Procedural Synthesis of Synthesizable Molecules
Authors: Michael Sun
Alston Lo
Minghao Guo
Jie Chen
Connor W. Coley
Wojciech Matusik
Venue: ICLR 2025 Poster (2025)
Keywords: molecular design
synthesis planning
tree generation
graph generation
Primary area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)
TL;DR: We tackle synthesizable analog generation and synthesizable molecule design by modeling the design of synthesis pathways as a conditional program synthesis problem.

Abstract: Designing synthetically accessible molecules and recommending analogs to unsynthesizable molecules are important problems for accelerating molecular discovery. We reconceptualize both problems using ideas from program synthesis. Drawing inspiration from syntax-guided synthesis approaches, we decouple the syntactic skeleton from the semantics of a synthetic tree to create a bilevel framework for reasoning about the combinatorial space of synthesis pathways. Given a molecule we aim to generate analogs for, we iteratively refine its skeletal characteristics via Markov Chain Monte Carlo simulations over the space of syntactic skeletons. Given a black-box oracle to optimize, we formulate a joint design space over syntactic templates and molecular descriptors and introduce evolutionary algorithms that optimize both syntactic and semantic dimensions synergistically. Our key insight is that once the syntactic skeleton is set, we can amortize over the search complexity of deriving the program's semantics by training policies to fully utilize the fixed horizon Markov Decision Process imposed by the syntactic template. We demonstrate performance advantages of our bilevel framework for synthesizable analog generation and synthesizable molecule design. Notably, our approach offers the user explicit control over the resources required to perform synthesis and biases the design space towards simpler solutions, making it particularly promising for autonomous synthesis platforms. Supporting code is at https://github.com/shiningsunnyday/SynthesisNet.

Published as a conference paper at ICLR 2025
PROCEDURAL SYNTHESIS OF SYNTHESIZABLE MOLECULES
Michael Sun', Alston Minghao Guo', Jie Chen', Connor Coley", Wojciech Matusik
MIT CSAIL. MITT-IBM Watson Al Lab, IBM Research [msun415, 5, on, wojciech| (esechh@@ssiii @csailcl....... mit edu, ccoleyemit.ede edu
*MIT Chemical Engineering chenjie@us. ibm. com,
ABSTRACT
Designing synthetically accessible molecules and recommending analogs to unsyn- thesizable molecules are important problems for accelerating molecular discovery. We reconceptualize both problems using ideas from program synthesis. Drawing inspiration from -onix- synthesis approaches, we decouple the syntactic skeleton from the semantics of synthetic tree create a bi-level framework for reasoning about the combinatorial space of synthesis pathways. Given a molecule we aim to generate analogs for, iteratively refine its skeletal characteristics via Markov Chain Monte Carlo simulations over the space of syntactic skeletons. Given black-box oracle optimize, formulate a joint design space over syntactic templates and molecular descriptors and introduce evolutionary algo- rithms that optimize both syntactic and semantic dimensions synergistically. Our key insight is that once the syntactic skeleton is set, we can amortize over the search complexity of deriving the program's 's semantics by training policies fully utilize the fixed horizon Markov decision process imposed by the syntactic template. We demonstrate performance advantages of our bi-level framework for synthesizable analog generation and synthesizable molecule design. Notably, our approach offers the user explicit control over the resources required to perform synthesis and biases the design space towards simpler solutions, which is par- ticularly promising for autonomous synthesis platforms. Supporting code is at https:/ /ttp://gittti com http/hhiiiiin http:///////i..nnuuunaaaaaaaaaaa SSntthsssttsst...
INTRODUCTION
The discovery of new molecular entities is central to advancements in fields such pharmaceuticals (Zhavoronkov et al., 2019; Lyu et al., 2019), materials science (Hachmann et al., 201 I; Janet et al., 2020), and environmental engineering (Zimmerman et al., 2020; Yao et al., 2021). Traditional make-design-tess workflows for molecular design typically rely on labor-intensive methods that involve high degree of trial and eиror (Sanchez-Lengelnn data-efficient approaches that minimize costly experimental trials are the key to accelerating these processes (Coley et al., 2020a;b; Gao et al., 2022). In recent years, large number of molecular generative models has been proposed (De Cao & Kipf, 2018; Ma et al., 2018; Simonovsky & Komodakis, 2018; You al., 2018; Li al., 2018 8; Samanta al., 2020; Liu et al., 2018; Jin et al., 2018; 2020; Guo et al., 2022; Sun et al., 2024). However, few of their outputs are feasible to make and proceed experimental testing due to their lack of consideration for synthesizability (Gao & Coley, 2020). This has motivated recent methods that integrate design and synthesis into a single workflow, aiming to optimize both processes simultaneously (Button al., 2019; Bradshaw et al., 2019; 2020; Gao et al., 2021; Swanson et al., 2024) which significantly closes the gap between the design and make steps, reducing cycle time significantly (Koscher et al., 2023; Volkamer et al., 2023; McCorkindale, 2023). This development is spurred by the curation of a small but robust collection of expert reaction templates that are inspired by real-world reactions and defined in close collaboration with chemists Hartenfeller al. (2011). This workflow facilitates de novo applications by imposing synthetic accessibility by design, recasting molecular design as navigating the space of possible synthetic procedures over of building blocks and forward reaction steps, defined in Vinkers
& Aspuru-Guzik, 2018). Systematic and

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et al. (2003). However, these methods still face computational challenges, particularly in navigating the combinatorial explosion of potential synthetic procedures (Smith, 1997).
