AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning
Authors: Mohammadamin Tavakoli
Pierre Baldi
Ann Marie Carlton
Yinting Chiu
Alexander Shmakov
David Van Vranken
Venue: NeurIPS 2023 poster (2023)
Keywords: Chemistry
Reactions
Contrastive
Radical
Graph

Abstract: Deep learning-based reaction predictors have undergone significant architectural evolution. However, their reliance on reactions from the US Patent Office results in a lack of interpretable predictions and limited generalizability to other chemistry domains, such as radical and atmospheric chemistry. To address these challenges, we introduce a new reaction predictor system, RMechRP, that leverages contrastive learning in conjunction with mechanistic pathways, the most interpretable representation of chemical reactions. Specifically designed for radical reactions, RMechRP provides different levels of interpretation of chemical reactions. We develop and train multiple deep-learning models using RMechDB, a public database of radical reactions, to establish the first benchmark for predicting radical reactions. Our results demonstrate the effectiveness of RMechRP in providing accurate and interpretable predictions of radical reactions, and its potential for various applications in atmospheric chemistry.

AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning
Mohammadamin Tavakoli
Yin Ting T.Chiu
Department of Computer Science University of California, Irvine mohamadt@uci.cem
Department of Chemistry University of California, Irvine yintcluci. edu
Alexander Shmakov Department of Computer Science University of California, Irvine
Ann Marie Carlton Department of Chemistry University of California, Irvine agcarlto@uci. edu
ashmakovQuci.co edu
David Van Vranken Department of Chemistry University of California, Irvine david. wwwuviiievooom edu
Pierre Baldi
Department of Computer Science University of California, Irvine pfbaldi@uee edu
Abstract
Deep learning-basee based reaction predictors have undergone significant architectural evolution. However, their reliance on reactions from the US Patent Office results in lack of interpretable predictions and limited generalizability domains, such as radical and atmospheric chemistry. To address these challenges, we introduce a new reaction predictor system, RMechRP, that leverages contrastive learning in conjunction with mechanistic pathways, the most interpretable represen- tation chemical reactions. Specifically designed for radical reactions, RMechRP provides different levels of interpretation of chemical reactions. We develop and train multiple deep-learning models using RMechDB, public database of radical reactions, to establish the first benchmark for predicting radical reactions. Our results demonstrate the effectiveness of RMechRP in providing accurate and inter- pretable predictions of radical reactions, and its potential for various applications in atmospheric chemistry.
other chemistry
Introduction
Three primary approaches have been employed for the systematic prediction of chemical reactions: Quantum Mechanics simulations learning methods [ [00 II
[
2) Template-basee methods [3] 4 [ 6 77 8) and machine
14 15). Notably, among these methods, machine learning techniques have demonstrated superior generalization capabilities and have enabled high-throughput
I2
13
predictions.
However, the majority of recently developed data-driven predictive models are primarily designed to operate at the level of overall transformations. These predictors trained using the chemical transformation dataset sourced from the US Patent Office (USPTO) [16,, along with a few additional, is important to note that all these datasets have notable limitations, which include: limited coverage of chemical reactions, lack of information about the byproducts, imbalance between reactants and products,
though relatively small, datasets introduced scatteredly in various studies [1 18 19]
37th Conference on Neural Information Processing Systems (NcurIPS 2023).

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and scarcity of information concerning elementary reaction steps. For example, the USPTO dataset predominantly presents chemical reactions as overall transformations with a single primary product, providing limited insights into underlying mechanisms, critical intermediates, and side products. Moreover, extracting information on radical reactions challenging. as they are underrepresented in this dataset.
