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This case was judged by an anonymized AI reviewer and has not been independently reviewed by people; inspect the draft, cards, and both outputs to assess it.
AutocompleteWithout cards better146 / 280 · beu-590037ccb152ecea

A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges

Quantitative biology · 2306.15890v1

FLOWING EVIDENCE BENCHMARK

How do we compare writing with and without cards?

At the same writing position, the same model produces one first result with cards and one without. All 280 pairs enter the overall anonymized review, which assesses whether a decision not to continue fits the draft.

Overall: 138 with-card wins, 26 ties, 107 no-card wins, 5 pairs where neither was usable, and 4 inconclusive judgments. Both sides wrote text in 218 pairs; 48 had a deliberate stop and 14 included an invalid output.

SAME DRAFT · WITH OR WITHOUT CARDS

01 · FIXED WRITING POSITION

MANUSCRIPT

Same writing position ▌

Matched autocomplete at the same draft position

02 · TWO MATCHED VERSIONS

SHARED BY BOTH

Draft, writing task, and generation model

A · With cards

Relevant literature cards supplied

B · Without cards

No literature cards supplied

Generation model

Same model and version

A → first output

B → first output

Anonymized AI judge

First outputs are anonymized as X and Y

Continuation review: accuracy, fit to the writing task and usability
Returns: X preferred / tie / Y preferred / both unusable

Review again with the answer order swapped

HOW DOES BLIND JUDGING WORK?

① Anonymize both outputs
The AI judge sees the same draft and both first outputs without knowing which received cards.

② Compare and swap order
The judge applies task-specific criteria in X/Y and then Y/X order.

③ Review disagreements
A third pass resolves disagreements. Incomplete reviews remain in the denominator.

How are positions and cards selected?

We selected 40 writing positions in each of seven fields, for 280 comparisons. The position and relevant literature cards were set before each comparison, so both versions began from the same draft.

The main result retains every frozen position, including places where cards might not add a useful next move. This avoids selecting cases based on their outputs, but 49% describes the whole cohort, not a win rate where evidence clearly had an opportunity to help. This cohort did not record that opportunity label for each position before generation, so those two groups cannot yet be separated reliably.

CARD USE · 140/280

Reported card use in continuations with text

The model reports whether a card affected its continuation. In 140 of 280 tests it produced continuation text and reported card use. The previous count of 150 included 10 cases without continuation text and has been corrected.

COMPARISON · 70 / 7 / 30

Which written version was better?

Of the 107 reported-use cases where both versions produced text, the blinded AI judge preferred the with-card version 70 times, found 7 about the same, and preferred the without-card version 30 times.

How are the two table percentages calculated?

ADOPTION COVERAGE

140reported use with text
÷
280all positions
=
50%

WITH-CARD VERSION PREFERRED

70with cards better
÷
107comparable tests
=
65%

The percentages use different groups: 50% counts continuations with text reporting card use among all 280 tests; 65% covers the 107 such cases with text on both sides. The generating model reports card use, while an anonymized AI judge compares writing quality.

Card use is self-reported by the generator. The anonymized AI judge sees the draft and source snippets, so source-aware preference remains possible; there was no independent human review. The overall result describes these 280 frozen positions, not a win probability for any writing task.

Manuscript writing position

Text excerpt · not a PDF page

Research manuscript · excerpt

A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges

1 Introduction

In actual production environments, chemists often aim to design synthesis routes that can lead to target molecules through a series of chemical reactions. One common strategy is to decompose target molecules into simpler precursor structures that can be synthesized more easily, a process known as retrosynthetic analysis.

…l structured prediction problem posed by reaction prediction and single-step retrosynthesis prediction. For the remainder of this survey, we will refer to single-step retrosynthesis prediction as simply retrosynthesis prediction. Reaction prediction is another key task in organic synthesis analysis. A robust reaction prediction model can provide insight into the underlying mechanisms of biochemical reactions, as well as generate virtual reactions to expand the database for retrosynthetic planning. In summary, reaction and retrosynthesis prediction are interrelated and can enhance one another.