Inspired by techniques in program synthesis, particularly syntax-guided synthesis (Alur et al., 2013), our method decouples the syntactical template a synthetic procedure (the skeleton) from their chemical semantics (the substance). This bifurcation allows for more granular optimization process, wherein the syntactical and semantic aspects of reaction pathways can be optimized independently yet synergistically. Our methodology employs bi-level optimization strategy. The upper level optimizes the syntactic template of the synthetic pathway, and the lower level fine-tunes the molecular descriptors within that given structural framework. This dual- dual-layered approach is facilitated by a surrogate policy, implemented by graph neural network, that propagates embeddings top-down following the topological order of the syntactical skeleton. This ensures that each step the synthetic pathway is optimized in context, respecting the overarching structural strategy while refining the molecular details. We address the combinatorial explosion in the number of programs using tailored strategies for fixed horizon Markov decision process (MDP) environments. This algorithm amortizes the complexity of the search space through predictive modeling and simulation of Markov Chain Monte Carlo (MCMC) processes (Metropolis et al., 1953; Hastings, 1970; Gilks et al., 1995), focusing on the generation and evaluation of syntactical skeletons. By leveraging the inductive biases from retrosynthetic analysis without resorting to retrosynthesis search, our approach combines accuracy and efficiency in "synthesizing" synthetic pathways. In summary, the contributions of this work are:
1. We reconceptualize molecule design and synthesis as a conditional program synthesis problem, establishing common terminology for bridging the two fields.
2. We propose bi-level framework that decouples the syntactical skeleton of a synthetic tree from the program semantics. Then, we introduce amortized algorithms within our framework for the tasks of synthesizable analog recommendation and synthesizable molecule design.
3. We demonstrate improvements across multiple dimensions of performance for both tasks, and include in-depth visualizations and analyses for understanding the source of our method's efficacy well its limitations.
RELATED WORKS
2.1 SYNTHESIZABLE ANALOG GENERATION
The problem of synthesizable analog generation aims to find molecules close to the target molecule that are synthesizable. Closely related but distinct from this problem is computer-assiste ret- rosynthetic analysis, which has developed through the decades (Corey et al., 1985) in tandem with computers, and now known as retrosynthetic planning due to its resemblance to more classical tests of Al based around planning. As retrosynthetic planning is done by working backwards (top-down), partial success is not straightforward to define. In other domains, procedural modeling is a bottom-up generation process that generates analogs by design (Merrell & Manocha, 2010; Merrell et al., 201 Müller et al., 2006; Merrell, 2023). Thus, synthesizable analog generation warrants more specialized methods. Prior works such as Dolfus et al. (2022); Levin al. (2023) address this by performing alterations of existing retrosynthesis routes, but this constrained approach severely limits the diversity of analogs. Instead, we neither start from search route nor constrain the search route, but instead, extract analogs via the iterative refinement of the program's 's syntactical skeleton with inner loop decoding of the program semantics using an MCMC sampler with stationary distribution governed by similarity to the target being conditioned on. This is a common technique used to search over procedural models of buildings, shapes, and furniture arrangements (Merrell al., 201 1; Talton al., 2011; Yu al., 201 1), and showcase its efficacy for the new application domain of molecules.
bi-level setup. We implement the iterative refinement phase
2.2 SYNTHESIZABLE MOLECULE DESIGN
The problem of synthesizable molecule design is to design the synthetic pathway, or program, whose output molecule optimizes some property oracle function. Note that unlike generic molecular opti- mization approaches, the design space is reformulated to guarantee synthesizability by construction. The early works to follow this formulation (Vinkers et al., 2003; Hartenfeller et al., 201 1; Button
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et al., 2019) machine learning assemble molecules by iteratively selecting building blocks and virtual reaction templates to enumerate a library, with recent works such as Swanson et al. (2024) obtaining experimental validation. The key computational challenge these methods must address is how to best navigate the combinatorial search space of synthetic pathways. Prior efforts using bottom-up generation (Bradshaw et al., 2019; 2020) probabilistically model synthetic trees as sequences of actions. These adopt an encoder-decoder approach to map between continuous latent space and complex combinatorial space. This results in low reconstruction accuracy and hinders the method on the task of conditional generation. Instead, SynNet (Gao et al., 2021) admits unified framework for solving analog generation and molecule design by formulating the problem as an infinite-horizon MDP and doing amortized tree generation conditioned on Morgan fingerprints. show improvements to both tasks through our novel formulation.
2.3 PROGRAM SYNTHESIS
Program synthesis is the problem of synthesizing a function f from set of primitives and operators to meet some correctness specification. A program synthesis problem entails: (1) a background theory T that is the vocabulary for constructing formulas, (2) correctness specification, i.e., logical formula involving the output of f and T, and (3) a set of expressions I. that f can take on, described by context-free grammar Gr. In molecular synthesis, for chemical reactions, constants for reagents, molecular graph isomorphism checking, and so forth. The correctness specification for finding synthesis route for a molecule M is simply f(B) M, where B is a set of building blocks, and we seek to find an implementation f from L to meet the specification. A coarser specification is match the molecule's ' fingerprint, FP()(B) and as shown by Gao et al. (2021), this relaxed formulation enables both analog generation and fingerprint-based molecule optimization. Our key innovation takes inspiration from the line of work surrounding syntax-guided synthesis (Alur et al., 2013; Schkufza et al., 2013). Syntax guidance explicitly constrains GL, which reduces the set of implementations f can take on (Alur et al., 2013) and facilitates more accurate amortized inference. Further discussion the connections between program synthesis and molecular synthesis and the similarities to the literature of retrosynthesis are given in App. D.
can formulate T as containing operators
FP.
