Retrosynthesis Planning via Worst-path Policy Optimisation in Tree-structured MDPs
Authors: Mianchu Wang
Giovanni Montana
Venue: NeurIPS 2025 poster ()
Keywords: Retrosynthesis planning
reinforcement learning
self-imitation learning
Primary area: machine_learning_for_sciences
TL;DR: We propose Interactive Retrosynthesis Planning, a novel framework that learns to construct retrosynthetic routes by interacting with tree MDPs and optimising a worst-path objective by self-imitation learning.

Abstract: Retrosynthesis planning aims to decompose target molecules into available building blocks, forming a synthetic tree where each internal node represents an intermediate compound and each leaf ideally corresponds to a purchasable reactant. However, this tree becomes invalid if any leaf node is not a valid building block, making the planning process vulnerable to the "weakest link" in the synthetic route. Existing methods often optimise for average performance across branches, failing to account for this worst-case sensitivity. 
In this paper, we reframe retrosynthesis as a worst-path optimisation problem within tree-structured Markov Decision Processes (MDPs). We prove that this formulation admits a unique optimal solution and provides monotonic improvement guarantees. Building on this insight, we introduce Interactive Retrosynthesis Planning (InterRetro), a method that interacts with the tree MDP, learns a value function for worst-path outcomes, and improves its policy through self-imitation, preferentially reinforcing past decisions with high estimated advantage. 
Empirically, InterRetro achieves state-of-the-art results — solving 100\% of targets on the Retro*-190 benchmark, shortening synthetic routes by 4.9\%, and achieving promising performance using only 10\% of the training data.

Retrosynthesis Planning via Worst-path Policy Optimisation in Tree-structured MDPs
Mianchu Wang"
Giovanni Montana
"University of Warwick
The Alan Turing Institute
fmianchu.c wang, g mmmonaana@@karricc..ek
Abstract
Retrosynthesis planning aims to decompose target molecules into available building blocks, forming synthetic tree where each internal node represents an intermediate compound and each leaf ideally corresponds to purchasable reactant. However, this tree becomes invalid if any leaf node is not a valid building block, making the planning process vulnerable to the "weakest link" in the synthetic route. Ex- isting methods often optimise for average performance across branches, failing to account for this worst-case sensitivity. In this paper, reframe retrosynthesis as worst-path optimisation problem within tree-structured Markov Decision Processes (MDPs). We prove that this formulation admits unique optimal solution and pro- vides monotonic improvement guarantees. Building on this insight, we introduce Interactive Retrosynthesis Planning (InterRetro), method that interacts with the tree MDP, learns a value function for worst-path outcomes, and improves its policy through self-imitation, preferentially reinforcing past decisions with high estimated advantage. Empirically, InterRetro achieves state-of-the-an results solving 100% of targets the Retro*-190 benchmark, shortening synthetic routes by 4.9%, and achieving promising performance using only 10% of the training data.
Introduction
Retrosynthesis aims to identify the reactants needed to synthesise a target molecule with desired properties. As fundamental task in computer-aided molecular design, retrosynthesis underpins progress in drug discovery and materials science 39 A key challenge in retrosynthesis is single-step prediction, which involves predicting the reactants for given product (illustrated in Figure [.. While recent approaches using supervised learning have achieved human-level accuracy in this task 2 38), their practical applicability limited, the suggested reactants often commercially unavailable. Unlike single-step prediction, multi-step planning recursively decomposes the target into simpler intermediates, aiming to construct a synthetic route whose leaf nodes purchasable compounds. This process naturally forms a sequential decision decision-making problem, where early decisions influence future steps and the overall outcome 7 21).
Most methods tackle this decision-making problem using heuristic search, with Monte Carlo Tree Search (MCTS) being widely adopted for its ability balance exploration and exploitation 17 24
In this framework, a single-step retrosynthesis model suggests candidate reactions and predicts the most likely reactants for given product. During simulations, MCTS selects reactions by weighing their estimated value against their exploration potential. After simulating a complete synthetic route, the values of the decisions made along the path are updated to guide future searches more effectively. Building on this foundation, recent studies have proposed various enhancements to improve the trade-off to stabilise value estimation
37 However, these
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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methods heavily rely on time-consee consuming real-tim time search, requiring hundreds of model calls for each molecule, limiting their practical utility in large-scale molecular design scenarios.
To improve search efficiency, recent work fine-tunes pre-trained single-step models by imitating decisions made during successful heuristic search trajectories (
TO 34 The key insight is that reaction choices selected after look-ahead planning are often more reliable than those proposed by the original model [22 28 By training on these improved decisions, the resulting policies can propose more plausible reactions and accelerate the search for viable synthetic routes. However, this fine-tuning process adapts the model to the distribution of molecules encountered during search, rather than those seen in direct inference. As result, the model may perform well when used within the search loop but struggle to construct full synthetic routes independently, limiting its utility in settings where fast, search-free freeeefeeeeeeee planning is desired.
In this paper, we propose novel perspective: reframing retrosynthesis planning as a worst-path optimisation prob- lem in tree-structured Markov Decision Processes (tree MDPs). We observe that existing approaches typically optimise for average cumulative rewards across all root- to-leaf paths in the synthetic tree [TO] 21] overlooking a critical insight: a synthetic route is only valid if every root- to-leaf path terminates at a purchasable compound. Even single unsuccessful path invalidates the entire synthetic route, making the worst-performing path the limiting fac- tor in overall performance. This observation leads us to introduce new "worst-path" objective that focuses ex- plicitly on improving the most challenging path the synthetic route.
Single-step Prediction
Product
Reactants
Synthesazod
by
Multi-step Planning
Target
Intermediates
Building Blocks
Building this novel formulation, we develop Interac- tive Retrosynthesis Planning (InterRetro)- framework for multi-step retrosynthesis that learns to generate com- plete synthetic routes without search at inference time, InterRetro treats single-step model as an agent operating within the tree MDP, recursively decomposing molecules into reactants through environment interactions. The agent is improved via weighted self-imitation of its past suc- cessful decisions to encourage shallow synthetic trees, effectively bootstrapping its performance. In particular, InterRetro operates three key steps. Firstly, the agent in- teracts with the tree MDP to construct complete synthetic routes. Secondly, it identifies successful subtrees whose leaf nodes correspond to commercially available compounds. Finally, it fine-tunes the policy to imitate decisions from these subtrees, with theoretical guarantees of monotonic improvement and convergence unique optimal value function. This iterative self-improvement allows InterRetro to eliminate the need for real-time search while maintaining high planning quality.
Figure 1: Single-step prediction decom- poses a molecule into reactants, whereas multi-step planning searches for syn- thetic route, aiming to reach purchasable building blocks.
This paper makes four key contributions: (1) we introduce the novel worst-path optimisation frame- work for tree-structured MDPs, specifically designed for problems where the weakest component determines the overall success; (2) we develop a weighted self-imitation learning algorithm with monotonic improvement guarantees for optimising this worst- potst-path objective, proving the existence of a unique optimal solution through Bellman optimality analysis; (3) we apply this framework to retrosynthesis through InterRetro, a search-free approach to multi-step planning; and (4) we empirically demonstrate that InterRetro outperforms sfate-of-the-art (SOTA) methods in terms of success rate (achieving up to 100% on benchmark datasets), route length (reducing steps by 4.9%), and sample efficiency (reaching 92% of full performance with only 10% of training data).
2 Related Work
Single-step Prediction. The fundamental task in retrosynthesis is single-step prediction, which identifies the reactants that produce given target molecule. Early approaches are template-based,