↓ Next paragraph was generated here

The highlighted text is near the writing position. This is a layout of the evaluation input, not a PDF screenshot.

A matched comparison at the same writing positionOverall AI review: Without cards better

Both arms use the same model, task and manuscript context. Only the retrieved passages differ. The first recorded output from each arm is shown below.

Read overall AI review rationale

A = with cards · B = without cards

Both outputs provide coherent continuations, but B is superior for this specific position in the draft. The visible text ends with 'In summary, reaction and retrosynthesis prediction are interrelated and can enhance one another' — a concluding summary statement. Output A continues with a technical framing of how the tasks are formulated (sequence-to-sequence/graph-to-graph), which introduces new substantive content that feels like a new section beginning rather than a completion of the current thought. Output B directly addresses the logical next move: it takes the interdependence just asserted and identifies a gap (studied in isolation), then pivots to the survey's purpose. This creates a smooth argumentative bridge — 'they're interrelated' → 'yet they've been studied separately' → 'hence this unified survey.' This fits the survey introduction genre far better and requires no repair. Output A's content, while accurate, would work better after establishing the problem/gap rather than immediately after a summary statement. Additionally, B's 'motivating the unified perspective adopted in this survey' elegantly sets up what follows.

Evidence

Same model and position · retrieved passages supplied · first output

Not preferred in this comparison

Retrosynthesis prediction and reaction prediction are typically formulated as a mapping between reactant and product molecules, and recent work has increasingly framed both tasks as sequence-to-sequence or graph-to-graph translation problems.

Passages supplied to the Evidence version

Card 1 · Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits

The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely synthesis pathway cannot be applied due to additional constraints, which requires proposing alternative chemical…

Read full passage excerpt

The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely synthesis pathway cannot be applied due to additional constraints, which requires proposing alternative chemical reactions. With this in mind, we present Molecule Edit Graph Attention Network (MEGAN), an end-to-end encoder-decoder neural model. MEGAN is inspired by models that express a chemical reaction as a sequence of graph edits, akin to the arrow pushing formalism. We extend this model to retrosynthesis prediction (predicting substrates given the product of a chemical reaction) and scale it up to large data sets. We argue that representing the reaction as a sequence of edits enables MEGAN to efficiently explore the space of plausible chemical reactions, maintaining the flexibility of modeling the reaction in an end-to-end fashion and achieving state-of-the-art accuracy in standard benchmarks. Code …

Card 2 · Learning Graph Models for Retrosynthesis Prediction

Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule. A key consideration in building neural models for this task is aligning model design with strategies adopted by chemists. Build…

Read full passage excerpt

Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule. A key consideration in building neural models for this task is aligning model design with strategies adopted by chemists. Building on this viewpoint, this paper introduces a graph-based approach that capitalizes on the idea that the graph topology of precursor molecules is largely unaltered during a chemical reaction. The model first predicts the set of graph edits transforming the target into incomplete molecules called synthons. Next, the model learns to expand synthons into complete molecules by attaching relevant leaving groups. This decomposition simplifies the architecture, making its predictions more interpretable, and also amenable to manual correction. Our model achieves a top-1 accuracy of $53.7\%$, outperforming previous template-free and semi-template-based methods.

Card 3 · RetroXpert: Decompose Retrosynthesis Prediction like a Chemist

Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate or assist in the retrosynthesis analysis, various retrosynthesis prediction algorithms have been propose…

Read full passage excerpt

Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate or assist in the retrosynthesis analysis, various retrosynthesis prediction algorithms have been proposed. However, most of them are cumbersome and lack interpretability about their predictions. In this paper, we devise a novel template-free algorithm for automatic retrosynthetic expansion inspired by how chemists approach retrosynthesis prediction. Our method disassembles retrosynthesis into two steps: i) identify the potential reaction center of the target molecule through a novel graph neural network and generate intermediate synthons, and ii) generate the reactants associated with synthons via a robust reactant generation model. While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation.