Syntax Trees T
Synthetic Dataset
Molecules M
Fingerprints x C {0, 1]
R3
SMIRKS: [CI, [CI.Br.IIccS(ee:(e.....
Operators R
Programs P
R1
R2
R3
R4
R3
R2
R1
Primitives B
B1
B2
B3
Figure 1: Program synthesis terminology for modeling synthesis pathways.
3
METHODOLOGY
3.1
PROBLEM DEFINITION
For clarity, recapitulate the problems of interest discussed in Section 2:
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Synthesizable analog generation is the inverse task of inferring the program and inputs that best reconstructs a target molecule. Denoting the set of reactions as R and the set building blocks as B, we obtain compact yet expressive design space P: all non-trivial, attributed binary trees where each internal node corresponds to a reaction. Drawing parallels to the program synthesis literature, now be formalized: given a space of molecules M, learn mapping F: M + P x B", M = (P, B) such that B can be assigned to the leaf nodes of bottom-up manner (by recursively feeding the product of
call P the program space. The problem
P and running the reaction(s) in node's 's reaction to its parent) produces a molecule M. with minimal "distance" to M. Using program execution notation, the objective is stated as: arg min Px dist(P(B)... M).
Synthesizable molecule design is the forward task of finding the program and inputs whose output optimizes property oracle function. The oracle can represent property predictors, simulation, exper- imental validation, etc. but are black-box in nature. The objective arg min Oracle(P(B)).
Moving forwards, we identify (P, B) with its output M. when simplifies notation. Stripping the semantics from program leaves behind a syntactic skeleton, which lies in the space T of non-trivial binary trees. Figure illustrates the discussed terminologies'.
3.2 SOLUTION OVERVIEW
Herein, we use expert-defined reaction templates, popular abstraction codifying deterministic graph transformation patterns in the SMIRKS formal language. SMIRKS assumes the reactants are ordered for defining how atoms and bonds are transformed from reactants to products. Since templates are designed to encompass the most common and robust chemical transformations, ours are restricted to uni-molecular (e.g., isomerization, ring closure, fragmentation reactions) bi-molecular (e.g., additions, substitutions, or coupling reactions) reactions. practice, we featurize molecules using Morgan fingerprints with radius 2 and d 2048 bits, which is common molecular representation in both predictive and design tasks. This means that F is now technically a map over fingerprint space X' (0, 1jd. It is then natural use the Tanimoto distance between fingerprints as notion of molecular distance.
dist
M
F(),
F(w, Fii,)
F(a,T((3))
exp(-9 (dst. to AF)
7
(3)
Figure 2: (Left) Our Metropolis Metropolis-Hastinng algorithm in Section 3.3 iteratively refines the syntax tree skeleton towards the stationary distribution which proportional the inverse distance to our target molecule M. (Right) Our genetic algorithm over the joint design space X' x T in Section 3.4 combines the strategies of semantic crossover (-)) and syntactical mutation to encourage both
global improvement and local exploration.
3.3 BILEVEL SANTAX-SEMANTIC SYNTHESIZABLE ANALOG GENERATION
Given a molecule M E M. we aim to find program and inputs (P, B) whose output M, is most similar to M. Suppose first that the syntax 7 of P was given. Then, we would use two learned policies RR and TB that iteratively attribute reactions and building blocks to the nodes of T'' in topological order (Section 3.3.2). Recalling Section 2.3, this can be seen as parameterizing the derivation process of context-free grammar Gp. We give further details in App. E and discuss how these derivations are constrained through syntax.
Please refer Figure 3 of Gao al. (2021) for example chemical illustrations of the tasks.
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The application of our policies essentially specifies a syntax conditione program synthesis map F: M x T x B" However, T is not known during inference, so to remove this dependence, consider two further strategies. One is to learn classifier M T predict the most likely syntax tree of the program that produces M. We implement with multi-layer perceptron (MIP) trained under standard classification task. However, a single prediction may be inadequate, since there can be multiple skeletons corresponding to the same molecule. Instead, our second strategy uses bi-level setup, where the outer loop explores syntax space T through invocations of the inner loop F, which explores the program's 's semantics. This approach is further made efficient by amortizing the training of the inner loop.
3.3.1 1 OUTER LOOP: SYNTAX TREE STRUCTURE OPTIMIZATION
We simulate a Markov process on T for discovering skeletons whose decoding will maximize the similarity between M and the program's 's output (Figure 2 (Left)). The details for how we bootstrap and apply T in App. A. We adopt the Serropolis-Hasting algorithm with proposal distribution q(T') (T) exp(-Xd7( To)) and scoring function f(2) x xxp(-3on d F (M)))), where and 3 are parameters that trade-off exploration with exploitation, dr the tree edit distance, and the inner loop to score candidates in T.
dm is the Tanimoto distance. In other words,
3.3.2 INNER LOOP: INFERENCE OF TREE SEMANTICS
We now formulate syntax-conditioned derivations under Gp finite-horizon MDP.