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where reaction templates are extracted from patents and literature data [20], and a model selects the most appropriate template to decompose a molecule 23 Later methods propose template- free strategies, directly mapping the SMILES string of the target molecule to that of the reactants without relying on predefined transformation patterns B 26 Recent studies introduce semi-template- based approaches, which combine both paradigms by first identifying intermediate structures and then completing them into full reactants - either by generating leaving groups [25], SMILES tokens [5] 32. or graph edits (30, 38). While these single-step methods achieve high accuracy in predicting known reactions, they typically optimise for likelihood of historical reactions rather than synthesisability from commercially available compounds. In contrast, our approach explicitly favours reactions that lead to purchasable building blocks, enhancing downstream planning efficiency and practical upplicability.
Multi-step planning aims to construct a synthetie tree rooted at the target molecule, expanded by a single-step model, and terminating at commercially available compounds. Existing methods rely on heuristic search to find viable synthetic routes. For example, [6] formulates the retrosynthesis problem
Multi-step Planning.
an AND/OR tree search and adopts proof-number search to find the optimal solution; Retro* * [ explores the ANDIOR tree using A search and proposes value estimation methods in this setting; To improve exploration, 23 and 37 adopt MCTS, which better balances exploitation and exploration compared to A These crarch-based approaches, however, require extensive computation at inference time, often necessitating hundreds of model calls per molecule. Recent efforts such as [9] and [1O] address this limitation by fine-tuning the single-step model using successful trajectories collected during the planning phase, reducing search iterations and improving synthesis success rates. Yet, these methods still ultimately rely on search during inference, merely reducing rather than eliminating this computational burden.
Self-imitation Learning. Self-imitation learning improves policy performance by encouraging the agent to replicate its own high-return past behaviours [6. A central aspect of selff is the use of support constraints - the learned policy is regularised to remain close to the data-generating policy, which stabilises training and mitigates distributional shift [66] (2]] This principle has been effectively applied in offline and offline-to-oon to-online reinforcement learning [14] 24 27 where self- imitation facilitates safe policy improvement without extensive exploration. In our work, apply self-imitation to retrosynthesis planning by initialising the single-step model to generate reactions from the dataset and refining it through its past successful decisions, ensuring that its proposed reactions remain chemically plausible throughout training while progressively favouring those that lead to commercially available building blocks.
3 Retrosynthesis via Worst-path Optimisation in Tree MDPs
Prior work in multi-step retrosynthesis typically minimises the total cost of a synthetic route by optimising cumulative rewards across all root-to-leaf paths in the synthetic tree. In contrast, our worst-path objective targets the weakest path, ensuring viability by requiring all paths to terminate purchasable compounds. To efficiently optimise this criterion, we propose a weighted self-imitation algorithm that leverages successful trajectories while prioritising improvement on failure-prone paths.
3.1 Tree-structured MDPs
We formalise the retrosynthesis problem as a tree-structured Markov Decision Process (tree MDP), denoted by (8, A, T, Tr, Snt). Each state S represents molecule, and each action a E A corresponds to a chemical reaction. Unlike standard MDPs, where each transition leads to a single successor state, a chemical reaction may decompose a molecule into multiple reactants. To capture this branching structure, we define the power set of all possible molecules as 2 and introduce a branching transition function T S x A + 25, which maps product molecule and a reaction a to a set of reactants. These reactants become the children of in the synthetic tree. The reward function r: S + R assigns a numerical value to each molecule, and Sp. C denotes the set of commercially available building blocks. A synthetic tree is considered successful only if all of its leaf nodes belong to S.s. A policy π S A - [0, 1] defines a probability distribution over feasible reactions for a molecule s, thereby determining how the synthetic tree expands. Throughout this paper, we denote a complete synthetic tree by т, and let P(r) represent the set of all root-to-leaf paths within T. The tree MDP formulation is illustrated in Figure
3

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E
B
B
Successful Synthetic Tree
Failed Synthetic Tree
Building Blocks
Non-building Blocks
Figure 3: Examples illustrating the tree MDP formulation. Each non-leaf node represents molecule that decomposed into one or more reactants. Left tree: A successful synthetic route for target molecule A. It contains root-to-leaf paths: P(r) {ABD, ABEF, ABEGH, AC). Since all leaf nodes are building blocks, each path receives value y" where T the path length. The tree's overall value is p(r) { 3 y1, 7) 71. determined by the longest path. Right free: failed synthesis attempt for molecule A. One of its paths, ABEG, terminates at G, which is not building block. This gives path ABEG value of O, making the tree's overall value pin (72 7",, 7 = 0, illustrating why single failing path invalidates the entire route.
3.2 Worst-path Objective
The width of synthetic trees is naturally bounded, most molecules can be synthesised from only few reactants, As shown in Figure 2 98.6% of reactions in the USPTO- 50k dataset involve three or fewer reactants. This suggests that the quality of synthetic route is primarily determined by its depth rather than branching factor. Accordingly, evaluate each synthetic tree by the length of its longest root-to-leaf path. Figure iillustrate this concept: the left tree successfully synthesises molecule A, with its value determined by the longest path, whereas the right tree fails due to unsynthesisable intermediate G, yielding a return O.
76.6%5
Parcentage (16)
16.0%
6.0%
Reactants
Formally, define the reward function as
Figure 2: Distribution of reactant counts in the USPTO-50k dataset.
1. if E Spe. 0o otherwise.
r(s)
(1)
This assigns a reward of to commercially available building blocks and 0 otherwise. Since the reward is non-zero only at terminal states (i.e., leaf nodes), the return of any root-to-leaf path = (so, ao, .1,...,877 ST) is
T
rr(s)) = =
(2)
1=0
where y (0, is the discount factor. Hence, successful path with r(sr) receives value yT, penalising deeper decompositions, while dead-end paths with r(8r) = 0 yield zero return. We then define the worst-path objective
J(TT) ETAT pEP(F)
min
Σ >rr(s=)) t=
This objective captures the intuition that synthetic route only as viable as its weakest link: if any path leads to a dead-end, the entire tree's value becomes zero. Maximising the worst-path return therefore encourages the policy to ensure that every branch terminates building blocks through the shortest possible routes.

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3.3 Advantage Estimation
To optimise the worst-path objective, the agent interacts with the tree-structured MDP and learns to reproduce its past advantageous decisions. In this section, we formally define the advantage to quantify how beneficial reaction relative the policy's average behaviour. We then propose to estimate this advantage using value function learned from interaction experiences. Finally, we analyse theoretical properties and establish the existence of an optimal policy that maximises the worst-path objective.
Given molecule as the root node, a Q-function estimates the expected worst-path return when first expanding with reaction and subsequently following policy π.
(=) BTTT
min PEP(T)
7'r(s:)
8,
t=0
Here, So and denote the root molecule and the initial reaction, respectively. Similarly, the value function V (s) represents the expected worst-path return when all subsequent reactions follow policy π.
(T(() By
min PEP(T)
2'r(s)) 80
With these definitions in place, express the advantage function, which quantifies the relative benefit of applying reaction a to molecule s compared to following the policy:
A² a) Q" (s) (*)).
(6)
positive advantage indicates that reaction a leads to better outcomes than the policy's average behaviour. Proposition establishes relationship between Q* a) and V* (s):
Proposition 1. The Q-function Q (s, a) equals its immediate reward plus the discounted value of its
next states:
(" a) = r(s) + 71 ((1-((ss)) min V*(s). (ET(aa
The proof is provided in Appendix B.I.
Thus, we can estimate the advantage as:
AT (s a) r(s) + 7(1 r(s)) min
VT(8) (s)...
8
s'E((s,)
Next, derive recursive form of the value function, which forms the foundation for learning parameterised value function.
Proposition The value function (*)s) satisfies the recursion:
(*((s= = ( r(s))
Σ
T(a| 8) min (T((').
d'ET(a.a)
acA
The proof is provided in Appendix
B.2
Beyond the value functions for given policy, can define the optimal worst-path value function, V*, which represents the maximum possible worst-path return achievable from any state.
Proposition 3. This optimal value function V* uniquely satisfies the Bellman optimality equation for the worst-path objective:
V2(66)
(*())= = r(s) + x(1 - r(s)) max
min aEd (''E)().))
(10)
The proof is provided in Appendix B.3
In the proof, we show that the corresponding Bellman optimality operator a contraction mapping, which guarantees the existence and uniqueness of V* and the convergence of value iteration to V*. Furthermore, there exists at least one deterministic stationary policy म" that greedy with respect to V and is therefore optimal policy.
5

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3.4 Self-imitation Learning for Retrosynthesis
key challenge in retrosynthesis ensuring that the learned policy proposes chemically valid reactions at each step. To address this, we constrain policy learning within the support of a pre- trained single-step model, which can empirically capture high-fidelity chemical transformations. Specifically, we leverage pre-trained single-stes step model x trained to reflect expert or empirical reaction knowledge from the dataset [38] and define the constrained policy set:
II π
TT s) 0 whenever T (a s) = =0}
(11)
By restricting क to actions that T² assigns non-zero probability, we eliminate the risk of generating unrealistic reactions while allowing flexibility to re-weight feasible actions based on their effectiveness for multi-step planning.
Our objective is to find a policy π II that maximises the worst-path objective. We achieve this through iterative procedure: in each iteration imitating advantageous state-action pairs (s, a) experienced under policy n'. The advantage A (s.a) 1 quantifies this, and the learning objective for n' is formulated as:
aim to find an improved policy x by
(12) where 3 >> O is the advantage coefficient controlling the strength of advantage weighting, and d is the state distribution induced by policy n² [29]. this case, reactions with higher advantages receive higher weights, guiding the policy toward better-than--verage reactions.
J( Fandd,1())
exp (3.A" (s, u)) log ( [s)
Since each new policy n'+1 is derived by re-weighting T', and the initial policy x² restricts the support, the entire policy sequence {T') J220 remains within the feasible set II [12. This ensures that all proposed reactions throughout training remain chemically valid.
Proposition 4. Let g'+1 be the policy obtained by optimising the objective in 12 Then, the updated policy guaranteed to perform at least as well the previous policy for all states:
V
(s) > y" (s).
(13)
The proof is provided in Appendix
B.4
Proposition 4 guarantees monotonic improvement under the exact policy update:
r++1(a (a s) x n' (a exp a)),
(14)
which re-weights the previous policy by the exponentiated advantages, thereby increasing the proba- bility of actions that yield higher returns than the current expectation.
For policy optimisation, we use the current policy X' to interact with the retrosynthesis environment and collect data to learn the next iteration's 's policy xf+1_ Through iterative weighted imitation, the policy increasingly favours high-quality reactions that lead to successful synthetic routes with shorter paths, while maintaining chemical plausibility by respecting the support of the pre-trained model.
4 The InterRetro Algorithm
We now present our algorithm InterRetro for retrosynthesis planning. Section 4.1 introduces how the single-step model interacts with the tree MDP and collects trajectories. Section 4.2 describes how to learn a value function on the worst -att objective and how to fine-tune the policy to reproduce advantageous decisions. Finally, Section 4.3 provides more implementation details for reproducibility.
4.1 Environment Interactions
The EXPLORE procedure in Algorithm shows how InterRetro constructs a synthetic tree by interacting with the tree MDP. Starting from target molecule m. the single-step model proposes reaction to decompose the molecule into a set of reactants S,. These reactants will be attached to the parent node m in the synthetic tree. Among them, the non-building blocks are then placed in collection (e.g., queue) for further expansion. Each subsequent round pops a molecule from the collection to continue the decomposition process. The interaction terminates when the collection becomes empty (meaning all leaf nodes are building blocks) or when predefined maximum number of steps is reached.

