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Development case · blind model judgment pending independent human review; not a formal Benchmark conclusion.
AutocompleteNo-Evidence version preferred13 / 162 · dd158ce70abf9ca5

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 tell whether Evidence helps?

At the same writing position, with the same model and task, how does supplying retrieved paper passages change the first output? We compare matched versions and retain ties, unusable outputs and incomplete reviews.

SAME DRAFT · EVIDENCE ON OR OFF

01 · FIXED WRITING POSITION

MANUSCRIPT

Same writing position ▌

Matched autocomplete at the same draft position

02 · TWO MATCHED INPUTS

SHARED BY BOTH

Manuscript context, model, task and prompt

A · With Evidence

Retrieved paper passages supplied

B · Without Evidence

No retrieved passages supplied

LLM

Same model and version

A → first output

B → first output

Blind judge agent

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

Order check: X / Y → Y / X

HOW DOES BLIND JUDGING WORK?

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

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 autocomplete positions stratified?

Before seeing generation outcomes, we check whether a retrieved passage contains a specific proposition that directly supports the next writing move. Those positions appear in the left opportunity group; the rest are ordinary positions on the right. We select a balanced sample from admitted papers in each field. The 50/50 split is experimental, not a measure of how often either type occurs in writing.

AUTOCOMPLETE · 110

Biology, statistics and astrophysics

55 positions on each side; statistics uses the ten-paper rerun.

AUTOCOMPLETE · 52

Psychology and climate science

26 positions on each side; psychology includes nine papers and climate science four.

How are the table percentages calculated?

Across five fields, 51 of 81 left-column positions preferred the Evidence version. Ties, pairs where both versions were unusable and incomplete reviews remain in the denominator.

51Evidence version preferred
÷
81All positions in this group
=
63%Evidence preference in this group

Source contribution is a separate review: 16 of 22 Evidence wins entered into source review directly used retrieved papers; another 29 wins await review.

These are development-stage model judgments pending independent human review. They are not formal Benchmark conclusions and do not, on their own, establish causality.

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

…ave achieved initial success. In this survey, we conduct a comprehensive investigation of advanced deep learning-based models for reaction and retrosynthesis prediction. We summarize the design mechanisms, strengths, and weaknesses of state-of-the-art approaches. Then, we discuss the limitations of current solutions and open challenges in the problem itself. Finally, we present promising directions to facilitate future research. To our knowledge, this paper is the first comprehensive and systematic survey that seeks to provide a unified understanding of reaction and retrosynthesis prediction.

…rocess is notoriously labor-intensive and costly. As Eroom’s law suggests Scannell et al. 2012 , the exploration of new drugs becomes increasingly slower and more expensive over time. Therefore, it is natural and significant to leverage machine learning techniques to accelerate the drug discovery process. In recent years, the use of deep learning approaches to enhance different stages of drug discovery has become prevalent due to the rise of deep learning. Among these stages, reaction prediction and retrosynthesis prediction are two fundamental steps that can benefit from deep learning tools.

↓ 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 positionJudgment: No-Evidence version preferred

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.

Evidence

Same model and position · retrieved passages supplied · first output

Not preferred in this comparison

Recent studies have sought to combine expert experience with deep learning by introducing structural motifs into retrosynthesis, treating the task as a molecular editing problem. While such motifs can simplify the editing process, they also enlarge the vocabulary and can reduce predictive consistency.

Passages supplied to the Evidence version

MotifRetro: Exploring the Combinability-Consistency Trade-offs in retrosynthesis via Dynamic Motif Editing

Combining expert experience with deep learning is a promising direction for retrosynthesis prediction, and some recent studies Gao et al. (2022) ; Liu et al. (2022) ; Dai et al. (2019) ; Somnath et al. (2021) have followed this approach by introducing structural motifs into retrosynthesis, which is…

Read full passage excerpt

Combining expert experience with deep learning is a promising direction for retrosynthesis prediction, and some recent studies Gao et al. (2022) ; Liu et al. (2022) ; Dai et al. (2019) ; Somnath et al. (2021) have followed this approach by introducing structural motifs into retrosynthesis, which is essentially a molecular editing problem. As shown in Figure 1 , introducing motifs can simplify the molecular editing process, but it can also increase the vocabulary size and decrease predictive consistency.

No Evidence

Same model and position · no retrieved passages · first output

Preferred by blind review

Reaction prediction aims to determine the products of a given set of reactants, whereas retrosynthesis prediction seeks to identify plausible reactants that could produce a target molecule. The two tasks are complementary: the former models the forward direction of chemical transformations, while the latter reasons backward from a target compound to its synthetic precursors.

Blind model judgment

The blinded model review preferred the no-Evidence version. Its original rationale appears below.

Read the original model judgment

Both outputs are coherent academic paragraphs that could follow the draft. However, Output B is preferable for this autocomplete task because it directly introduces and defines the two core topics (reaction prediction and retrosynthesis prediction) that the survey promises to cover, as established in the first paragraph's thesis statement ('reaction and retrosynthesis prediction'). This creates logical continuity: the draft introduces the survey's scope, discusses drug discovery motivation, and then Output B defines the key terms before the survey proceeds to detailed analysis. Output A, while source-grounded, jumps ahead to a specific technical approach (motif-based retrosynthesis) that would more naturally appear in a later section about methods or recent advances, not in the introductory positioning paragraph. Output B requires less structural reorganization of the manuscript and better serves the survey's expository arc.

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.