State space: To bridge T and P x B', we introduce an intermediate state space of partial programs 0P consisting of all possible partial programs arising from the following modifications to any syntax tree T E T: (1) prepend a new root node of T, and attribute it with fingerprint from X, or (2) attribute some internal (resp. leaf) nodes of T with elements of R (resp. B). We further require that if node in T is filled, then so is its parent. Intuitively, OP comprises all partially filled-in trees in T obeying topologiaal order (with added root node attached a molecular fingerprint).
Action space: At a state S E OP, the actions are to attribute any frontier node, i.e., unfilled nodes whose parents are filled, with an item from R (resp. B) if the node is internal (resp. a leaf).
We parameterize the policies Tą: 0P - R' and πg: 0P + B* with separate graph neural networks. Given partial program as input, the former predicts the reactions that should be attributed to internal frontier nodes as an [R] llaell classification problem, while the latter does so for building blocks and leaf nodes. However, [B] >> [R. is very large, so лड instead predicts -56-dimensional embedding each nodes which implicitly specifies the building block whose 256-bit Morgan fingerprint is closest.
Policy network:
Training: We train both policies using supervised policy learning. Key to this approach is the dataset used for training constructed using Algorithm See App. for further details.
Algorithm Construction of training dataset.
Require: A synthetic dataset D, S P x B* of programs (Section 1.1.
1: D <= Ø
2: for each (P) E Do do
3: Turn (P) into fully-filled program E OP whose root is attributed with FP(P, B).
4: for each A 2 containing the root and closed under parent(-) do
5: Frontier(A) + {ie T i & and parent(i) A}
6: Populate node features H and labels Y based on P and B (App. F)
7:
for i A do
8:
Mask the feature in H corresponding to node for i T Frontier(A) do Mask the label in Y corresponding to node
9:
10:
11:
D D (TT, H,Y
return D
II practice, we restrict finite subset of T by imposing a maximum number of reaction nodes.
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R3
R1
closest f.p.
B1
for all topological orders
Figure 3: Illustration of our decoding scheme F: (Left) The input is a Morgan fingerprint æ and syntax skeleton T; (Middle) Decode once for every topologiaal ordering of the tree, tracking all partial programs with stack; (Right) Execute all decoded programs, then returning the closest analog which minimizes distance to æ.
3.4 BI-LEVEL SYNTAX- XYNTAX-SEMANTIC SEMANTIC SYNTHESIZABLE MOLECULAR DESIGN
The task of synthesizable molecule design is to find program / and building blocks B whose M. maximizes a property of interest. Given the learned policies from synthetic planning, we apply the inner loop procedure F: X x T + P x B as a surrogate, casting the optimization problem as one over the joint design space over fingerprint space X' that leverages this extra dimension of syntax T (Figure 2 (Right)). The seed population obtained by sampling random bit vectors. To generate a child xr from two parents a and 222, we combine semantic crossover with syntactical mutation, reminiscent of our bi-level approach for analog generation:
We approach this problem with a genetic algorithm (GA)
Semantic crossover: We first generate æ. by combining bits from both and æ2 and possibly mutating small number of bits of the result.
Syntactic mutation: We set T T(x.) and apply random edit(s) to obtain perturbed tree TV. Together, (x,, T) and (æ,, T') form a sibling pool. Applying the surrogate F to each child gives two sibling SMILES that turned into two sibling fingerprints. We fit Gaussian process past individuals and select the sibling with the highest expected improvement as the favoured child æ.
Intuitively, semantic crossover optimizes for chemical semanties by combining existing ones from the mating pool, while syntactic mutation explores syntactic analogs of individuals of interest. Each child is then given fitness score under the property oracle, and the top scoring unique individuals are retained into the next generation (i.e., elitist selection). Further details and hyperparameters are given in App. G.
EXPERIMENTS
4.1 EXPERIMENT SETUP
4.1.1 DATA GENERATION
We use 91 reaction templates from Hartenfeller et (2011): Button al. (2019) representative of common synthetic reactions. They consist primarily of ring formation and fusing reactions but also peripheral modifications. Starting from 147,505 purchaseable compounds from Enamine Building Blocks, we follow the same steps as Gao et al. (2021) to generate 600,000 synthetic trees. Filtering by QED > 0.5 the product molecules leaves 227,808 synthetic trees (136,684 for training, 45,563 for validation, and 45,561 for testing), which are then preprocessed into programs to construct our final datasets. We bootstrap our set of syntactic templates based on those observed in the training set, resulting in 1117 syntactic skeleton classes. Additional statistics these syntactic templates and insights on their coverage are given in App. A. Further analyses of the structure- structure-property property relationship and detailed case study given in App. C.

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4.1.2 BASELINES
We evaluate against all 25 molecular optimization methods benchmarked in the large-scale study by Gao et al. (2022). Most of these are divided into three categories based on the molecular representation used: string, graph, or synthesis trees. Synthesis methods restrict the design space to only products of robust template- based reactions, so for fair comparison, we also consider intra-category rankings. We report the synthetic accessibility (SA) score (Ertl & Schuffenhauer, 2009) of the optimized molecules to cross-verify the synthesizabilityy of synthesis-based methods well investigate the performance trade-off imposed by constraining for template-compatibl synthesizability.