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Algorithm Interactive retrosynthesis planning (InterRetro).
Input: pre-trained one-step policy тe, value function Vg, training set D, replay buffer B. def EXPLOOE((To, m))
def INTERRETRO m):
1:
for i :::=...... I....,I do
2:
while D is not empty do
1: tree + Tree(root = m)
3:
m <- D.pop()
2: q {m]
4:
tree (TP
3: step +
5:
brs 4
4: while q a and step max_steps do
for each subtree T tree do
5: 8 + q-pop()
7:
if r is successful then
6: o, + iT.. To..ett TT..get_reccaant((()
brs < brs ALLBRANCHES(T) end if end for B.append(brs)
7: tree add_ brd_branch
9:
8:
10:
9:
# Add non building blocks
11:
q qu E E, & )
12:
branches < B.sample() Vg.updatee(rranches
11:
13:
Eq 15
step + step +
14:
Ty. ug.update(Vor branches)
Eq. 16
13: end while
15:
end while
14: return tree
16:
end for
4.2 Value Function and Policy Learning
The EXPLORE procedure constructs synthetic trees by interacting with the tree MDP. We extract all branches (s, a, S,) within successful subtrees and store them in a first-in in first-out replay buffer B for value function and policy learning (see INTERRETRO Algorithm
To learn the value function network Ve with parameter é, we minimise the mean squared error between the predicted value (s)s) and its Bellman target derived from Proposition
( ).....S))-B (VV(()) (r(s) + 7(1 r(s)) min Va- (s))))²,, s'Es,
(15)
where Vø- is a target network updated slowly for stability.
The policy network Th with pre-trained parameter is updated using the weighted imitation learning objective from Eq. 12 implemented as the following loss function:
((0) [0))==E(ssee. -B(,,,,S.)
(BAA(s, (s, a)) (a s)],
(16)
where 3 > 0 the advantage coefficient which controls the imitation strength on the past successful experience, and exPelip(-) (-) is the exponential function with a clipped output range (0, C*| for numerical stability. The advantage is estimated by the value function network, according to Eq.
As(s, a) = r(s) + 7(1 r(s)) min
Vo(s') - Vo(s).
(17)
e'ET(s.a)
This joint optimisation of the value and policy networks enables InterRetro to fine-tune the single-step model, increasing the probability of reproducing high-advantage past decisions as indicated by the value function.
4.3 Implementation Details
We choose Graph2Edits as the single-step model due to its strong performance and flexibility in single-step retrosynthesis prediction (38). Graph2Edits represents molecule through graph and predicts a sequence of graph edits to transform to reactants. These graph edits can delete bonds, modify bond types, alter atoms, attach leaving groups (see Appendix C for more details). The single-step model and our proposed value function encode the molecule using a message passing neural network 33 and forward the latent code into linear layers with ReLU as the activation function. For training, we run 6 parallel exploration processes and collect 36 synthetic trees per iteration. The networks are updated 5 times per iteration using data from a compact replay buffer of maximal 20, 000 branches, chosen to reduce CPU memory usage and maintain close alignment between the data-collecting policy x' and the updated policy æ +++1 Our models are trained on single NVIDIA RTX A5000 GPU and, without pre-tra traininin the single-step model, require approximately 48 hours fully converge. The code has been open- sourced
GitHub repository: https://itttub.cmm/aaaaaauuuuau

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Table 1: Performance evaluation on three benchmarks. The evaluation metries include the success rate under different test molecules with different budgets of model calls, which are direct generation (DG), 100, 200 and 500 model calls. The DG columns are single-step model's results without search.
Retro's -190
ChEMBL-1000
GDB17-1000
Single-step
Search
DG
100
200
500
DG
100
200
500
DG
100
200
500
Template
MCTS
20.00
43.68
17.37
62.63
32.00
45.60
68. 80
71.90
3.00
3.20
3.70
4.50
Template
Retro*
20.00
36.42
58.42
75.26
32.00
00. 10
72.00
74.70
3.00
5.40
6.00
7.50
LocalRetro
MCTS
22.10
44.21
57.36
62.10
47.30
62.77
69.10
75.00
4.60
14.00
16.70
20.30
LocalRetro
Retro
22.10
58.94
63
73.68
47.30
74.80
80. 10)
82.40
4.60
18.90
22.20
28.80
MEGAN
Retro
8.42
60.52
62. 10
73.15
38. 00
71.70
40
79.00
6.20
37.60
15.70 15.70
37 20
Graph2Fdits
Retro
16.84
41.05
50.00
56.31
47.10
08.70 70
78 80
80.70
5.90
18:20
24.00
32.20
Self-improwe
Retro
67.37
83
94.74
81.10
PDVN
Retro
93.68
97.37
98.95
83.50
26.90
RetroCaptioner
Retro*
T2.
60
78.70
56,20
DreamRetroer
32.10
78.94
88 -12
90.52
31.10
78. 10
81. 770
83. 10
4.20
27.36
28.97
33.20
InterRetro InterRetro
MCTS
95.78
89.47
98.
100.00
78. .0
89.30
97.50
89.00
80.80
99.50
Retro
95.78
96.31
100.00
100.00
93.11
91.40
96.20
96.20
89.00
83.80
96.50
97.20
5 Experimental Results
In this section, we aim to answer the following questions: (1) What are the ad advantages of our proposed method compared the SOTA algorithms? (2) How does each component of the method contribute to the performance? Additionally, we examine the real-world feasibility of the proposed synthetic routes and present illustrative examples in Appendix
5.1 Benchmark Results
The proposed method is trained by creating synthetic routes for nearly 300k molecules in the USPTO- 50k dataset with commercially available building blocks from the eMolecules datase We evaluate performance on three benchmarks of increasing difficulty: Retro []]. ChEMBL-1000 [10 35),
and GDB17-1000 [O where the suffix indicates the dataset size. We compare against established retrosynthesis planning methods: MCTS, Retro M. Self-improve (9) PDVN [T]. and GraphRetro 31. Additionally, we include two single-step step methods, MEGAN 19 and Graph2Edits (38). combined with Retro* as the search algorithm, and two recently proposed methods, DreamRetroer [36] and RetroCaptioner [TT). Baseline results are produced from their official implementations or the Syntheeeus project [13]
Success Rate. Success rate measures the percentage of target molecules that can be successfully decomposed into building blocks. In T we firstly compare methods under different model-call budgets: 100, 200, and 500. Our proposed InterRetro significantly outperforms SOTA algorithms across all three test sets. With 500 model calls, InterRetro achieves 100%. 98.2%, and 99.5% success rates on 1 by InterRetro on Retro* 190 using 500 model calls are provided in the supplementary materials.
-0000, and GDB17-1006 respectively. All synthetic routes generated
Most notably, InterRetro maintains exceptional performance the challenging GDB17-1000 bench- mark, where prior approaches such as PDVN and Self-improve struggle (26.9% and 15.0% respec- tively). This dramatic performance gap suggests that our worst-path objective effectively handles complex molecules that require precise reaction planning.
Furthermore, InterRetro can directly generate high-quality synthetic routes without any search al- gorithm, as shown in the Direct Generation (DG) columns. Our search-free performance (95.78%, 93.10% and 89.00% across the three benchmarks) substantially exceeds even the search-based performance of competing methods. This demonstrates that our self-imitation learning approach suc- cessfully transfers planning capabilities to inference time, effectively eliminating the computational bottleneck of real-time search.
Route Length. Route length is the number of reactions needed to synthesise target molecule. Route length could be a concern with our worst-path objective since does not consider the width of the tree and the total number of reactions required. In Table we compare the route length with other baselines on the 138 target molecules that all methods can resolve. Our method outperforms the SOTA by 4.85%.
Meoeeculss: http:://oowllaaasscmoeellllecccs
8

