules satisfying a multi-parameter drug discovery optimization task while being synthesizable, as deemed by the retrosynthesis model.

• The paper introduces a method that integrates retrosynthesis models into generative design to optimize synthesizability directly.
• It leverages a sample-efficient generative model, repositioning retrosynthesis from a post-hoc filter to an active optimization tool.
• Experimental validation on a multi-parameter drug discovery task highlights the method’s potential to streamline synthetic feasibility in drug design.

Optimizing Synthesizability in Generative Molecular Design

The paper "Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models" by Jeff Guo and Philippe Schwaller presents a novel approach in the field of computational drug design. This work focuses on addressing a critical challenge in generative molecular design: how to generate molecules that are not only theoretically promising but also practically synthesizable. The authors propose leveraging sample-efficient generative models to directly incorporate retrosynthesis models into the optimization process, a departure from traditional post-hoc filtering methods.

Core Contributions

1. Synthesizability in Molecular Design: The paper acknowledges the persistent issue of synthesizability in molecular design. While many existing generative models produce chemically interesting molecules, these designs often face practical challenges when it comes to synthesis. The authors focus on bridging this gap by integrating synthesizability considerations directly into the molecular generation process.
2. Use of Retrosynthesis Models: Retrosynthesis models, traditionally used as a filtering tool due to their high inference costs, are repositioned as integral components of an optimization loop. The paper demonstrates that, with a sufficiently sample-efficient generative model, retrosynthesis models can be employed directly to ensure that generated molecules come with viable synthetic routes.
3. Experimental Validation: Under a constrained computational budget, the authors show the feasibility of their approach. They deploy their methodology to solve a multi-parameter drug discovery task that balances chemical utility with synthesizability, potentially streamlining the drug development pipeline by focusing on molecules that are not only potent but also practical to synthesize.

Methodological Insights

• Generative Model Efficiency: The authors employ the Saturn generative model, noted for its sample efficiency, which allows them to treat retrosynthesis models as an oracle within the goal-directed generation framework.
• Contrasting Established Models: By integrating retrosynthesis models in the optimization phase, this work contrasts with established models which utilize retrospective methods post-generation. This method highlights a potential shift in how computational chemistry can approach synthesizability.

Implications and Future Directions

The immediate implication of this research is a push towards integrating practical drug synthesis considerations in early-stage computational design. By demonstrating that sample-efficient generative models can accommodate the complexity of retrosynthetic analysis, this paper encourages a reevaluation of strategies in drug discovery involving synthetic feasibility.

For future research, the methodology outlined presents several avenues:

• Benchmarking Across Diverse Chemical Spaces: Extending this approach to validate across varied chemical libraries and targets would help understand its robustness and generalizability.
• Refinement of Retrosynthesis Models: As these models play a pivotal role in this approach, continuous refinement and acceleration of retrosynthetic predictions could drastically improve efficiency and broaden applicability.
• Integration with Experimental Feedback: Incorporating feedback from actual synthetic experiments could refine the metrics further, aligning computational predictions with real-world synthetic success rates.

Concluding Remarks

This paper represents a significant contribution to the field of computational molecular design by substantively addressing the synthesizability challenge. It effectively combines model efficiency with chemical feasibility, proposing a workflow that can potentially accelerate the transition of computer-generated molecules from in silico designs to real-world chemical entities. As this work demonstrates, harnessing the full potential of computational chemistry requires not only innovation in algorithmic design but also a practical consideration of the synthetic endgame.