ReTRACE: Feedback-Guided LLM Agent in a Chemistry Harness for Retrosynthesis Planning

Jinxiang Xie; Yijian Zhang; Qingyuan Yi; Jianyi Wang; Hao Dong

cs.AIOct 17, 2026

ReTRACE: Feedback-Guided LLM Agent in a Chemistry Harness for Retrosynthesis Planning

Authors: Jinxiang Xie, Yijian Zhang, Qingyuan Yi, Jianyi Wang, Hao Dong

Organizations: State Key Laboratory of Analytical Chemistry for Life Science, Kuang Yaming Honors School, Chemistry and Biomedicine Innovation Center (ChemBIC), ChemBioMed Interdisciplinary Research Center at Nanjing University, Nanjing University, Nanjing 210023, China · School of Medicine, Guangxi University, Nanning, 530004, China

Abstract

Computer-aided retrosynthesis has advanced through single-step prediction, tree search and route scoring, but complex targets still require judgement over the whole route, where stereochemistry, functional-group compatibility, cost and greenness interact. Here we present \mbox{ReTRACE} (REtrosynthesis with Tool-orchestrated Reasoning And Context-aware Evidence), an agent that places a large language model (LLM) in a chemistry harness that integrates molecular tools, complementary single-step prediction engines, a purchasable starting-material library, a route memory and a multi-criterion route evaluator. Routes are judged by a new metric that combines programmatic checks with adversarial LLM review, and its verdicts are compared with a synthetic chemist's. The agent learns continually from evaluator feedback during search, diagnosing weak routes and backtracking to revise them. With Claude Opus 4.8, at least one of the top 10 routes proposed by ReTRACE passed the audit for 87.4%, 95.0% and 86.7% of targets on the three benchmarks. Two prospective wet-lab syntheses show that ReTRACE route proposals can be converted into executable procedures after expert revision.

Figures & tables

Figure 1: ReTRACE harness, two decision instances from real search trajectories and the response to score feedback within a run. a , An LLM coordinator works inside a harness of heterogeneous chemistry tools, deterministic gates that check every tool call, tool result and saved route, and a route memory that carries partial routes, past decisions and abandoned branches into the next round. Route scores return to the coordinator as feedback within a run. Ranked routes are judged by Audit@k as passed or failed, and selected routes go to chemist review and wet-lab synthesis. b , Adjudicating conflicting single-step suggestions. The coordinator keeps both a late-stage nitro reduction and a glycidyl N-alkylation open, checks whether the electrophile can be purchased and expands the shared lactam core with both engine classes before choosing. c , Diagnosis and backtracking. Route B scores 0.75 against 0.80 for the best route. The coordinator attributes the gap to lower cost and feasibility from the added steps, saves route B as an alternative and backtracks to shorten the linear sequence. Monologues in b and c are verbatim excerpts from run trajectories. d , Score feedback within a run in 670 completed Retro*-190 runs. Mean score of the best route so far (dark green) and of the j-th scored route (light green; left axis), and percentage of runs in which at least one of the first j scored routes passed the audit (grey, dashed; right axis).

Figure 2: Validity, reference recovery and complementarity of ten single-step engines, and engine agreement in planned routes. a–c, 50,000 USPTO-50K reactions, with engines ordered by top-10 recovery and reference matching. a , Fraction of reactions with output, with a rank-1 prediction that passes RDKit screening, with a rank-1 prediction that passes the LLM audit (95% Wilson interval), and with an exact top-1 reference match. b , Reactions recovered within the top 10 by that engine alone. c , Percentage of the top-10 misses of the row engine that the column engine recovers within its top 10, and the right column gives the top-10 miss rate of each engine. d , Route steps in the Retro*-190 runs that passed or failed the audit, by the number of runtime engines that proposed the step.