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Success Baza
Suceer
0.5
1.0
Gerwation
20000
10.00 15666
29032
Cassoet Usage
Paal Deptr
Thiiiing Steps
(a)
(b)
(c)
Figure 4: Experimental figures. (a) Performance under different training data usage and computation budgets. (b) Statistics on estimated depth of synthetic trees. (c) Ablations on the advantage coefficient.
Sample Efficiency, Sample efficiency refers to the amount of training data required learn an effective policy. We evaluate the policy using subsets of the training data at 1%, 2%, 5%, 10%. and 100%, corresponding to approximately 3k. 6k, 15k. 30k, and 300k molecules. As shown in Fig- ure 4a InterRetro achieves near SOTA performance (92%) with just 10% of the training set when directly generating synthetic trees, and reaches SOTA performance when com- bined with 200 Retro* search iterations. Notably, when trained on the full dataset, InterRetro outperforms most existing methods in direct generation, achieving suc- cess rate of 95.78%. Similar trends are observed on the ChEMBL-1000 and GDB17-1000 benchmarks.
Table 2: The average length of the routes on the Retro*-190 test set.
Algorithm
Average Length
Retro*
5.83
RetroGraph
5.63
PDVN
4.83
MEGAN
4.12
Graph2Edits
4.38
InterRetro
3.92
Ablation Studies
Value Estimation. We proposed value function estimate the worst-pant return. The worst-path return indicates the estimated depth of the synthetic tree: depth(s) = log Va(e) Figure 4b we investigate the distribution of the estimated depth, corresponding to the real depth by direct generation. We found that the value function reflect the difficulties to synthesise the molecules, while it shows better capability on the molecules that require a less deep synthetic route.
log
Advantage Coefficient. The hyperparameter 3 controls the imitation strength on the past successful experience. A large means focused imitation on the action with a high advantage. Figure shows the learning curve on the Retro* 190 test set with 3 E {0, 1, 5, 10, 20). The figure shows that the DG performance increases from 16% to around 80% by uniformly imitating successful past experiences. With the advantage weighting, the performance soars success rate of more than 90% when 3 increases 2, and more than 95% with 10.
6 Conclusions and Discussion
We introduced InterRetro, a novel approach that frames retrosynthesis planning as worst-path optimi- sation in tree-structured MDPs. Our weighted self-imitation algorithm enables single-step models to generate high-quality synthetic routes without search at inference time, achieving SOTA performance in success rate, route length, and sample efficiency.
Further investigation can proceed in three main directions. First, the worst-path objective could be extended to incorporate additional real-world constraints, such as reaction conditions and the cost of building blocks. Second, and perhaps most importantly, our method and other contemporary work assume that all proposed reactions are feasible in real-world settings - an assumption that does not always hold. Although we mitigate this by imitating only actions supported by pre-trained models, computational approaches cannot guarantee practical feasibility without experimental validation. Future work can therefore focus integrating reaction feasibility checks into InterRetro. Furthermore,

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the "weakest-link principle underlying our method is broadly applicable beyond chemistry, extending sequential decision problems where overall success depends on the least reliable component for example, in robust project planning where delays in any critical task impact the entire timeline, or in multi-agent systems where team performance is constrained by the least capable agent.
Broader Impacts. The retrosynthesis community has recently open-sourced many high-quality models capable of suggesting synthetic routes for a vast number of molecules including both beneficial drugs and potentially harmful compounds. To promote safe and ethical research, we propose that access such models or their source code be governed by a Responsible Use License, under which researchers acknowledge the responsible and lawful application of the technology.
Acknowledgments
We acknowledge support from UKRI Turing AI Acceleration Fellowship (EPSRC EP/V024868/1).
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A Case Studies
ome
Step 3
HO
NH
Step 1
Step
8
9
Step
Product
10
CH2
Step
Step
CH3
HN
12
HgC
Product
15
16
18
20
21
19
Step
Step 2
Step 3
Product C
17
Figure 5: Predicted synthetic routes three randomly selected molecules in Retro*-190. The yellow circles highlight the reaction centres.
In Figure we randomly select three targets from the Retro -19 benchmark and illustrate their complete synthetic trees as directly generated by InterRetro in a single forward pass. (i) For the sulfonylated chiral cyclohexanol derivative, InterRetro proposes four-step route involving: sulfonylation of chiral alcohol to introduce the sulfonate ester, electrophilic bromination of diazine core, a C(sp")C(sp²) cross-coupling to build the fused triazolopyrimidine system, and an N-arylation to complete the final structure. (ii) For the alkyne-substituted diazine, InterRetro constructs the target via two steps: first an acylation of the brominated azine to give the N-formyl intermediate, followed by an SN2 N- propargylation
(iii) For the complex triazolopyridine fused drug-like scaffold, InterRetro suggests a three-step route: a benzylic etherification that assembles the protected piperidine core, C(sp') cross-coupling append the triazolopyridine fragment, and an N-alkylation that installs the silyl-protected side chain. Across all three cases, InterRetro applies feasible chemical transitions and terminates in commercially available building blocks. A full list the synthetic routes on Retro 190 is attached in the supplementary materials.
B Proofs and Further Theory
B.1 Proof of Proposition
Proof. Recall that
T
(s, = Erer min pPP((T)
Σ (s))
=
1=0
Since the transition function T is deterministic taking action in 8 immediately leads to the set of building block (in which case no further
next states T(s, a). Observe that r(s) is if and only transitions occur), and 0 otherwise. We prove the desired equality by simple case distinction:
13

---

1. Case r(s) 1 (i.e., s is a building block). In this case, once we reach the path terminates and collects reward 1. Hence
Q" r(s) r()) +7 (1 - r(s))
V=(s') (since 1 - --r(s) = 0). ('ET(s,0)
min
2. Case r(s) 0 (i.e., is not a building block). Any path extending from (s,a) a) must proceed into one of the next states E (T...a) a). Because we are taking "worst-path" (minimum-returm) perspective, the worst-case continuation value from under action is
min V=(8') <TT(s,)
and each step is discounted by y. Therefore,
(s. = r(s) + + min
(*(s') r(s) + y(1 - r(s)) (1-r(ss) (TE(()))
V"(s'). s' (s.a)
min
Combining both cases completes the proof:
(= r(() 7 -(())) min
(T((s).
(ET(aa
B.2
Proof of Proposition] 2
Proof. Recall that
T 7 rst)
(T) = Eyan
min (p
=
t=
When are in state the next action a is sampled from the policy x(. 8). Because the environment
deterministic, taking (s, a) leads to the set of next states T T(s,a) By the definition the worst-path criterion (the inner minimum), have
Q (s. = r(s) + (1
min ('E((a.a)
VT((',,
and
(*()) = Σ a(a s) (s,
acA
Substitute (s(ssa a) into the sum:
(T(() Σ T(a s)(r()) (1-r(s))
V=((')]
min
s'E((s,a)
Since EoEA T(a| 8)
factor out r(s) to obtain
(*()) r(s) Σ T(als) + - r())) Σ x(als)
min V*((s).
(T.())
A
aEA
Hence,
(*()) = r(8) + 7 71 rls)()) Σ x(als)
min
V*(s'),
<TT(s,,)
a
completing the proof.
14

---

B.3 Proof of Proposition 3
We consider the space V of all bounded real-valued functions V : S R defined over the state space S. For any function V E V. its -nom (or max-norm) is given by || = supses (V()). The space (V. || (loe) complete metric space (a Banach space).
The Bellman optimality operator B" V + V for the worst-path objective, with discount factor y (0, is defined for any value function V E V and any state E S as:
(B*V)(s - r(s) + + r())) max aed
min (TET())))
v(()
(18)
Note that if 8 E Sab. then r(s) 1, so r(s) O, and (B) r(s). If s & Saa. then r(s) O,
r(s) 1. and (B)) max min function V* is the unique fixed point of this operator, i.c., V = BVV.
V(5)]. The optimal value
Proposition 5. The Bellman optimality operator B' defined in Eq. 18 a y-contraction mapping with respect to the Los That is, for any V1, 1/2 E V:
1B B V2 S <>lVV V2
Proof. Let V1, 1/2 E be two arbitrary bounded value functions. We consider any state E S.
Case 1: E Sbs- In this scenario, (B)) r(s) and (B²V))(s) r(s). as the second term in Eq. 18) (involving the maximisation) vanishes because r(s) = 0. Thus, (B))
(B* V2) () = 0. The contraction inequality 0 S V/ V2 Voll therefore holds trivially.
Case 2: s Saa. In this case, r(s) 0, so r(s) 1. Then,
(B²)))(s)) 7 max aEA
min (EET(a.))
Vi(s')
(B*V2)(8) =7 max aEA
min ('ET(N,a)
V2(s))
Therefore,
((B)))((s) (B*)) =7
max aEA
vi(o)) - max (T,(a)
min
min (TE((a.))
V2((²)
aed
Let fv(a) min V(5). The expression is 7/ max, fV; (a) - max. Svz (a)). Using the property that for any functions 91 92. maxx 91 (x) - max. (2(2)) S sup, (z) ((()))a0 (2(()), have:
max (a) - max (a) S sup lfv, (a) - ffs REAA
aeA
aEA
Vi((') acA 8 ('ET(a.
sup
min
min s'ET(a.a)
V2(5')
Using the property that for any functions f1, h2 miny h1(y) min, h2(y)l S sup, (1)(2)) - (v)l:
min Vi(8')= - min (TET() s'ET(a,a)
V2(8)) < sup s'E((s,a)
-V1(') - V3(o'))
Combining these,
(B*V))(s)) - (B*V)(())) S sup aEA
sup
Vi((') V2('')
('ET(s,a)
S y sup
[i(")) - Va(s")) (as T(s, a) S S and we take sup over a)
8" EEES
V Vz lo
This inequality holds for any s & Sob-
Combining Case and Case 2, for all & E S:
(BB)))(ss (B)) s <>IVV V2lloo
15

