Synthesizability of Molecules Proposed by Generative Models
The paper "The Synthesizability of Molecules Proposed by Generative Models" by Wenhao Gao and Connor W. Coley addresses a crucial challenge in the field of cheminformatics and drug discovery: the synthetic feasibility of molecular structures proposed by generative models. The authors provide a comprehensive evaluation of the ability of various state-of-the-art generative algorithms to propose synthesizable molecular compounds, with implications for their integration into drug discovery workflows.
Background and Objective
The discovery of functional molecules, particularly in the pharmaceutical industry, is notoriously costly and time-consuming. Recent advances have seen the emergence of de novo molecular generation and optimization techniques, driven by modern deep learning methodologies. Such techniques are instrumental in exploring vast chemical spaces to discover potential therapeutics with desirable properties, bypassing brute-force methods. However, a prevailing obstacle is the synthesizability of generated molecular candidates; often, promising suggested structures cannot be synthesized using currently available methods or materials.
Methodology and Analysis
The authors employ a data-driven computer-aided synthesis planning tool, ASKCOS, to quantitatively assess the synthesizability of molecules generated by various generative models. The analysis conducted encompasses two categories: distribution learning models, which interpolate within a chemical space described by training data, and goal-directed generation models, which aim to optimize for specific chemical properties or objectives.
Evaluation Metrics
Gao and Coley assess synthesizability using synthetic complexity scores such as the synthetic accessibility score (SA_Score) and the synthetic complexity score (SCScore). These metrics offer heuristic evaluations of a molecule's ease of synthesis, while ASKCOS conducts explicit retrosynthetic analysis to determine viable synthetic pathways.
Key Findings
- Distribution Learning Models: Models trained on datasets with a higher fraction of synthesizable compounds (e.g., MOSES) tend to propose molecules that are synthesizable at comparable rates. This suggests that starting with a curated, synthesizable dataset can influence the output positively.
- Goal-Directed Generation Models: A significant proportion of high-scoring molecules proposed by these models are not synthesizable. The paper reveals that some compounds screened with SMILES GA and Graph GA methodologies are especially problematic, proposing nonsensical structures that, despite scoring well on evaluation metrics, lack practical synthetic pathways.
- Heuristic Biasing: The introduction of heuristic biases, particularly through the normalization of the objective function with synthesizability scores like SA_Score, considerably improves the fraction of synthesizable outputs. However, this improvement often comes at the cost of the primary objective function value.
Implications and Future Directions
This work highlights a fundamental consideration in the practical application of generative models in drug discovery: the necessity to balance molecular novelty and optimizable properties with synthesizability. The application of heuristic biases or constraints during the generation phase directly addresses this balance, though at the expense of raw objective optimization.
The authors advocate for further development of generative algorithms that intrinsically account for synthetic feasibility—potentially by integrating explicit synthesis planning methodologies or embedding synthetic constraints within the generation process itself. Future advancements in computational efficiency and synthesis prediction accuracy could also enable real-time incorporation of synthesizability assessments during molecule generation, enhancing their practical utility in drug discovery pipelines.
In conclusion, Gao and Coley's analysis provides a critical perspective on the current limitations and necessary advancements for integrating AI-generated molecular suggestions into reliable, real-world synthetic processes, marking a forward step in the melding of computational algorithms with experimental chemistry.