The utilization of this constrained source of data for training data-driven models leads predictive models that may have inherent limitations and biases, primarily catering to specific types of chemical reactions. These models often struggle to predict balanced reactions and lack crucial information regarding intermediate byproducts. Moreover, they fall short in offering explanations regarding the underlying chemistry responsible for the predicted products. Given the scarcity of available training data, there is currently no widely adopted reaction predictor specifically tailored for radical reactions. Radical reactions play a significant role in various domains, including synthetic pathway planning, biological chemistry, and atmospheric chemistry. These reactions frequently involve intricate sequences of chemical steps and highly branched mechanistic pathways. Hence, there is critical need to develop an accurate radical reaction predictor that can effectively address the aforementioned limitations.
Interpretability of Mechanistic Reaction Steps
Instead of overall transformations (e.g., samples of the USPTO dataset), expert chemists typically employ an alternative approach to represent and conceptualize chemical reactions. Specifically, they utilize an intuitive representation that involves the consideration of single-state transitions and arrow-pushing mechanisms, which we refer to as elementary reaction mechanisms or elementary steps. Overall chemical transformations can be deconstructed into a chain of elementary reaction mechanisms, each characterized by a singular transition state [20)21]. Figure[[provide, an illustrative example of this process. Curved arrows (or fish-hook arrows for radical reactions) are employed to depict the reaction mechanisms
[22]. and correspond to the interaction of singly occupied molecular orbitals with both the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) [23]. Hence, the development of a reaction predictor that operates at the reaction mechanism level can confer three critical benefits (shown Figure] that none of the current reaction predictors can offer.
Chemical interpretabilityy:
The first key benefit is enhanced chemical/orbital interpretability. The use of curly arrows, or arrow-pushing mechanisms, allows for an accurate understanding of the fundamental chemistry underlying each reaction step. This approach facilitates the understanding of the interactions between molecular orbitals, which ultimately drive each reaction step.
Pathway interpretability: The second key benefit is enhanced pathwaylrannsforatiinn interpretabil- ity. A predictor trained to predict elementary steps can be iterated to expand a tree of such steps rooted at the initial reactants. This allows for the interpretation of any overall transformations leading to several final products, some of which are unknown. By expanding such a tree of pathways there is no chance of missing key intermediates that give rise competing pathways during change. This level of interpretability enables several applications, most importantly for drug discovery and atmospheric chemistry where highly reactive radicals are involved in massively branched reactions.
Balance and atom mapping: Finally, the third benefit is the preservation of the balance between reactants and products, in conjunction with the underlying atom mapping. The balance is maintained at all times throughout the tree of pathways (i.e., the chain of reaction steps), which can be highly valuable, for instance in retrosynthesis 24 and mass spectrometry.
3 Data
To develop a radical reaction predictor operating at the mechanistic steps, we utilize the standard train and test sets from the recently released RMechDB dataset [25). This dataset comprises approximately 5500 radical elementary step reactions sourced from chemistry textbooks (core reactions) and scientific articles on atmospheric chemistry (atmospheric reactions). Each reaction in the RMechDB dataset is labeled with different categorizations enabling comprehensive evaluation of the predictive models trained on RMechDB. See Table for a summary of the RMechDB dataset used subsequent training and testing experiments.

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Overall Transformation from USPTO
Broken Down Into the Following Mechanistic Steps:
NHz
Figure I:
The reaction the top is overall, unbalanced, transformation from the USPTO dataset. can be broken down into four mechanistic steps with arrow-pusie prrow-pushing mechanisms, This provides chemical interpretability for each step, as well for the overall pathway while maintaining full balance at each step.
Table 1: The size of the various subsets of elementary radical reaction steps contained in the RdachDBdatabuse (25]. These are used train and test the predictors.