---

Taking the supremum over all E S on the left side:
18 B ll sup (B))))(s) (B"V) (B)) - >l[V V2lloo
sES
Since 0 < <イ< < 1, B* is n-contraction mapping in (V, || l The space V of bounded real-valued functions S, equipped with the -norm, is complete metric space Banach space). Therefore, by the Banach fixed-poing point theorem, B* has unique fixed point V E V such that V = BVV'. Furthermore, for any initial bounded value function Vo E V. the sequence VA+1 B* (i.e.. value iteration) converges to V* The existence of unique V implies the existence of at least one stationary deterministic optimal policy x such that for 8 $ Sus:
π (s) arg max min (s,)))
('(())
B.4 Proof of Proposition
Proof. We begin by recalling standard identity from policy improvement theory, which relates the value difference between two policies xf+1 and x' to the expected advantage under s++
Vx (8) V -
A (s, (s.a)}
(s. where A as - V= (s) is the advantage function under the behaviour policy T'.
The update rule defined by the weighted imitation objective yields policy m²+1 that is propor- tional to the exponentiated advantage:
n" 7a s) a'(a 8) exp(3A a))
Z(s)
where Z(s) = Ew (a' s) exp(3A (s, a')) is normalising constant. This update ensures that r+ remains in the same support set as .'. Substituting into the value difference identity, we get:
E. aaaa 5) exp(3A (s, A (s, a) Σ. (' 8) exp(3A*(s,,))
v= (s) - V (5((s) = Σ n it (a (8) A ( (s,
This expression is a weighted average of the advantages A (s, a), where the weights are strictly positive and increasing the advantage. Therefore, unless all advantages are exactly zero, the expectation is strictly non-negative:
(8) 2 V= (8), E.
C Graph2Edits as Single-step Model
Graph2Edits 38 considers one-step retrosynthesis as a graph-editing game. Starting from the product molecule, the model autoregressively predicts a short sequence of primitive edits - Delete Bond, Change Bond, Change Atom, Attach Leaving Group, and finally a Terminate token. At each step the current intermediate graph is embedded by directed message-passing neural network (D-MPNN), whose atom- and bond-level embeddings are fed three linear heads that score every possible bond edit, atom edit and the termination symbol. The highest-scoring edit is applied to yield the next intermediate, and the process repeats until Terminate is chosen, at which point the intermediates have been fully converted to the predicted reactants. This edit vocabulary, mined from training data, covers 99.9% of USPTO-500 reactions and encodes stereochemistry as well as functional leaving groups.
Trained with teacher forcing, the system already achieves a 55% top-1 exact-match accuracy on USPTO-50k and maintains high validity and diversity even for long or stereochemically rich reactions; these strengths make it an ideal single-step model inside our multi-step InterRetro framework.
16