4.1.3 COMPUTATIONAL EFFICIENCY
Constructing D. Alg. suggests [D] (2maxT (Do)) because compute all the (closed under parent(-)) +) masks per P E Do. However, we don't need to explicitly store DD We only need to precompute the (closed under parent(-)) masks for each T E To (k fixed and small), so the running time is O'Err ) independent of Do. Besides, the actual number of masks is far smaller than 2 and high [T'] less represented in Do, since large programs are less likely to be sampled, so in practice |D| is much smaller than 2max, 1T (Do). Detailed statistics are given in App A.
Training with D. During training, we flat-index into Do and their precomputed masks to perform a pass over D. Despite the larger dataset, we find the total training steps to actually be comparable with Gao et al. (2021). 1). To scale to larger k; we propose a stratified sampling strategy that does only (Po) pass per epoch, has positive support over D, and represents each (P, B) Do equally. The idea is to sample a constant number of masks C' per P. B E Do. and re-sample each epoch. We show this has complexity linear to the size of Do per epoch but empirically converges in much fewer training steps than SynNet for C = 1. Details are in App F.7. We further include an ablation study on the downstream task performance of this simplified training strategy in App F.5,
4.1.4 EVALUATION METRICS
Synthesizable analog generation. We evaluate the ability to generate diverse set of structural analogs to a given input molecule using the Recovery Rate (RR, whether the most similar analog is exactly the target), Average Similarity (as measured by Tanimoto distance to the input), SA score, and Internal Diversity (average pairwise Tanimoto distance).
Synthesizable molecule design. We evaluate our method's ability to optimize 15 oracle functions (Huang et al., 2022) relevant to real-world drug discovery:
1. Bioactivity predictors (GSK333 JNK3, DRD2) that estimate responses against targets related to the pathogenesis of various discases such as Alzheimer's disease (Koch al., 2015) based on experimental data (Sun et al., 2017) (),, and whose inhibitors are the basis for many antipsychotics and have shown promise for treating diseases like Parkinson's 's schizophrenia and bipolar disorder (Madras, 2013).
2. Structural profiles (Medianl, Median2, Rediscovery) that primarily focus on maximizing structural similarity multiple molecules, which is useful for designing molecules fitting more multifaceted structural profile (Brown et al., 2004). The rediscovery oracle focuses on hit expansion around specific drug.
3. Multi-property objectives (Osimertinib, 6 others) that use real drugs as a basis for optimizing additional pharmacological properties, mimicking real-world drug discovery.
4. Docking Simulations (MP DRD3) against MF the main protease of SARS-Cov-2, and DRD3, which has its own leaderboard with a particular focus on sample efficiency.
In addition to the average score of the top molecules, we particularly focus on sample efficiency, ie., the top-k AUC as described in Gao et al. (2022).
4.2 RESULTS ON SYNTHESIZABLEE ANALOG GENERATION
In Table 1, we see our method outperforming SynNet across both dimensions of similarity (how "analog" compounds are) and diversity (how different the compounds are). Additionally, our method
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Table We generate 5 unique analog molecules conditioned on imput molecule M and sort them by decreasing similarity to M. For SynNet, we follow their beam search strategy and produce analogs using the top beams. For Ours (T), we sample the top 5 syntactic templates from т. Ours (T, rev) the same except we use bottom-up decoding process, and it is included as ablation for Section 4.4.1. Then, we evaluate how similar, diverse, and structurally simple the first & molecules are. The best method is bolded. For three-way comparisons, the second best method is underlined.
Avg. Sim. f
SA 4
Diversity t Top-3 Top-5
Dataset
Method
RR f
Top-1 Top-3 Top-5
Top-1
Top-3
Top-5
Ours (T, rev) Ours (7)
79.3 %
0.923
0.632
0.569
3.072
2.795
2.716
0.615 0.657
Train Set
88.1 %
0.958
0.704 0.626
3.099
2.928
2.852
0.532 0.615
SynNet
46.3%
0.766
0.622 0.566
3.108
3.057
3.035
0.525
0.584
Test Set
Ours (r, rev) Ours (7)
40.8
0.749
0,548 0.487
2.970
2.743
2.659
0.640 0.685
52.3%
0,799
0.588 0.548
3.075
2.895
2.856
0.609 0.653
SynNet
4.9%
0.499, 0,436, 0.394
2.669,
2.685,
2.697
0.644, 0.693
ChEMBL
Ours (7)
7.6%
0,531, 0,443, 0.396
2.544,
2.510, 2.460
0.675,
0.727
Ours (MCMC)
9.2%
0.532, 0,486, 0.432
2.364,
2.310, 2.263
0.765, 0.759
achieves lower SA Score, which is a proxy for synthetic accessibility that rewards simpler molecules. Guided by a set of simple yet expressive syntactic templates, our model simultaneously produces more diverse and structurally simple molecules without sacrificing one for the other. Additionally, our policy network is well-suited to navigate these simple yet horizon structures, enabling 6% higher reconstruction accuracy after training on the same dataset. Combining these three dimensions, we conclude our method is the superior one for the task of synthesizable analog generation. To better understand which design choices are responsible for the performance, we provided a comprchensive analysis of the policy network in App. F. We begin in App. E.3 by elaborating on the main distinction of our method vs existing works, highlight the novelty of our formulation, and motivate an auxiliary training task that takes inspiration from cutting-edge ideas in inductive program synthesis. We then perform several key ablations in App. F.4, using concrete examples to highlight success and failure cases. Lastly, we perform step- walkthrough of our decoding algorithm in App. F88, visualizing the evolution of attention weights to showcase the full- horizoon awareness of our surrogate and the dynamic incorporation of new information. These analyses shed insights into why our surrogate works, and points to future extensions to make it even better.