Card 4 · Root-aligned SMILES: a tight representation for chemical reaction prediction

Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates synthesis prediction as a sequence-to-sequence translation problem, where the typical SMILES is adopted for molecule rep…

Read full passage excerpt

Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates synthesis prediction as a sequence-to-sequence translation problem, where the typical SMILES is adopted for molecule representations. However, the general-purpose SMILES neglects the characteristics of chemical reactions, where the molecular graph topology is largely unaltered from reactants to products, resulting in the suboptimal performance of SMILES if straightforwardly applied. In this article, we propose the root-aligned SMILES (R-SMILES), which specifies a tightly aligned one-to-one mapping between the product and the reactant SMILES for more efficient synthesis prediction. Due to the strict one-to-one mapping and reduced edit distance, the computational model is largely relieved from learning the complex syntax and dedicated to learning the chemical knowledge for reactions. We compare the proposed R-S …

Card 5 · Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction

Retrosynthesis is the task of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found. Consequently, the goal is to provide a valid synthesis route for a molecule. As more single-step models develop, we see increas…

Read full passage excerpt

Retrosynthesis is the task of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found. Consequently, the goal is to provide a valid synthesis route for a molecule. As more single-step models develop, we see increasing accuracy in the prediction of molecular disconnections, potentially improving the creation of synthetic paths. Multi-step approaches repeatedly apply the chemical information stored in single-step retrosynthesis models. However, this connection is not reflected in contemporary research, fixing either the single-step model or the multi-step algorithm in the process. In this work, we establish a bridge between both tasks by benchmarking the performance and transfer of different single-step retrosynthesis models to the multi-step domain by leveraging two common search algorithms, Monte Carlo Tree Search and Retro*. We show that models designed for single-step retrosynthesis, when extended t …

Model-reported card use

Card 4 · terminology

Affected output: sequence-to-sequence or graph-to-graph translation problems

Model explanation: This row supplied the field-native term 'sequence-to-sequence translation problem' used to characterize how the tasks are formulated.

Card 2 · scope

Affected output: Retrosynthesis prediction and reaction prediction are typically formulated as a mapping between reactant and product molecules

Model explanation: This row anchored the framing of retrosynthesis prediction as a precursor-to-target mapping, keeping the sentence scoped to the task definition rather than a specific method.

No Evidence

Same model and position · no retrieved passages · first output

Preferred by blind review

Despite this interdependence, the two tasks have largely been studied in isolation, with separate model architectures, datasets, and evaluation protocols developed for each. This separation limits the transfer of chemical knowledge between reaction and retrosynthesis prediction, motivating the unified perspective adopted in this survey.

Earlier card-use comparison

The earlier diagnostic compared only cases with reported card use and text on both sides; its scope differs from the overall AI review above.

The blinded model review preferred the Evidence version. Its original rationale appears below.

Read the original model judgment

Output A contains unsupported claims. It states that reaction and retrosynthesis prediction 'have largely been studied in isolation' with 'separate model architectures, datasets, and evaluation protocols' and that 'This separation limits the transfer of chemical knowledge.' None of the supplied sources support these claims about isolation, separate datasets/protocols, or knowledge transfer limitations. The sources discuss specific models and approaches but do not make general claims about how these tasks have been studied in isolation or that this limits knowledge transfer. The phrase 'motivating the unified perspective adopted in this survey' also makes an unsupported claim about the survey's motivation that isn't grounded in the draft or sources. Output B is grounded in the sources. The claim that both tasks are 'formulated as a mapping between reactant and product molecules' is supported by multiple sources (e.g., Root-aligned SMILES describes 'synthesis prediction as a sequence-to-sequence translation problem,' and Learning Graph Models describes graph edits transforming target to synthons). The framing as 'sequence-to-sequence or graph-to-graph translation problems' is directly supported by the Root-aligned SMILES source (sequence-to-sequence) and the Learning Graph Models/MEGAN sources (graph-based approaches). Output B provides a coherent continuation that transitions logically from the interrelation discussion to how the tasks are computationally formulated, without adding unsupported factual claims.

The passages are input material; their presence does not mean an output used them or that they support every claim. Draft and source passages are excerpted; no simulated scores or PDF appear here.