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NeurIPS Paper Checklist
1. Claims
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Question:
Answer: [Yes]
The abstract and introduction state four contributions - worst-path optimi- sation framework, self-imitation algorithm with monotonic improvement guarantees, search-free retrosynthesis approach, and SOTA empirical results and every one of these is delivered and substantiated in the main text and experiments.
Justification:
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results be expected to generalize other settings.
It is fine include aspirational goals as motivation long is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: Section Dexpliiity notes that InterRetro and contemporary works assume the suggested reactions are experimentally feasible. We recognise this may be violated, and recommend adding feasibility filters in future work.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
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The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low or images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed not penalize honesty concerning limitations.
3. Theory Assumptions and Proofs
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Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
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The paper gives dataset names/sizes, model-call budgets, hyper-parameters, and code repository link that contains the full training and evaluation code.
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5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, described in supplemental material?
Answer: [Yes]
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The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
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For each experiment, does the paper provide sufficient information the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
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Reviews:
[{"review_id": "XMrbrxilF9", "reviewer": "Reviewer_paFC", "summary": "Here the authors introduce a new method (InterRetro) for multi-step retrosynthesis planning by introducing a worst-path objective that aims to improve the most infeasible path in a synthetic route. Overall I think the idea is strong and novel, and the paper is nice amd well-written and could be well-suited to this venue, but IMO the main limitation holding it back right now is that it is missing comparison to actual SOTA baselines for this task (e.g., AiZynthFinder or ASKCOS) and instead comparing against not the leading models for multi-step retrosynthesis prediction (then claiming SOTA). This would need to be improved for me to recommend the paper for acceptance.", "questions": "- How do the depths of the predicted synthesis trees compare to the real synthesis trees in the training data?\n- The authors mention that their models are better at generating shorter routes, which is good, but how good is the model at also predicting known routes? This is not something that has been evaluated in the paper and I think it is important (it is also separate from the success rate).\n- How was data selected for the dataset usage experiments (e.g., 1%, 5%, etc.)? Was this done randomly, and does this make sense?\n- How can it be that sometimes the single-step model is better than the multi-step model (e.g., for limited model calls?)? I feel like this could be better explained because to me this is an unexpected result.", "limitations": "Comparison to SOTA is not well done (see comments above).", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- Code available (and lots of routes provided as supplementary material, a bit strange), which is a strong point, but the documentation on how to use the code, packages/environment, etc is lacking. This could be improved to improve the utitlity of the model\n- Seems comparing to neither AiZynth nor ASKCOS which IMO are the leading models for retrosynthesis prediction, especially when it comes to multi-step retrosynthesis planning.\n- I like the results illustrating the performance under different budgets (e.g., Figure 4a), it would be nice to also know how the number of calls compares to compute (e.g., if this was done on a single GPU, etc), and how this compares to other methods.\n- Limited details on the dataset used for training (and how evaluations were done), this should be improved. It is not sufficient to simply say USPTO-50k, since how the splits were created for training/validation/testing are also important. With this in mind, I would encourage the authors to look into the PaRoutes (https://doi.org/10.1039/D2DD00015F) dataset which has been nicely curated for this task.\n- Similarly, on the molecules selected for evaluation from the three different datasets (e.g., Retro*-190, ChEMBL-1000, GDB17-1000) not much is said, some justification needs to be provided here. For instance, it is not clear to me why GDB17 was selected at all, as these molecules to my knowledge are not particularly drug-like or representative of a chemical space in either drug discovery or materials science, so unclear why they are used to evaluate a synthesis tool (do not get me wrong, I think there is value in GDB17, just not for this task).", "was_revised": true, "final_justification": "I have increased my score by 1 pt following the authors' thorough and thoughtful response to my recommended revisions surrounding baselines.", "rebuttal": "Thank you for recognising the novelty of our idea and the quality of our presentation. We have carefully addressed your concerns point by point and revised the manuscript accordingly. We hope that our responses satisfactorily resolve the issues raised.\n\n**W1.** The routes provided in the supplementary materials are the synthetic routes generated by InterRetro on the Retro*-190 dataset, as stated in Section 5.1. We have improved the documentation and updated the anonymous repository, including a `requirements.txt` file and a step-by-step usage example. We believe that incorporating your suggestions enhances the reproducibility of our work.\n\n**W2.**  Thank you for raising the important concerns regarding baselines. AiZynth (from AstraZeneca) and ASKCOS (from MIT) are software platforms that allow users to deploy their own retrosynthesis algorithms via a user-friendly interface; they do not introduce new algorithms themselves. By default, both platforms implement a template-based search algorithm, which corresponds to the Template-MCTS baseline included in our original manuscript (Table 1). InterRetro significantly outperforms this baseline (20.00% vs. 95.78% success rate in Direct Generation, and 62.63% vs. 100.00% success rate when the model call budget is 500).\n\nTo further address the concern and strengthen our SOTA claim, we have run more experiments comparing InterRetro against two recent compelling algorithms: DreamRetroer (Zhang et al., 2025) and RetroCaptioner (Liu et al., 2024). As shown in the following table, InterRetro demonstrates superior performance, particularly in the search-free Direct Generation (DG) setting. We believe this new comparison reinforces InterRetro's state-of-the-art status. \n\n| Method           | Direct Generation | Model Call 100 | Model Call 200 | Model Call 500 |\n|------------------|-------------|----------|----------|----------|\n| DreamRetroer   | 32.10       | 91.57    | 94.73    | 95.26    |\n| RetroCaptioner   | 5.26        | 68.94    | 72.63    | 85.26    |\n| InterRetro       | 95.78       | 96.31    | 100.00   | 100.00   |\n\nDue to the limited rebuttal period, we have focused on the Retro*-190 benchmark. Results for ChEMBL-1000 and GDB17-1000 will be included in the final version of the paper.\n\n**W3.** A key advantage of our method is its computational efficiency at inference time. We benchmarked the wall-clock time on a single NVIDIA RTX A5000 GPU. Our results show that Direct Generation takes approximately 0.88 seconds per molecule, while a search with a 500-model-call budget requires 8.53 seconds. The total wall-clock time required to solve the 190 molecules in the Retro*-190 dataset is presented below, demonstrating that InterRetro solves target molecules much faster than recent algorithms.\n\n| Methods            | Direct Generation | Model Call 100 | Model Call 200 | Model Call 500 |\n|--------------------|-------------------|----------------|----------------|----------------|\n| DreamRetroer     | 67 min            | 95 min         | 104 min        | 116 min        |\n| RetroCaptioner     | 74 min            | 134 min        | 141 min        | 240 min        |\n| InterRetro  | 3 min             | 16 min         | 20 min         | 27 min         |\n\n**W4.** Our training and evaluation protocol follows current methods such as PDVN (Liu et al., 2023) and Retro* (Chen et al., 2020). The training set provides target molecules for the model to learn how to construct valid synthetic trees. The trained models are then evaluated on the test set based on their success rate in constructing valid trees and the length of those routes. The training set consists of the shortest routes synthesizable using USPTO-50k reactions and eMolecules building blocks. The test set is constructed by extending route extraction, ensuring no overlap with the training set, and applying additional filtering to make the test set more challenging. More details can be found in Section 5.1.2 of Chen et al. (2020).\n\nThank you for recommending the PaRoutes dataset. We have tested InterRetro on the set-n1 and set-n5. Due to the large size of each set (10,000 molecules), we evaluated only the first 200 molecules in each. As shown in the table below, InterRetro shows consistently strong performance, as it does on Retro*-190, ChEMBL-1000, and GDB17-1000. This supports the objectivity and robustness of our chosen benchmarks.\n\n| Route Set | Methods             | Direct Generation    | Model Call 100   | Model Call 200    | Model Call 500    |\n|-----------|----------------------|-------|-------|--------|--------|\n| set-n1    | InterRetro-Retro*    | 91.50  | 94.50  | 98.50  | 99.00  |\n| set-n5    | InterRetro-Retro*    | 94.00  | 97.00 | 100.00 | 100.00 |\n\n**W5.** We believe that testing on multiple molecule datasets is helpful to comprehensively evaluate each algorithm, thus we choose multiple distinct benchmarks - Retro*-190, ChEMBL-1000 and GDB17-1000. Retro*-190 dataset serves as the standard benchmark in the field, enabling direct comparison with prior work. ChEMBL-1000 from ChEMBL dataset (Zdrazil et al., 2023) is a manually curated collection of bioactive molecules exhibiting drug-like properties, making it an appropriate benchmark for retrosynthesis planning. Regarding GDB17-1000 (Ruddigkeit et al., 2012), it covers a size range common for typical drugs and lead compounds, featuring unusual ring systems and connectivity patterns that challenge generalisation capabilities. If a method performs well on both drug-like molecules (ChEMBL) and structurally diverse, synthetically challenging molecules (GDB17), it demonstrates strong generalisation beyond memorisation of common reaction patterns. Thus we believe that these three datasets are appropriate benchmarks for retrosynthesis planning. We have added this justification to Section 5.1 to clarify our evaluation strategy.\n\n**Q1**. Our analysis on the Retro*-190 benchmark shows that InterRetro tends to find more direct routes than those in the dataset. Specifically, the valid routes from our search-free Direct Generation (DG) model have an average depth of $4.54$, which is shorter than the dataset's average of $6.96$. As shown in the table below, this trend of finding valid routes continues when a search budget is applied. Unfortunately, we cannot perform a similar analysis for the ChEMBL-1000 and GDB17-1000 benchmarks as they do not provide ground-truth synthesis routes.\n| Methods                 | Average Depth |\n|-------------------------|----------------|\n| Dataset Routes          | 6.96           |\n| InterRetro (DG)         | 4.54           |\n| InterRetro (100 calls)  | 3.21           |\n| InterRetro (200 calls)  | 3.03           |\n\n**Q2.** While our primary objective is to discover valid synthetic routes, we agree that evaluating the model's ability to reproduce known routes is an important indicator of its feasibility. When planning with Retro* and maximal model call 500, 38.9\\% of the known routes appears in the search tree constructed by InterRetro, while 46.4\\% of the known routes appears in the search tree built by standard Graph2Edits. Although slightly lower than the standard Graph2Edits, our finetuned model is still effective at recovering the routes presented in the dataset. In our experiments, InterRetro can represent valid but genuinely different synthetic strategies, often shorter than the routes presented in the dataset (as shown in the supplementary materials). This suggests our model balances between learning from established chemistry and discovering potentially more efficient alternatives.\n\n**Q3.** For the dataset usage experiments shown in Figure 4a, subsets of molecules were selected via random sampling from the USPTO-50k training data. To ensure robust results for each sampling ratio (1\\%, 2\\%, 5\\%, and 10\\%), we created three independent, randomly sampled subsets and trained a separate model on each. The performance reported in the figure is the average across these three runs. This process ensures our results are objective and robust to sampling variations.\n\n**Q4.