Table 2: Our model's average performance across 13 TDC oracles compared to baselines compiled in Gao et al. (2022). We limit to 1000 oracle calls each run and normalize oracle outputs to [o, 1]. We report mean score, AUC, and SA scores (Ertl & Schuffenhauer, 2009) of the top 10 molecules. Methods are categorized by Gao et al. (2022) and, for brevity, we display only the top three algorithms within each category with respect their AUC. The best method per column is bolded and the best synthesis-based method is underlined. See App. H for the full results and experiment details.
Score
AUC
SA
Calegory
Method
Value Rank
Value Rank
Value
Rank
Screening
0.426
20
0.377
20
3.097
Screening
MolPAI
0.472
16
0.444
15
3.018
4
REINVENT
0.607
2
3.415
VENNSEES-SELFIES
0.682
3
0.578
4
3.791
15
String
STONED
0.609
0.555 8
6
5.550
24
7 rows omitted ...
Graph-GA GPBO
0.701
1
0.601
3
3,982
17
Graph
0.642
6
0.570
5
3,954
16
DST
0.555
10
0.479
11
4,146
20
7 rows omitted
SynNet DoG-Gen
0.578
9
0.545
7
3.075
6
0.634
7
0,511
9
2,793
2
Synthesis
DoG-AE
0.460
18
0.450
14
2.857
3
Ours
0.670
4
0.608
2.739
1
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Table 3: Autk Vina scores against DRD3 and M limited to 5000 oracle calls. For ZINC (Screening), we use numbers from TDC's DRD3 Leaderboard, and for SynNet, we report both their paper's numbers (*) and our reproduced results. We also report the top binders for M for the real-world case study in App. I.
Score
AUCT Top-10
SA
Target
Method
#Calls
1st
2nd
3rd
Top-10
Top-100
Top-10
ZINC
12.8
12.59
12.08
SynNet*
5000
12.3
12.02
11.13
DRD3 SynNet
5000
10.8
10.4
10.3
10.30
9:20
9.55
2.59
Ours
5000
13.7
13.1
13.1
13.01
12.13
11.91
2.13
SynNet
5000
8.3
8.3
8.2
8.09
7.46
7.60
2.27
M
Ours
5000
9.9
9.7
9.7
9.54
9.02
9.01
2.59
SynNet*
10.5
9.3
9.3
Ours
10000
10.8
10.7
10.6
Table 4: (Left) Ablation of sibling pool generation strategies on JNK3: (edits) mutates the syntax, (7) uses the top skeletons predicted from T, and (flips) doesn't consider skeleton and instead flips random bits in the fingerprint. (Right) Ablation of SynNet with our Bayesian optimization (BO) acquisition over a sibling pool generated by beam search. Seeds and All are the average scores of the initial and final populations.
Oracie
Method
Top-1
Top- 10
Top. 100
Seeds
All
SynNet
0.94
0.051
0.80M 0.041
SynNei RD
0.85
0.024
0.447
1st
2nd
3rd
Top 10
Top 100
Diversry
Ouzs
0.58
0.055
(calt))
(U87
0.8
SynNee
0.80
0.719
0.025
0.715
JNK3
SynNet DO
0.31
0.241
0.143
0.006 1 0.012
0.134 0.000
(flipe)
0.77
Ows
0.88
0.862
0,800
0.050 = 0.053
0.792 0,150
SynNet
1.000
1.000
0.998
0.007 1: 0.018
0.996 1: 000
DRD2
SynNer BO
0.982
0,963
0.722
0.305 :: 0.018
0.672 0.144
Curs
1,000
1,000
1.000
0.022 + 0.056
1,000 0,000
4.3 RESULTS ON SYNTHESIZABLE MOLECULE DESIGN
In Table 2, we see our method outperforming all synthesis-based methods on average across the 13 TDC oracles for all considered metrics- average score, AUC, and SA score. Surprisingly, our method stays competitive with the state-of-the-art string and graph methods in terms of average score (ranking 4th) but being considerably more sample-efficient finding the top molecules (ranking 1st for top- 1/10 AUC). We see evidence that sathhesszaaility-oosttaaaa design space does not sacrifice end performance when reaping benefits of enhanced synthetic accessibility and sample efficiency.
The AutoDock Vina scores reflect our method's strength in real-world ligand design tasks. Our best binders against M in Table are significantly better than nearly all known inhibitors from virtual screening or literature (Ghahremanpour et al., 2020; Zhang al., 2021). For example, Zhang et al. (2021) report best score of -8.5. Our best binders against DRD3 also rank us 3rd the TDC Leaderboard (as of Aug. 2024). We present additional analysis of the best binders for our two docking targets in App. I.
4.4 ABLATION STUDIES
In Tables and 4, we analyze findings from four carefully designed ablation studies (I 4.4.1, & 3 in 4.4.2, 4 in 4.4.3) to justify the key design decisions that differentiate our method from the predecessor SynNet as well as other synthesis-basee methods that have similar modules.