Train
Test
Core
1512
150
Specific
3397
367
Combined
4909
517
4 Methods
Here we first describe the OrbChain as a model of radical mechanistic reaction based on the interaction of idealized molecular orbitals. We utilize Orbchain represent and process radical mechanistic reactions for machine learning models. Then we describe three different machine learning approaches for predicting the outcome radical mechanistic reactions with or without their associated arrow- pushing mechanisms. The first approach follows the methodology described in (20: 21 26 where predictions are carried out using deep learning two steps. The first step identifies reactive sites, while the second step ranks the plausibility of all possible reactions based on the interactions of the previously identified reactive sites. In the second approach, we directly predict the most reactive pair of molecular orbital. Within this one-step prediction approach, we test two different representations for the atoms: our own atom descriptor and RxnHypergraph [27] which applies a transformer model to the molecular graph representations. Lastly, we use purely text- text-based approach (28) leveraging transformers and large language models to predict the products radical reactions using a neural machine translation system [29]. This approach is based the text representation of chemical reaction and aims to "translate" the sequence of reactants into the sequence products. Notably, this approach only outputs the string associated with the products, without the arrow-pu sroo-pushing information. As a result, this approach cannot provide chemical interpretability. although it shows promising results the RMechDB test sets.
4.1 OrbChain: Standard Model of Mechanistic Reactions
radical mechanistic reaction Rxn reaction with single transition state that involves least
one half-occupied orbital. Rxn consists set of reactant molecules R (R; { set of product molecules P = " and a set of fish-hook arrows A showing a bond cleavage and movements of single electrons. Each of the reactant and product molecules R, P; represented by a connected molecular graph G; (N;, V.) where the vertices N, represent labeled atoms {a}} and edges V
represent labeled bonds {b) ). Inspired by 120 21. we model radical mechanism by OrbChain as

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(*) (we refer to MO as the interaction between two reactive Molecular Orbitals (MOs) m] and mn and reactive MO as m())). This reaction model matches the arrow-pushing diagram where the interaction is shown by a group of directed half arrows from one reactive MO to the other reactive MO.
Since arrow- prro-ppshiig mechanism A, uniquely defines the single transition state (see [25 by knowing the atom mapped R. atom mapped P, and A, OrbChain can uniquely determine the reactive pair of orbitals, (m²)) ml-)). in R. Also, by knowing the atom mapped It and two orbital pairs (assumed to be reactive) (ml² m²"), OrbChain can uniquely determine the atom mapped P and A.
We summarize the OrbChain in the Equation 1
(1) R.P (mi, m2): Atom mapped R, P and A, determine the reactive MOs.
OrbChain
(mL, my) P', A mechanism an interaction between a pair of MOs m, my-
(2) R
The problem of predicting the outcome of radical reaction mechanism is to find the P and A given R. Using OrbChain, this problem is equivalent to finding the reactive pair of molecular orbitals (m("), .m,)) given R.
To determine and in accordance with the model described above, the first step is for OrbChain to identify all molecular orbitals (MOs) by performing an iterative process over the set of nodes aj present in the reactant molecules RG Each MO associated with four distinct parameters m =a, (a,e, e, n, c). where a represents the atom corresponding to the MO (i.e., the central atom of the MO), e denotes the number of electrons involved in the MO (0, 1, or 2), indicates the atom adjacent to the atom
the case of a bond orbital (such as a т or a bond), and signifies the possible chain of filled or unfilled MOs (such as a system). By utilizing we can uniquely determine P and A by recursively following the electron transfers from one MO to the next.
In a reactant molecule R₁ with n; atoms, we show the set of found MOs as m, {me} }. Then the total number of possible (not plausible) mechanistic radical reactions from the reactant molecules R = { RR 1.- is approximated in Equation 2 This would be large number for molecular systems with approximately 20 atoms. Within this vast space of possible mechanistic reactions, the number of plausible ones (e.g.. (m(*) m2 m/(")) is tiny, usually less than or equal to three.
X =
(2)
2
4.2 Two-Step Prediction
To address the challenges associated with the multitude of potential mechanistic reactions, we adopt a method inspired by [20). We begin by shrinking the pool of potential mechanisms through filtering process called reactive sites identification. In this step, we filter the potentially reactive MOs, so we can only consider the interaction between the potentially reactive MOs. This filtering process offers two key advantages: (1) reduced computation time and (2) removal of false positives, leading to employ
improved precision and recall in the overall prediction. After reactive sites identification, a reaction ranker, a machine learning model that ranks the possible interactions (m, my) within the refined space of filtered MOs. The reaction ranker facilitates the identification of the most favorable orbital interactions for the specific set of reactants under examination.