** We appreciate the reviewer pointing out this interesting result, which is indeed reasonable on closer inspection. The Direct Generation (DG) model is entirely focused: it makes a single, decisive prediction for the best reaction at each step. In contrast, a search algorithm like Retro* or MCTS expands a much wider tree by considering multiple (e.g., 10) possible reactions at each step. When the search budget (i.e., the number of model calls) is severely limited, the algorithm cannot explore these numerous branches deeply enough to reliably identify the optimal path, increasing the chance of selecting a suboptimal action compared to the focused DG approach.\n\n---\n\nWe would like to thank you again for raising important questions about our submission. After incorporating this valuable feedback, we believe that our current version becomes much stronger. Have we sufficiently addressed the main concerns? Please feel free to let us know if there are additional concerns or questions.\n\n**References**\n\nChen, Binghong, et al. Retro*: Learning retrosynthetic planning with neural guided A* search. ICML, 2020.\n\nLiu, Guoqing, et al. Retrosynthetic planning with dual value networks. ICML, 2023.\n\nLiu, Xiaoyi, et al. RetroCaptioner: Beyond attention in end-to-end retrosynthesis transformer via contrastively captioned learnable graph representation. Bioinformatics, 2024.\n\nRuddigkeit, Lars, et al. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. Journal of Chemical Information and Modeling, 2012.\n\nZdrazil, Barbara, et al. The ChEMBL database in 2023: A drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Research, 2023.\n\nZhang, Xuefeng, et al. A data-driven group retrosynthesis planning model inspired by neurosymbolic programming. Nature Communications, 2025."}, {"review_id": "U1mvd8S2A9", "reviewer": "Reviewer_SSSw", "summary": "This paper introduces InterRetro, a novel approach that frames multi-step retrosynthesis planning as a worst-path optimization in tree-structured MDP. Unlike traditional methods that optimize for average or cumulative rewards, InterRetro focuses on maximizing the value of the most challenging path, ensuring all branches terminate successfully.", "questions": "1. What is the insight for choosing Graph2Edits as the single-step model?\n2. Can you ablate your method by replacing the single-step model with the pre-trained MLP from retro*? This can help understand the relative importance of the design made in the paper.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: The proposed worst-path optimization is intuitive while achieving strong empirical results. Besides, The authors provide rigorous theoretical guarantees, including proofs for the existence of a unique optimal solution and assurances of monotonic policy improvement with their weighted self-imitation learning algorithm.\n\nWeaknesses: My main concern regarding this paper is which component contributes to the empirical performance improvement. While InterRetro proposes a novel objective and training methodology, it utilizes Graph2Edits as its underlying single-step model. Graph2Edits is a high-performing single-step retrosynthesis model itself, and its inherent strength might contribute significantly to the overall success. Without an ablation study specifically varying the single-step model while keeping the worst-path objective and self-imitation learning algorithm consistent, it is difficult to fully ascertain whether the improvements are primarily from the novel objective and learning paradigm or from the strong Graph2Edits base.", "was_revised": true, "final_justification": "In my initial review, I noted that the source of the performance improvement was unclear. The authors' rebuttal addressed this, clarifying that the gains are primarily attributed to the single-step model rather than the worst-case objective. As the authors have agreed to incorporate this important clarification into the final version, I will maintain my score.", "rebuttal": "Thank you for your detailed feedback and for the time you spent in reviewing our manuscript. We have addressed every point you raised and will incorporate the corresponding changes into the final version.\n\n**Weaknesses.** Thank you for raising this important concern. We agree that isolating the source of performance improvements is crucial for properly attributing the effectiveness of our approach. To address this, we provide the following ablation results on the Retro*-190 dataset. The first two rows compare the performance of the original pretrained Graph2Edits model (used with Retro*) and the finetuned version (InterRetro). The substantial improvement in performance after finetuning demonstrates that the self-imitation learning process and the worst-path objective meaningfully enhance the base model’s effectiveness.\n\nIn the next two rows, we evaluate a template-based model (Template - Retro*) and its finetuned variant using the same training framework. Again, we observe a significant performance gain after fine-tuning, even though the underlying single-step model is not as strong as Graph2Edits. This indicates that our learning framework improves multi-step planning across different single-step models, not just Graph2Edits.\n\nThese results suggest that the performance gains stem from the proposed training methodology rather than solely relying on the strength of Graph2Edits.\n\n| Method                        | Direct Generation | Model Call 100 | Model Call 200 | Model Call 500 |\n|-------------------------------|-------------------|----------------|----------------|----------------|\n| Graph2Edits - Retro*          | 16.84             | 41.05          | 50.00          | 56.31          |\n| Graph2Edits (finetuned) - Retro*           | 95.78             | 96.31          | 100.00         | 100.00         |\n| Template - Retro*             | 17.89             | 38.42          | 58.42          | 79.47          |\n| Template (finetuned) - Retro*| 32.10             | 46.31          | 65.26          | 85.26          |\n\n**Q1.** We chose Graph2Edits as our single‑step model for three practical reasons: \n\n* Better generalisation. Graph2Edits edits molecules at the bond‑ and atom‑level, so it can apply the same local transformation to many unseen but similar leaving groups. Template‑based methods, which store entire reaction patterns, often fail when an exact template match is missing. \n\n* Fast and lightweight. The Graph2Edits model requires less than 10 MB of memory, whereas a template-based method, due to the storage of template information, can consume up to 1 GB, which hinders its applicability in parallel computation. At inference time Graph2Edits model is far cheaper than large generative models (e.g., transformer–decoder architectures), which speeds up both training and route generation. \n\n* High‑quality support constraint. The Graph2Edits paper reports state-of-the-art single‑step accuracy and shows that its predicted edits follow well‑established reaction chemistry. By constraining our policy to this edit vocabulary, we preserve chemical plausibility while still allowing our advantage‑weighted updates to re‑rank the edits.\n\n**Q2.** Thanks for your suggestions. We have reported the results in our response to the Weakness above. We have revised the submission to reflect the ablation results. \n\n---\n\nWe would like to thank you again for raising important questions about our submission. Due to this year's rebuttal policy, we cannot upload a revised PDF to reflect these changes. However, we believe that after incorporating the valuable feedback our current version becomes much stronger. Have we sufficiently addressed the main concerns? Please feel free to let us know if there are additional concerns or questions."}, {"review_id": "mWUcISKhAy", "reviewer": "Reviewer_sMtG", "summary": "This paper formulates retrosynthesis as a worst-path optimization problem within tree-structured Markov Decision Processes, and proposes InterRetro, a method that interacts with the tree MDP, learns a value function for worst-path outcomes, and improves its policy through self-imitation. Experimental results show that InterRetro achieves state-of-the-art performance, solving 100% of the targets on the Retro*-190 benchmark, and also demonstrates strong results on two additional test sets.", "questions": "1. The paper mentions hundreds or thousands of model calls per molecule; however, previous retrosynthesis algorithms tested on the Retro*-190 dataset achieve a success rate of 99% with an average number of calls fewer than 50 [1] [2], and significantly reducing the number of model calls can also be achieved by caching the results of previous single-step retrosynthesis model calls to avoid redundant computations.. Please kindly verify this point again.\n[1] Yu,Y.etal.Grasp:Navigating retrosynthetic planning with goal-driven policy. In Advances in Neural Information Processing Systems (NeurIPS 2022)\n[2]  Xie,S.etal.Retrograph:Retrosynthetic planning with graph search.In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2120–2129 (ACM, 2022).\n\n2. $\\gamma^T$ is employed to penalize deeper decompositions; however, if the target molecule requires a relatively long synthesis pathway, could this negatively impact the search efficiency? Specifically, might the search become biased towards shorter paths and spend excessive effort exploring those regions?\n\n3. Graph2Edits serves as the single-step retrosynthesis model in your approach. Could you please explain how it is combined or integrated with the trained policy during the planning or search process?\n\n4. In table 1, success of Retro* with template-based single-step model is $75.26\\%$ when the number of model calls is limited to $500$, which is different with the result displayed in the paper of Retro*. Could you please provide an explanation?\n\n5. What is the average number of model calls for the algorithm? Could this information be included in a table for clearer comparison?\n\n6. This paper optimizes for the shortest path to generate feasible synthesis routes. However, retrosynthesis involves various optimization objectives, such as the cost of chemical reactions. For example, Retro* approximates cost optimization by using the negative log of the policy function. How does the algorithm proposed in this paper incorporate such diverse optimization objectives?\n\nIf the authors can address the question I raised above, I would be willing to increase my score.", "limitations": "The main text of the paper does not discuss limitations or potential negative societal impacts; these aspects are only addressed in the appendix.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper is well-written and presents a comprehensive and complete study.\n2. The idea of worst-path optimization is quite interesting. It offers a new perspective for addressing the retrosynthesis planning problem.\n3. The experimental results are compelling and demonstrate significant improvements compared to prior algorithms.\n\nWeaknesses:\n1. The clarity of some descriptions could be improved. For example, the deterministic transition function is a mapping from (state, action) to to a set of reactant molecules. Each molecule corresponds to a single state, so transitioning from one state to a set of states is not a well-defined formulation.\n\n2. The experimental part needs to be supplemented with more details. For specific points, please refer to the Questions part.\n\n3. The significance of the paper needs to be further elaborated. In retrosynthesis planning, real-time feedback is not typically required, and taking several minutes to find a feasible solution is generally acceptable. The quality of the solution is more critical. Therefore, the claim that time-consuming real-time search limits practical utility in large-scale molecular design scenarios warrants further scrutiny.", "was_revised": true, "final_justification": "After discussing with the authors, I have a better understanding of the training process and the motivation for the shortest path. However, I still have reservations about the significance of the problem itself. I have adjusted my score to 4, while remaining neutral on whether the paper should be accepted.", "rebuttal": "Thank you sincerely for your professional and constructive feedback. We are pleased to receive your comments, which have helped us improve the manuscript significantly. We have analysed and addressed each point below, and we hope our responses satisfactorily resolve your concerns.\n\n**W1.** In line 141, we define the transition function as a mapping from the state–action space to the power set of the state space, $\\mathcal{T}: \\mathcal{S} \\times \\mathcal{A} \\rightarrow 2^{\\mathcal{S}}$, in line with your suggestion. We have revised the presentation to improve clarity.\n\n**W2.** We have revised the experimental section accordingly; please see our response to the Questions below.\n\n**W3.** We agree that the quality of the synthetic route is essential in experimental settings. However, our emphasis on computational efficiency reflects the growing demand for scalable retrosynthesis planning, not a compromise on quality. In AI-guided drug discovery, retrosynthesis planning is increasingly deployed in batch settings to evaluate large numbers of candidate molecules generated by models such as LLMs. These pipelines often screen tens of thousands of compounds, where each must be filtered or ranked based on synthesizability. In such settings, latency per molecule quickly becomes a bottleneck. Furthermore, human-in-the-loop platforms (e.g., IBM RXN and ASKCOS) allow chemists to interactively sketch molecules and receive immediate feedback on the molecule's synthesizability. These real-time applications require short latency, enabling seamless user interaction and rapid design iteration. Such fast feedback is critical in practical workflows, where chemists often explore many variants of a lead compound in a single session. Therefore, in modern retrosynthesis applications, short inference times are not merely desirable but often necessary. When route quality is comparable, computational efficiency becomes the key to making retrosynthesis planning actionable at scale.