Improving synthesis prediction of small molecules at scale with RetroChimera

Improving synthesis prediction of small molecules at scale with RetroChimera - Microsoft Research

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At a glance

We report on the recent publication of our retrosynthesis model RetroChimera in the journal Nature (opens in new tab) .

The paper describes the model’s architecture as well as extensive validation studies, including the model’s ability to recall rare reaction types, and successful zero-shot transfer and fine-tuning on proprietary datasets.

We open-source RetroChimera’s implementation and weights in the hope that it will enable researchers to accelerate development of new medicinally relevant molecules and advanced materials.

Developing new medicines and materials requires making new molecules but planning how to make them is still largely manual, time-consuming, and costly. RetroChimera automatically proposes high-quality synthesis routes. The model combines two strong models with complementary strengths, learning how to rank their proposals to produce better predictions than either alone. In blind tests, PhD-level chemists prefer RetroChimera’s individual reaction predictions over preceding models and recorded literature reactions.

Custom-made molecules are unlocking advances in modern medicine, smart materials, and sustainable agriculture. Yet, progress is slowed by chemical synthesis—the time-consuming process of making new molecules from simpler building blocks in the lab. In addition, synthesis is a significant driver of drug development costs. So even as computational methods make it possible to explore large numbers of novel molecules, finding practical ways to synthesize them remains a critical challenge.

Figure 1: Planning a synthesis by working backward. Retrosynthesis starts with a target molecule and proposes successive disconnections into simpler precursors until purchasable building blocks are reached. The highlighted path shows a complete synthesis route; pale branches illustrate alternatives explored along the way. Circles represent molecules and squares represent reactions. For clarity, only a few branches are illustrated, with chemical structures shown for the target, one intermediate, and selected building blocks.

Retrosynthesis approaches this problem by working backwards from a target molecule, breaking it down step by step into simpler precursors (Figure 1). This process is comparable to playing strategic board games like chess and Go. It involves contemplating a wide range of possible immediate moves, or individual disconnections, while also requiring high-level strategic thinking to reach the end-to-end synthesis plan. However, the number of possible moves in retrosynthesis is much larger than in board games, and it is not obvious which moves would be available for a given molecule. Existing systems face major challenges, including recalling rare but strategically important reactions, robustness beyond the training distribution, and aligning with chemists’ expectations. As a result, retrosynthesis often requires highly specialized expertise, which hinders scaling and automation of scientific discovery.

Figure 2: Our framework for ensemble-based retrosynthesis with learned re-ranking which underpins RetroChimera. The ensemble receives a target molecule as the input, which is then processed by the sub-models. The model outputs are aggregated using a learning-to-rank strategy. While in this work we only investigate deep learning models as prediction sources (solid boxes), it is possible to add additional sources, for example calls to reaction databases or human-in-the-loop queries (dashed box).

In a paper recently published in the journal Nature (opens in new tab) , we present RetroChimera (opens in new tab) , a new framework for retrosynthesis prediction. It is built around two models (Figure 2). R-SMILES 2 , a Transformer-based de-novo model, predicts precursor molecules directly from the input molecule. This gives it the flexibility to learn reaction patterns directly from data. However, its unconstrained generation can also make it prone to hallucination.

NeuralLoc , in contrast, is a graph neural network- (GNN) based model that encodes both the target molecule and reaction templates as graphs. It selects reaction templates and predicts where they should be applied to the target molecule. Its predictions are grounded in reaction patterns extracted from the training data, so it tends to produce more accurate and reliable outputs. But it’s more constrained when encountering reactions not covered by the template library.

These differences actually turn out to be a strength. Rather than making the same kinds of predictions, the two models capture complementary patterns in chemistry and specialize in different reaction types. R-SMILES 2 performs particularly well on reactions that involve large changes over the course of the reaction, while NeuralLoc excels in reactions of low precedence and...