4.2.1 Reactive Sites Identification
We divide orbitals into reactive and non-reactive orbitals based on their role in the reaction. Based on statement (2) in Equation there are only two reactive orbitals for each radical mechanistic step (R, P, A). Instead of predicting the reactive MOs (a alom, e, n, c), it is more convenient predict the label of atom a, which is associated with the MO as follows:
10 m & {m m (a), e,,,e,t,o) c)}
s(a)) 1 m² E {m m = et,
(3)

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Then, finding set of potentially reactive atoms in a group of reactant molecules is a classic node classification problem in the context of graph learning.
We adopt the reactive sites identification method from 20]. which represents atoms using continuous vectors based predefined atomic and graph-topological features. We expand these atom descriptors by incorporating more atomic features such as the number of radical electrons. For all the atoms within the RMechDB datasets, we extract these feature vectors to train classifier that can classify each atom into reactive or non-reactive. The parameters of the trained model and the statics of the training data can be found in the Appendix.
To improve this atom classification and circumvent the manual feature extraction part, we deploy form of graph convolution neural network (GNN) suitable for the atom classification B0 [7 augmented with attention mechanisms [31] More information on the parameters and specifications of the GNN can be found in the Appendix. Table 2 shows the statistics of the training data for the reactive sites identification part.
The results of reactive sites identification are presented in Table 2 While these methods achieve reasonable accuracies, they overlook a crucial aspect: the context of the reaction, which encompasses both spectator and reagent molecules. Given that the reactivity of various sites and functional groups can be significantly influenced by the reaction context, we will propose context-aware approaches in Sections 4.3 and 4.4 to address this limitation.
4.2.2 Plausibility Ranking
Once reactive sites are identified within a set of reactant molecules, we employ OrbChain's 's statement (2) for all combinations of reactive sites. Since each reactive sites (i.e., atoms) corresponds to an MO, this process yields all possible mechanistic reactions from the set of input reactants. Each of these mechanistic reactions includes atom-mapped products and arrow codes representing interactions between predicted MOs. To rank the proposed mechanistic reactions, we utilize deep Siamese neural network [32 designed for entity ranking. During training, this network takes a pair of plausible and implausible mechanistic reactions as input and produces real-valued number for each. The network's 's parameters are learned by minimizing the following loss function:
(4) In the given loss function, f represents a neural network, a denotes the logistic function, and Rxn represents mechanistic reaction. Minimizing this loss function allows the function f to assign higher scores to plausible reactions. To establish a relative plausibility score, we train the model using pairs of Rxnplousible and Rxnimpleusiile with identical reactants. During training, the Rxpplausible is a sample of the RMechDB dataset while the Rxhimplausible is generated using OrbChain's 's statement (2) with randomly chosen nea-reactive MOs of the same sample from RMechDB. Once the model is trained, the output of function f can be interpreted as the plausibility score. We use this output to rank all generated mechanistic steps from the combination of reactive sites.