4.4.1 Top-down vs Bottom-up Decoding.
Should we decode top-down or bottom-up? This separation exists between retrosynthesis vs molecular design. Baselines that serialize the construction of synthesis trees (Bradshaw et al., 2019; 2020; Gao et al., 2021) 1) adopt the latter. Given only knowledge of the target molecule, the model has to predict the first building block, the first reaction, and by Gao et al. (2021) that the first few steps are difficult, due to inherent ambiguity multiple valid choices exist. Our method resolves this bottleneck by reformulating the (Markov) state partial
Our empirical findings confirm observations

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Published as a conference paper at ICLR 2025
syntax trees, where holes reactions and building blocks left to predict. This state captures the horizon structure, so we can learn tailored policies for the fixed horizon. We reintroduce the inductive bias of retrosynthetic analysis to procedural synthesis. Conditioned show a top-down filling order outperforms bottom-up, with two intuitive explanations: (1) there orders of magnitude less reactions than building blocks (91 vs. 147,505) and (2) it is easier to reason backwards from the specification (target molecule) which reactions lead to the product. To demonstrate these factors compensate for any gains from bottom-up pruning of applicable reactions, we perform an additional ablation in Table 1. Instead of the MDP enforcing we fill in a skeleton top-down, we fill the skeleton from the bottom-up. We retrain the model by pretraining on inverted masks, and decode by following every possible topological order of the skeleton with edges reversed. The results show this cannot reconstruct the training data as well and struggles on generalization.
the syntax (skeleton),
4.4.2 How Analog Generation Capabilities Translate to Design Capabilities.
Why does our superior analog generation capability translate to better performance when used as an offspring generator within molecule optimization? An ideal surrogate takes as input a fingerprint and outputs multiple synthesizable analogs, creating diverse offspring(s) that balance local neighborhood exploration with global exploitation. SynNet does the former by mutating the fingerprint directly, whereas the key insight of our syntax-guides method is to mutate the syntactic skeleton instead, doing via editing mutations. Table 4 (Left) shows edit-based mutations are superior to the top recognition strategy used for analog generation ((Skeleton) in Table 1) and the trivial strategy of ignoring the skeleton and flipping individual bits obtain siblings. This suggests edits to the skeleton better preserves the locality bias within the GA. Higher population diversity and average scores for k {10, 100} suggest the same symbiotic relationship between diversity and similarity in analog generation is also the key enabler to better GA optimization. Our GA benefits from an inner loop sibling acquisition within the crossover operation, acquiring the highest expected improvement sibling to expend an oracle call on. It can be argued this extra mechanism is why our method gets better results and makes for an unfair comparison with SynNet, or that this is a method-agnostic hack to improve GA performance. In Table 4 (Right), we show SynNet endowed with a similar mechanism in its crossover operation (generate an offisring pool using the top beams then apply a BO acquisition step on top) didn't it improve, but actually downgraded the performance. We hypothesize that SynNet's optimization trajectory is derailed by the additional variation to its sibling pool, reducing local movements within the output space that syntactic editing approach naturally preserves. Thus, believe the performance gains of this mechanism is unlocked by our syntax- driven approach.
4.4.3 Extrapolation to Unseen Templates.
How dependent is our framework on the initial set of syntactic templates? Can SynthesisNet generalize to more templates? Our ablation study in App. B evaluate whether the model can extrapolate new template classes. We investigate: 1) how well SynthesisNet extrapolates to programs whose structural template was unseen during training, 2) how well the framework can incorporate new templates at test time, and 3) how robust overall task performance is to them. To answer these questions, we hold out x 25% of Ō to be the test set, remove all programs belonging to those templates, retrain, and analyze changes in task performances, both holistic results and results specific to the held-out templates. Our results reveal minor performance drop, and in some instances, improved results along some metrics. Our analysis reveals the surrogate model is robust and unsensitive to missing templates, and the framework can also incorporate unseen templates in the online phases of the downstream tasks. We encourage future works to study the exact scaling laws between templates and performance, and we hope initial findings unlock new directions for scaling up our solutions to achieve greater coverage and impact.
5 DISCUSSION & CONCLUSION
We reconceptualize synthesis pathway design using program synthesis approach, introducing a bi- level framework that separates the syntactical skeleton of a synthetic tree from its chemical semantics. Our learning algorithms leverage the tree horizon structure, improving performance on key metrics of analog generation and de novo design. By decoupling syntax and semantics, we effectively navigate a rich design space, integrating design and synthesis into single workflow that reduces discovery cycle time. Our framework offers control over synthesis resources and biases towards simpler solutions, with the exciting prospect of integration with autonomous synthesis platforms (Coley et al., 2019).