\n\n**Q1.**  The heuristic search algorithms mentioned in the background paragraph require substantial model calls: Retro* typically exceeds 150, MCTS around 250, and Greedy DFS around 300. More details are provided in our response to Question 5. we agree that describing them as \"thousands\" is inaccurate; to clarify this, we have updated the text to: “These methods heavily rely on time-consuming real-time search, often requiring hundreds of model calls for each molecule...”\n\n**Q2.** Thank you for highlighting this concern. The $\\gamma^{T}$ term serves only to discourage unnecessarily deep decompositions: its effect is relative to the alternatives available for a given molecule. A short path that fails to reach purchasable building blocks receives a return of 0, whereas a longer but successful path still earns a positive return $\\gamma^{T}>0$; consequently, our policy will always favour the viable route, even if it is longer. Among successful routes, $\\gamma^{T}$ then biases the planner toward the shallower option, cutting out extra steps while keeping the route feasible. Moreover, because the worst‑path objective scores each synthesis tree by its weakest branch, any tree containing a dead‑end is automatically rejected, preventing the search from being “trapped” in short but invalid regions. Therefore, the depth penalty prunes gratuitous exploration while still selecting longer routes whenever they are actually required.\n\n**Q3.** In InterRetro, we initialise a pretrained Graph2Edits model ($\\pi_\\theta$ in Algorithm 1), and finetune it using self-imitation learning until convergence. During the search process, this finetuned Graph2Edits model is used as the one-step expansion model to generate reactants. We do not combine or integrate it with any other trained policy. The retrosynthesis search process, like Retro* (Chen et al., 2020) and MCTS (Hong et al., 2023), typically consists of three steps: selection, expansion, and update. Our finetuned Graph2Edits model is used exclusively in the expansion step, where it predicts a sequence of edits to decompose a molecule into its reactants. \n\n**Q4.** Thank you for pointing this out. We mistakenly reported the results for a model-call budget of 400 (Liu et al. 2023). We have now thoroughly reviewed all other reported results to ensure their accuracy.\n\n**Q5.** Thank you for your suggestion. Below, we compare our method with the suggested RetroGraph and other relevant algorithms:\n| Method              | Avg. Model Calls |\n|---------------------|----------------|\n| Greedy DFS          | 300.56           |\n| MCTS                |254.32           |\n| Retro*              | 209.86           |\n| RetroGraph          | 45.13            |\n| PDVN-Retro*         | 30.94            |\n| InterRetro-Retro*   | 30.83            |\n\nTo compute the average model calls, we set the maximum model-call budget to 500 and measured the average number of calls required by each algorithm to successfully construct a synthesis route for molecules in the Retro*-190 dataset. We find that InterRetro and PDVN both achieve state-of-the-art performance in terms of model-call efficiency. These results will be included in the revised version of the paper.\n\n**Q6.** We acknowledge the importance of a retrosynthesis planning algorithm to support various optimisation objectives. In this case, we could incorporate new quantities into the weighting function and encourage the policy to learn those low cost reactions. The cost of chemical reactions may come from the reaction conditions, the price of the reactants, etc. To the best of our knowledge, a comprehensive dataset capturing such information is not yet available. We would be happy to extend InterRetro in this direction once the necessary infrastructure and data become available.\n\n**Limitations.** In the revised version, we have incorporated the limitations and the potential negative societal impacts in Appendix to the last section. \n\n**Paper Formatting Concerns.** We have added the guidelines in the revised version.\n\n---\n\nWe would like to thank you again for raising important questions about our submission. Due to this year's rebuttal policy, we cannot upload a revised PDF to reflect these changes. However, we believe that after incorporating the valuable feedback our current version becomes much stronger. Have we sufficiently addressed the main concerns? Please feel free to let us know if there are additional concerns or questions.\n\n**References**\n\nChen, Binghong, et al. Retro*: Learning retrosynthetic planning with neural guided A* search. ICML, 2020.\n\nHong, Siqi, et al. Retrosynthetic planning with experience-guided Monte Carlo tree search. Communications Chemistry, 2023.\n\nLiu, Guoqing, et al. Retrosynthetic planning with dual value networks. ICML, 2023."}, {"review_id": "WAp7Q6blcW", "reviewer": "Reviewer_EwLN", "summary": "This paper introduces a new approach to retrosynthesis planning called InterRetro, which builds synthesis routes for molecules by focusing on the weakest part of the route. \n\nThe key idea is to ensure that all paths in the synthesis tree end in purchasable compounds, since one failed path makes the whole plan invalid. \n\nInterRetro treats the planning process as a worst-path optimisation problem in a tree-structured Markov Decision Process and improves its decision-making using self-imitation learning.", "questions": "Out of all the listed contributions, the third one—\"we apply this framework to retrosynthesis through InterRetro, a search-free approach to multi-step planning\"—is not entirely clear to me. I understand the other three contributions, but this one feels more like an application rather than a contribution in itself. I would appreciate an elaboration on what makes this application a distinct contribution.\n\nIn the Tree-structured MDPs section, could you please explain why the domain of the mapping is 2^S?\n\nWhat does 190 refer to in Retro*? I couldn't find any information about this in the citation link you provided—almost everywhere it's simply referred to as Retro*.\n\nAccording to the metrics, your results are quite impressive. Could you elaborate on why you think this is the case and what the consequences might be for the field? Should we expect a significant leap forward in retrosynthesis planning, or do these strong metrics not necessarily translate to major changes in industry applications?\n\n\nAfter addressing my questions and concerns, I would be willing to reconsider my evaluation. \n\nThank you for your submission and have a nice day!", "limitations": "Yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\nThe paper reframes retrosynthesis planning as worst-path optimisation in tree-structured MDPs, which is both novel and well-justified.\n\nIt derives Bellman equations for the worst-path value and proves the existence and uniqueness of the optimal value.\n\nStrong empirical results achieving state-of-the-art performance.\n\nThe code is available, and the provided example is reproducible.\n\nWeaknesses:\n\nThe quality of the figures does not match the quality of the paper. Some figures (e.g., Figures 2 and 4) show rasterization artifacts when the paper is enlarged to a comfortable reading size, and Figure 4 in particular is difficult to interpret when printed on A4 paper.\n\nThe design of Table 1 is confusing: in the text, you state that you compare established retrosynthesis planning methods and list them together, but in the table, they appear in separate columns. I still struggle to understand what is being presented in the table, even after reviewing it several times.", "was_revised": true, "final_justification": "Authors have addressed my concerns, thus I am raising my score", "rebuttal": "Thank you for your detailed feedback and for the time you spent in reviewing our manuscript. We have addressed every point you raised and will incorporate the corresponding changes into the final version.\n\n**W1.** Thank you for bringing this to our attention. We have regenerated Figure 2 and Figure 4 in PDF format to ensure its readability in different devices. For Figure 2, we enlarged the size of each sub-figure, increased the font size, and ensured that all sub-figures scale cleanly on A4 paper. Because we cannot upload revised paper during rebuttal, these improved figures will appear in the final version.\n\n**W2.** Table 1 shows the results for different combinations of single-step models and search algorithms across three benchmarks. Each row represents a specific combination (e.g., \"InterRetro - Retro*\" means InterRetro as the single-step policy with Retro* as the search method). The columns then show: (1) \"DG\" - direct generation without search, (2) \"100\", \"200\", \"500\" - results with different search budgets (maximum model calls). We will improve the caption and the table accordingly to make them easy to understand.\n\n**Q1.** Contribution 1 and contribution 2 formulate the problem into a tree-structured MDP with the worst-path objective, and propose self-imitation to optimise this objective. However, it still has a distance from the theory to a practical algorithm - we need to decide how the agent interacts with the tree MDP, how the agent stores past successful experience for self-imitation, etc. Therefore, we have contribution 3, which extends the theory, adds more implementation details, and solves the retrosynthesis problem in practice. The engineering decisions and implementation details that enable this capability constitute a significant contribution beyond the theoretical framework.\n\n**Q2.** The notation $2^{\\mathcal{S}}$ denotes the power set of $\\mathcal{S}$, which is the set of all possible subsets of $\\mathcal{S}$. We use this because in retrosynthesis, applying a reaction to a molecule produces a set of reactant molecules (typically 1-3 reactants), not just a single molecule. \n\nFor example, if molecule $s$ undergoes reaction $a$, it might produce two reactants {$s_1, s_2$}. Since {$s_1, s_2$} is a subset of all possible molecules $\\mathcal{S}$, we have {$s_1, s_2$} $\\in 2^{\\mathcal{S}}$. This is why our transition function is defined as $\\mathcal{T}: \\mathcal{S} \\times \\mathcal{A} \\rightarrow 2^{\\mathcal{S}}$ rather than $\\mathcal{T}: \\mathcal{S} \\times \\mathcal{A} \\rightarrow \\mathcal{S}$. We have added this clarification to Section 3.1 in the revised manuscript.\n\n**Q3.** The Retro*-190 in our paper refers to the dataset from Retro*, as described in Section 5.1 by Chen et al. (2020). $190$ is the number of target molecules included in the dataset. The dataset can be downloaded in Retro*'s GitHub page. We refer this dataset as Retro*-190 rather than simply Retro* to keep the suffix indicating the number of the test molecules in the dataset, being consistent with  ChEMBL-1000 and GDB17-1000.\n\n**Q4.** Our impressive results stem from two key innovations: (1) the worst-path objective that directly optimises for complete route validity, and (2) the self-imitation learning that effectively leverages successful experiences. For the research community, these results demonstrate that search-free retrosynthesis is not only possible but can outperform search-based methods. This could redirect research efforts toward end-to-end learning approaches. For practical applications, the search-free nature offers immediate benefits: real-time route generation for large molecular libraries, integration into interactive design tools, and reduced computational infrastructure requirements.\n\nHowever, we must acknowledge important limitations. Our approach, like all current methods, assumes predicted reactions are experimentally feasible — an assumption that doesn't always hold. Real-world deployment would require integration with reaction feasibility predictors and experimental validation workflows. Additionally, our method currently optimises for route depth rather than other practical considerations like cost and reaction conditions.\n\nWe see our work as a significant step toward practical automated synthesis planning, but achieving industry-ready systems will require addressing these additional challenges. The impressive metrics indicate that the core planning problem is becoming tractable, allowing the field to focus on these remaining practical considerations.\n\n----------\n\nFinally, we thank you again for raising important questions about our submission. Have we adequately addressed the main concerns? Please feel free to let us know if there are additional concerns or questions. Have a nice day!\n\n**Reference**\n\nChen, Binghong, et al. Retro*: Learning retrosynthetic planning with neural guided A* search. ICML. 2020."}]

Author rebuttal:

Meta-review: The authors present InterRetro, a novel framework for multi-step retrosynthesis planning that reframes the problem as a worst-path optimization within tree-structured Markov Decision Processes (MDPs). 

Reviewers agreed on the originality and justification of the proposed worst-path optimization. While initial concerns were raised regarding the absence of comparisons to certain state-of-the-art baselines and the precise attribution of performance improvements, the authors engaged extensively and effectively during the rebuttal period. They provided detailed explanations, additional experimental results, and prompt responses that comprehensively addressed these points. After the rebuttal session, reviews are generally positive about the responses.

Given the novel and well-supported methodology and the compelling experimental results, this submission deserves an accept. I would encourage the author to take the reviewers' comments into consideration to properly attribute the performance improvement to the components, and clarify the technical details in the revision.
Decision: Accept (poster)