= o (RXiptaueitte) - (RXI
The performance of the model significantly influenced by the choice of reaction representation and the corresponding neural network architecture for function f. We explore and compare four different reaction representations and their corresponding neural architectures: (1) pre-defined feature vectors 200. (2) reactionFP
B3) with three different molecular fingerprints including atom pair fingerprints (AP) 34 Morgan fingerprints [35], and Topological Torsion fingerprints (TT) [36. (3)
Differential Reaction Fingerprint (DRFP) (37. and rxnfp, bext- reaction representation based on a pre-trained model for yield prediction B8) Detailed descriptions of the methods for generating implausible reactions, the specific parameters used for each neural network, and the parameters used for each reaction fingerprint can be found in the Appendix. The comparative results of the plausibility ranking are presented in Table 3
4.3 Contrastive Learning
As stated above, the context of reaction can affect the dynamic of orbital interactions by changing the reactivity of different functional groups. An informative atom representation for reactive sites identification must take this context into account. In this section, we solve this problem by proposing a new method similar to 144 which computes and learns the atom representations by considering the entire context of the reaction. The key idea is to consider pair of atoms and predict the most
5

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reactive pair instead of predicting the reactive atoms separately. We take into account the full context of the reactions by considering all possible atom pairs and comparing them with the most reactive one in a contrastive learning manner. This contrastive learning model can approximate the probability distribution in Equation []] Notably, this method not only captures the impact of the reaction context but also streamlines the prediction process into single step, resulting in faster inference when compared to the two-step prediction method.
P (mm),,)) (mj.m²) R)
(5)
4.3.1 Atom Pairs and Atom Descriptor
First, we establish a baseline for approximating the probability mentioned above using atom descrip- tors. In this approach, the positive data consists of the most productive reactions (R, P. A)ii.. each sample of the RMechDB dataset), while the negative data includes all other possible reactions from the same set of reactants (R, P", ,'",'"'A) A'). To train the contrastive model, reactions, whether positive or negative are represented as a pair of atoms where each atom is the representative of its reactive MO. The targets Vyy for an atom pair (a,, az) is obtained as shown in Equation 3. We calculate the marginalized probability (Eqn. 5) by considering all possible atom pairs in reactants R. This approach has two advantages: (1) It enables one-step reaction prediction by identifying the most reactive pair of MOs, determining the product and arrows according Orbehain (statement (2)). (2) reduces false negatives by not discouraging less reactive, yet still plausible, MO pairs. These pairs ranked highly, with the most reactive MO pair receiving the highest ranking.
[ mi = ea,,e, & m² (ag, e', n',''c)
M),,,,, = lo Otherwise;
Figure 2 (left side) shows the schematics of the constrastive model. Table 4 presents the results of this method, while the Appendix provides details on the objective function, parameters of the contrastive model (Figure 2). and the atom descriptor.
4.3.2 Rxn-Hyyerggaph
Considering all atom pairs in a contrastive learning fashion would take the reaction context into account. However, the exn--yypegrapph Rxn- Axnn-Hypeggraph introduced in (27 is a graph attentional neural network that operates the hypergraph of the entire reaction. Instead of using the atom descriptor, Rxp-eyyggggaph model, to automatically learn a contextual representation of all atoms of the reactant sides.
compute more informative representation of atom pairs by utilizing
train
To adapt the Hypergrapp for the reaction predictor task (OrbChain Statement (2)), we modify the original structure of the hypergraph by duplicating the reactants both sides of the graph. This modification allows us to provide the reactants on one side while maintaining the full context on the other side. Following the training procedure of (27). compute atom representations and generate pairs of atoms. These pairs are then fed into the contrastive network depicted in Figure to obtain a ranked list of atom pairs. Each atom pair corresponds an interaction between two orbitals, which enables us to generate products and arrows using OrbChain. The results of the reaction predictor using atom pair prediction with Ryp-Hypeggg are presented in Table
4.4 Text Representation and Sequence to Sequence Models
Considering the SMILES string as the text representation of molccules [3 40 a chemical reaction can be seen the transformation of sequence of characters (reactants) to another (products). This makes the sequence-to-sequeee recurrent neural network architecture, a suitable predictive model for chemical reaction predictions
models, such as the Transformer 41 [2) and models based on the
43
45 28
retrosynthesis prediction [46) and molecular optimization 47
Existing text-based models for chemical reaction prediction exhibit limitations in various aspects, notably in terms interpretability and balance their predictions. SMILES representations are not unique and do not encode inherent molecular properties, such as invariance to atom permutations. Consequently, these models often require extensive data augmentation to improve performance. Nonetheless, we seck to leverage the success of these models and apply them to the prediction of

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mult
Imul
Applying Layers
Fistt Roactive Atom
Socord Reactiv Aton
Find Resetive Atom
Second Beoctiue Alom
Productive Reaction
Non Productive Reaction
One Set Reactants
Figure 2: Left: The architecture of the contrastive learning approach. Right: The schematic depiction of the Rxn-hypergraph.