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Reviews:
[{"review_id": "fBu8FH36tM", "reviewer": "Reviewer_EsMz", "summary": "1. This paper addresses the challenge of designing synthesizable molecules and creating analogs for unsynthesizable ones by framing these tasks within a program synthesis framework. The paper introduces a bi-level approach to explore synthesis pathways by separating the structural skeleton of a synthetic pathway from its functional properties. \n2. Through Markov Chain Monte Carlo simulations, they refine molecular structures iteratively and optimize both syntactic and semantic dimensions with evolutionary algorithms.", "questions": "1. How robust is bi-level framework to work with other architectures other than TransformerConv, something like Decision Transformer ?\n2. Is there any recent papers like GFlowNet that could be referred other than synthesis-based SynNet published in 2022?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The problem statement is well-defined, and the methods for synthesizing analogs and generating molecules are clearly explained, including the program's semantics.\n2. The method achieves state-of-the-art performance in molecule generation on benchmark datasets and demonstrates significantly greater efficiency than the SynNet method when tested with various oracles, such as GSK, JNK, and DRD2.\n3. Experimental analysis was conducted using various evaluation metrics, including bioactivity predictors (oracles), structural profiles, multi-property objectives, and docking simulations. The extensive number of experiments, detailed in Appendix G, provides strong evidence that this method outperforms other benchmarking methods.\n4. The figures in the paper effectively clarify the methodology. The t-SNE and MDS plots in Appendix B, based on data from the final hidden layer representation of the MLP, clearly illustrate the most popular skeleton classes.\n5. The model architecture used in the method is thoroughly explained, with detailed insights provided in the attention visualization section in Appendix E.", "weaknesses": "1. This paper failed to mention the source code / anonymous repository and also in Appendix E . 6 ATTENTION VISUALIZATION figure number is missing.\n2. Results are compared against the 2022 paper; The authors have not compared the results against any recent publications.\n3. This paper doesn't address the computational cost or effectiveness of the algorithms. How long does it take to train the inner loop given ~136k synthetic trees, molecule generation or analog creation?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QSn97lzZvb", "reviewer": "Reviewer_XGkL", "summary": "The paper reframes molecule design and synthesizable analog recommendation as conditional program synthesis problems. It introduces a bi-level framework that separates the syntactic skeleton of synthesis pathways from their chemical semantics, allowing efficient exploration of both syntactic and semantic design spaces using evolutionary algorithms and Markov Chain Monte Carlo simulations. By leveraging fixed-horizon Markov decision processes, the approach improves synthesizable molecule generation and offers control over synthesis complexity. Results demonstrate enhanced performance and resource efficiency, positioning this method as a promising tool for automated molecular discovery.", "questions": "The primary concern lies in the method's scalability under the current scheme. It remains unclear how the approach would handle a significantly larger design space with more templates, and how its performance would hold up as template diversity increases.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Frames molecular design and synthesizable analog recommendation as conditional program synthesis tasks, offering a fresh perspective in this field.\n2. Demonstrates robust performance across key metrics, underscoring the effectiveness of the proposed methods.\n3. Provides thorough experiments that validate the approach and its contributions to molecular design and synthesis.", "weaknesses": "1. The current approach uses a limited number of templates, and it is unclear how this framework could be expanded to include a broader range of templates, which could limit its flexibility.\n2. Although the authors claim efficiency, the paper lacks direct comparisons to demonstrate this advantage against other methods.\n3. The comparison between tasks in Section 3.1 could be enhanced with mathematical notation alongside chemistry examples. While the method draws on program synthesis concepts, the explanation may be confusing without using clear chemical illustrations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gmakcqCywk", "reviewer": "Reviewer_Z3YQ", "summary": "This paper presents a method for synthesize of synthesizable molecules. It includes separates the syntactic skeleton (structure) of a molecule from the semantics (functional groups and properties), Markov Chain Monte Carlo (MCMC) Simulations for skeleton refinement, Genetic Algorithms for the optimization of both the structural and chemical aspects of molecules, and Surrogate Modeling with Graph Neural Networks to represent the molecular structure.\n\nThe experiments show that the proposed framework matches and outperforms the current state of the art approaches to synthesizable molecule and synthesizable analog design.", "questions": "How would the method handle a much larger set of the input alphabet and structural templates?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The approach is interesting from the soft-computing point of view. The authors leverage the four different approaches on the right places. The separation between the structure and the content in the synthesis approach is interesting in particular. The use of MCMC and GA is more standard but is well suited for the new candidate tree generation and the search of the best structure and content of candidate molecule.", "weaknesses": "- I think the possible weakness is the dependency on the tree and the grammar components. On one hand having a very large amount of templates will increase the computational complexity of the model (it is not clear how for instance the MCMC algorithm would handle this) and on the other hand a more efficient smaller set will not allow to generate all desired solutions", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "BDvGtxnkkt", "reviewer": "Reviewer_iU5H", "summary": "In this paper, the authors address the problems of synthesizable analog generation and synthesizable molecule design, employing syntax-guided synthesis techniques from the field of program synthesis. Specifically, they developed a bi-level framework that decouples the syntactical skeleton of a synthetic tree from the program semantics, and introduced amortized RL algorithms based on the framework. They demonstrated improvements across multiple dimensions of performance for both tasks, and include in-depth visualizations and ablation studies.", "questions": "See above", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The problem of designing useful molecules with desirable properties and good synthesizability is very critical to drug discovery. \n2. The connection between the problem and the program synthesis community is vital. \n3. The method is carefully designed and well executed in experiments.", "weaknesses": "1. Since this work heavily involves discussion on search space, and amortized search within the synthesis tree, similar things are also explored deeply in the retrosythesis community. Thus, it would be beneficial to incorporate some discussions in the paper to discuss the connections and differences between this work and previous work.\n\n    1. Self-Improved Retrosynthetic Planning, Kim et al., ICML 2021\n    2. Retrosynthetic Planning with Dual Value Networks, Liu et al., ICML 2023\n    3. Retrosynthesis Zero: Self-Improving Global Synthesis Planning Using Reinforcement Learning, Guo et al., JCTC 2024\n\n\n2. The paper is clear and well-written. However, it would be helpful if the introduction includes a reference to the detailed description of the two tasks in the related work section. This will help readers understand the tasks before they delve into the techniques used.\n\n3. Regarding the experimental setup, could you explain the choice of the 91 reaction templates and the 147,505 building block compounds? Are they forward reaction templates? Do these choices reflect real-world applications in molecular design?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)