Controller / baseline	Run success (%)	Audit@ $k$ (%) $k=1$ $k=3$ $k=5$ $k=10$				Rank-1 route length vs reference (%) Shorter Equal Longer None
Retro*-190 (N = 190)
Claude Opus 4.8	97.9	70.0	78.4	83.7	87.4	65.8	15.8	18.4	0.0
GPT-5.6 Sol fast	100.0	64.2	76.8	78.4	80.0	64.2	16.3	19.5	0.0
GPT-5.6 Sol standard	100.0	65.8	75.3	79.5	82.6	64.7	11.6	23.7	0.0
GPT-5.6 Sol thorough	100.0	63.7	72.6	79.5	82.1	60.0	15.8	24.2	0.0
Qwen3.6-35B-A3B	91.6	23.7	33.2	33.7	34.7	74.7	12.1	8.4	4.7

Table 1: Route-planning performance of coordinating models and unified audit baselines across three datasets, with Claude Opus 4.8 tool-group ablations on Retro*-190.

Figure 3: Conventional tree-search control and ReTRACE route-level control, with one real decision trajectory. a , Conventional MCTS or Retro* controllers repeatedly select, expand, evaluate and backpropagate within a fixed candidate space. ReTRACE instead runs a agent loop that diagnoses weak steps, introduces alternative precursors and restructures a route before saving it. b , Five routes saved for one representative target, with their total scores. Routes 2 and 3 only reorder the steps of Route 1, Route 4 uses a Boc-last assembly, and Route 5 changes the disconnection class and reaches the highest score (0.8316) after its unpurchasable BPin–aryl ether leaf is disconnected further.

Figure 4: Prospective wet-lab validation of ReTRACE-assisted route design. a , Compound C1. b , Compound C2. Each panel compares the initial ReTRACE route with the chemist-revised experimental route. C1 required a revised protection strategy and an earlier Suzuki coupling, whereas C2 retained the core sequence with localized substrate and condition changes.

Figure 5: Chemical failure modes identified by the route-level adversarial audit. a , Operational grouping of the 497 audit-failed routes of GPT-5.6 Sol (standard) on Retro*-190. Each route is assigned the first family A–D that occurs among its fatal steps (filled bars); hatched extensions add routes that also contain that family but were assigned an earlier one. b , a Knoevenagel condensation should keep all 19 reactant carbons; the drawn product has no counterpart of carbon 2 and contains 18. c , an intramolecular oxa-Michael addition should join alcohol oxygen 3 to $\beta$ -carbon 1 and close a seven-membered ring; the drawn product joins it to $\alpha$ -carbon 2 and turns carbon 1 into a methyl group, giving a six-membered ring. d , a one-step construction of a $\delta$ -carboline (pyrido[3,2- b ]indole) from a phenylenediamine and a dibromopyridine ester forms new C–N and C–C bonds while the nitrogen count falls from five to four, with no nitrogen-containing leaving group.

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 7 — Figure A1: NMR and HRMS spectra of compound C1 and its intermediates. Compound numbers follow Figure 4 ; spectra are reproduced as recorded.

Panel of Figure 8 — Figure A2: Continued. NMR and HRMS spectra of compound C1 and its intermediates.

Panel of Figure 9 — Figure A3: Continued. NMR and HRMS spectra of compound C1 and its intermediates.

Panel of Figure 10 — Figure A4: Continued. NMR and HRMS spectra of compound C1 and its intermediates.

Figure A5: Continued. NMR and HRMS spectra of compound C1 and its intermediates.

Panel of Figure 12 — Figure A6: NMR and HRMS spectra of compound C2 and its intermediates. Compound numbers follow Figure 4 ; spectra are reproduced as recorded.

Panel of Figure 13 — Figure A7: Continued. NMR and HRMS spectra of compound C2 and its intermediates.

Panel of Figure 14 — Figure A8: Continued. NMR and HRMS spectra of compound C2 and its intermediates.

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Multi-step retrosynthesis planning seeks to decompose a target molecule into commercially available building blocks through a sequence of feasible reactions. The vast combinatorial search space makes this task challenging even for expert chemists. Traditional methods combine tree search with offline-trained value networks that score candidates in isolation, without reasoning about complete multi-step routes. Recent work leverages Large Language Models (LLMs) for this task, but relies on simple interfaces that limit exploration of the full search space. We introduce RetroAgent, an LLM agent that bridges symbolic search and neural reasoning through a harness with structured memory. Through memory and chemistry tools, the agent observes the full search state, including explored routes, available alternatives, and properties of intermediates, enabling informed decisions grounded in both global progress and domain knowledge. Experiments on in-distribution and out-of-distribution benchmarks demonstrate that RetroAgent delivers strong performance and generalization.

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