radical mechanistic reactions. In particular, we adopt the pioneering text-based reaction predictor, Molecular Transformer [28], which utilizes bidirectional encoder and autoregressive decoder with a fully connected network for generating probability distributions over possible tokens. The pre-trained Molecular Transformers were trained using different variations of the USPTO dataset [16]. During training the encoder computes contextual vector representation of the reactants by performing self-attention on the masked and randomly augmented coonnancicallee SMILES string of the reactant molecules. The decoder then uses the encoder output and the right-shifted SMILES string of the products to autoregressively generate the product tokens. Since the radical reactions in RMechDB are not labeled with reactants and reagents, we used the model which was pre-trained using the USPTO_MITT __ixed dataset [16 48 49
4.5 Fine-tuning Using R.MecDDB
Molecular Transformer enables the fine-tuning of pre-trained models for downstream tasks like radical reaction prediction. In our approach, we utilize pre-trane pre-trained models and conduct reactant- to-product sequence translation. During the fine-tuning process, our only augmentation technique involved rearranging the reactant molecules within the SMILES string. Specifically, for each reaction containing N reactant molecules, we employed N SMILES strings with reactants randomly reordered. We removed all the atom mappings and arrow codes from the R.MccDBB training data and fine-tuned the model using the augmented training set of the RMechDB.
shows the performance of the text-based prediction for both pre-trained and fine-tuned versions of the Molecular Transformer. We also include detailed information on training and fine-tuning parameters, well as tokenization statistics.
5 Results and Discussion
5.1 Performance on RMechDB
We assess the performance of the two-step prediction method, comprising reactive sites identification and plausibility ranking. The top N' accuracy of the reactive sites identification on the combined test datasets of RMechDB are presented in Table 2 We observe that GNN models outperform the method based on the atom descriptor (predefined feature extraction). This behavior is expected as the atom descriptor is limited three). However, the number of GNN layers can be optimized to construct the most informative atom representations. The advantage of GNNs is more evident for the atmospheric test data where there are usually more molecules present in the context of the reaction.
certain radius around the atom (in this case the radius is set to
The second model of the two-step prediction is the Siamese architecture that ranks the reactions based on their chemical plausibility. Four different architectures were used for different reaction representations: Pre-defined feature vectors from 20 reactionFP, DRFP, and the ranfp. The results of the top N accuracy for the combined test sets RMechDB are shown in Table The table shows that DRFP outperforms the reactionFP and the feature extraction methods. We believe the reason is

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mainly because of the different nature and underlying chemistry of the radical mechanistic reaction and the USPTO reaction, which was used to pre-train the rxnfp.
Table 2:
The performance of different methods for reactive sites identification. Each number represents the percentage of reactions for which both reactive atoms are identified within the top N predictions.
Method
Top2
Top3
Top5
Top10
Atom Descriptor
75.1
81.5
89.3
96.7
GNN
76,9
83.6
92.1
97.9
To predict the outcome of a mechanistic radical reaction using the two-step method, we must perform both reactive sites identification and reaction ranking. Given the performance of each individual predictor, the best combination would be the GNN and DRFP. Therefore in Table g we use this combination to compare the performance of the other two methods with the two-step prediction.
Table 3:
The performance of different methods for plausibility ranking. Each number represents the percentage of reactions for which the correct mechanism predicted in the top N plausible reactions.
