Generative Molecular Design with Steerable and Granular Synthesizability Control

Abstract: Synthesizability in small molecule generative design remains a bottleneck. Existing works that do consider synthesizability can output predicted synthesis routes for generated molecules. However, there has been minimal attention in addressing the ease of synthesis and enabling flexibility to incorporate desired reaction constraints. In this work, we propose a small molecule generative design framework that enables steerable and granular synthesizability control. Generated molecules satisfy arbitrary multi-parameter optimization objectives with predicted synthesis routes containing pre-defined allowed reactions, while optionally avoiding others. One can also enforce that all reactions belong to a pre-defined set. We show the capability to mix-and-match these reaction constraints across the most common medicinal chemistry transformations. Next, we show how our framework can be used to valorize industrial byproducts towards de novo optimized molecules. Going further, we demonstrate how granular control over synthesizability constraints can loosely mimic virtual screening of ultra-large make-on-demand libraries. Using only a single GPU, we generate and dock 15k molecules to identify promising candidates in Freedom 4.0 constituting 142B make-on-demand molecules (assessing only 0.00001% of the library). Generated molecules satisfying the reaction constraints have > 90% exact match rate. Lastly, we benchmark our framework against recent synthesizability-constrained generative models and demonstrate the highest sample efficiency even when imposing the additional constraint that all molecules must be synthesizable from a single reaction type. The main theme is demonstrating that a pre-trained generalist molecular generative model can be incentivized to generate property-optimized small molecules under challenging synthesizability constraints through reinforcement learning.

Overview of Generative Molecular Design with Steerable and Granular Synthesizability Control

The paper "Generative Molecular Design with Steerable and Granular Synthesizability Control" presents a framework addressing a critical challenge in small molecule generation: synthesizability. Current methods in molecular design either ignore synthesizability or offer limited control over synthesis pathways. This research introduces a novel framework that enhances control over the synthesizability of generated molecules by incorporating user-defined reaction constraints into the design process. The capability to enforce or avoid specific reactions, while achieving multiparameter optimization, advances the formulated methodologies for generating feasible synthetic routes.

Key Contributions

The authors introduce a generative framework that allows:

1. Enforcing Specific Reaction Constraints: The framework can guide the generative process to include or exclude specific reaction types like amide formation, ensuring that generated molecules are not just synthesized in silico but remain within experimental feasibility boundaries.
2. Granular Control Over Synthesizability: The model facilitates fine-tuned reaction constraints, allowing complex user-defined conditions while maintaining high sample efficiency. Generated molecules can incorporate specific building blocks or conform to straightforward synthesis routes, enhancing practical applicability.
3. Valorization of Industrial Waste: The framework demonstrates an innovative application—transforming industrial waste into commercially valuable molecules. By applying synthesizability constraints, this method optimizes waste valorization strategies under specific chemical editing principles.
4. Generative Virtual Screening: The framework mimics large-scale virtual screening operations by imposing reaction constraints equivalent to those used in large compound libraries. This capability significantly reduces required computational resources, effectively generating optimal candidates from billions of potential compounds.

Implications and Future Directions

The implications of this work are substantial for both theoretical exploration and practical applications in synthetic chemistry and drug design. The ability to incorporate explicit and customizable synthesis constraints into molecule generation opens avenues for more sustainable and economically efficient drug development processes. Moreover, this approach aligns with broader green chemistry goals by reducing synthetic complexity and promoting the use of more widely available or environmentally friendly reagents.

Looking to the future, the integration of more sophisticated and precise retrosynthesis models with the generative framework could further improve the accuracy of predicted synthesis routes. Additionally, exploring the application of this framework in real-time synthesis planning and automated laboratory environments could offer considerable advancements in the field of autonomous chemical synthesis. The approach could also be invaluable in developing customized synthesis pathways, improving the cost-efficiency and speed of drug discovery and development pipelines.

In summary, this paper delineates a significant advancement in generative molecular design, offering a versatile and powerful tool to enhance the practicality and efficiency of de novo synthetic chemistry, with broad implications for the future of molecular innovation.