The Engine

The Retrosynthesis Planning engine, powered by the ReaSyn model, predicts detailed synthetic pathways for target molecules within the Revilico Quantum Chemistry environment. It provides chemists and computational scientists with complete reaction sequences, from available precursors to final products, allowing exploration of feasible synthetic routes and analog compounds when direct synthesis is impractical. Integrated into Revilico’s pipeline management interface, it supports molecular input via SMILES strings and delivers structured reaction pathways optimized for chemical plausibility, cost, and synthesizability.

The Algorithm

ReaSyn applies an encoder-decoder transformer trained on large-scale reaction templates and building-block libraries to translate molecular SMILES representations into text-based synthesis pathways.

• Input: The target molecule is represented as a SMILES string.

• Encoding: The model encodes molecular structure and chemical context into a latent representation capturing bond connectivity, reactivity, and substructure similarity.

• Decoding: The decoder sequentially generates a chain-of-reaction representation, detailing intermediates, reactants, and reaction types leading to the desired product or its most synthesizable analog.

The engine incorporates a similarity-driven fallback mechanism: when a molecule is not directly synthesizable, it proposes structurally close alternatives with valid synthetic routes. Each step is generated in text form and parsed back into graph-based representations for visualization and further computational validation. The model operates on NVIDIA GPU infrastructure, leveraging CUDA acceleration for low-latency inference and scalable batch processing.

Algorithm Validation

Performance validation follows multiple independent benchmarking datasets, including ChEMBL, Enamine REAL, and ZINC250k. The model achieves high reconstruction rates for known synthetic routes and top-tier accuracy in predicting chemically valid reaction chains. Comparative evaluations show state-of-the-art performance against existing retrosynthesis models on synthesizability and diversity metrics. ReaSyn’s predictions have been further evaluated through human chemist review and automated reaction feasibility scoring, confirming consistency with experimentally validated reactions. Model explainability tools trace attention weights to reaction centers, allowing interpretability of key substructural contributions to each prediction.

Scientific Impact

The Retrosynthesis Planning engine enables automated, high-fidelity pathway generation at the interface of AI and organic chemistry. It allows researchers to rapidly prototype synthetic plans, optimize reaction steps, and assess analog feasibility before entering the wet lab. By providing machine-generated synthetic logic for complex molecules, it reduces the experimental trial burden and accelerates the translation from virtual design to bench synthesis. The model’s text-based reaction encoding also supports coupling with generative chemistry and property optimization engines, forming a closed loop between design, synthesis, and evaluation.

Business Impact

Within discovery pipelines, the Retrosynthesis Planning engine enhances productivity by streamlining synthetic feasibility assessments and reducing the need for external synthesis prediction tools. Organizations gain the ability to rank candidate compounds not only by potency or selectivity but also by ease of synthesis, manufacturing cost, and accessibility of starting materials. Integrated directly into Revilico’s environment, this engine enables automated planning for medicinal chemistry, material design, and process optimization, transforming retrosynthesis from a manual expert task into an AI-assisted, data-driven workflow that shortens development timelines and lowers R&D expenditure.