NOCTIS

NOCTIS constructs and analyzes graph networks from chemical reaction data to enable network-based reaction analysis and route mining for synthesis planning.

Key Features:

• Reaction Network Construction: NOCTIS builds Networks of Organic Chemistry (NOCs) from reaction strings using graph-based representations.
• Parallel Processing: The toolkit supports parallel processing of large datasets.
• Export Capabilities: Networks can be exported to common Python formats including NetworkX and pandas.
• Neo4j Foundation: The architecture is built on Neo4j technology with modular, extensible design and open-source dependencies.
• Route Enumeration Plugin: A companion plugin performs exhaustive route enumeration by traversing graph-encoded reactions to assemble all valid synthetic routes.
• Network Connectivity Analysis: The toolkit enables analysis of network connectivity and assessment of synthetic trees.

Scientific Applications:

• Synthesis Route Mining: NOCTIS mines large-scale reaction datasets from literature, patents, and high-throughput experimentation to identify valid synthetic routes and prevent redundant exploration.
• Knowledge Reuse in Synthesis Planning: The route enumeration plugin supports knowledge reuse by assembling all valid synthetic routes from graph-encoded reactions.
• Data-Driven Route Design: The toolkit lays groundwork for scalable, data-driven design of efficient and sustainable synthetic routes through network topology analysis.

Methodology:

NOCTIS is built on LinChemIn and uses graph-based representations to encode reaction space topology. The route enumeration plugin traverses graph-encoded reactions to assemble valid synthetic routes. The underlying algorithm is documented in detail along with its limitations. Demonstration used the MIT USPTO-480k dataset.