Method
Top1
Top3
Top5
Top10
Pre-defined Feature Vectors
73.1
79.2
88.3
96.3
AP
74.6
82.3
90.2
97.8
reactionFP
Morgan2
74.3
81.9
90.0
97.3
TT
74.3
82.4
90.0
97.8
DRFP rxmfp (pretrained)
78.6
90.2
95.1
100.0
75.9
86.2
94.3
97.9
As it is shown in Table 4 the contrastive learning approach yields the most accurate prediction across all the metrics. In terms of inference time, the contrastive learning approach is faster than the two-step prediction as it only consists of one neural network. This becomes crucial in the case of pathway search where we exponentially expand the tree of the mechanistic pathways by predictions. However, the advantage of using the two-step method becomes more evident when the size of the reactant molecules increases. In Figure 3 we show the performance of all three methods based on the number of heavy atoms the reactant sides. As expected, the prediction of both the contrastive method and text-based models becomes more faulty when big molecules react. This can be explained using the fact that the reactive sites identification part of the two-step method is mainly dependent local neighborhood of atoms and is not significantly affected by the size of the molecules.
The text-based models are outperformed by the other two graph-based methods. Although they offer faster inference times, they yield less accurate predictions. This poor performance mainly because Molecular Transformer model is trained on the USPTO MIT mixed dataset. Therefore, it learned to predict the only major product of overall transformations that are mostly polar. On the contrary, RMcchDB data are balanced, mechanistic, and involve radical species. Align with this low performance, according to Table 4 fine-tuning on the relatively small RMechDB dataset results in a slight decrease in the performance. This be attributed to the dissimilarities between RMechDB and the USPTO dataset. RMechDB comprises intermediate products of transformations and includes radical reactions with compounds not found in the USPTO dataset. During the tokenization process of the RMchhDB reactions, new tokens emerge that have not been extracted from the USPTO dataset.
We also provided separate performance of the predictors with respect to different radical reaction categories provided in RMechDB.
5.2 Pathway Search
After successfully predicting the outcomes of mechanistic radical reactions, we can chain these predictions to construct mechanistic pathways. Starting from a set of reactant molecules, we perform series of predictions by using each of the top N predicted products as the reactants for the next prediction. This would form a tree structure with the starting reactants as the root. This tree can be expanded to a desired depth, representing numerous mechanistic pathways leading to different
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Table 4: top N accuracy of all the three reaction prediction models on Core and Atmospheric test sets of RMechDB. For the text- based models, a prediction is considered to be correct, when at least one of the non-spectator product molecules predicted correctly. For the text-based models, we use (p) and (f) for pre-trained, and fine-tuned models respectively.
Core
Atmospheric
Time
Model
Variant
top N
top N
1
2
5
10
Two-step Best Combination
62.4
71.9
93.2
97.2
60.4
70.9
91.6
96.3
1.38
Atom Descriptor
62.9
73.8
94.2
96.5
61.0
73.6
93.0
94.4
0.08
Contrastive
RxnHypergraph
64.3
74.1
95.1
97.4
62.1
74.8
94.1
95.9
1.45
Pretrained
58.2
64.3
84.2
91.0
58.0
67.3
82.6
91.0
1.30
Text-based
Fine-tuned
57.7
64.0
83.9
90.4
57.1
66.8
82.2
90.3
1.30
Recovered Reactions Per Radical Category
Recovery Rate vs Reactant Size
Reactions
Two-step
Contrastive
Text-bared
Perfect Prediction
Twoostup
Con
Teot-bassed
NUMBER REACTIONS
0.91
0.89
1:52
80
T:R1
0:F1
0.5
0:62
33
3
05
0.4
REACTANTS SIZE
Homdlsso
Recombrintton
Adeibio
Abstrection
Resorince
10
20
30
40
50
Figure 3: Left: The number of recovered reactions in the top 5 for different methods and different classes of RMechDB reactions. Right: The recovery rate of different methods with respect to the size of the reactants.
products. These pathways are crucial for identifying synthetic routes, exploring intermediate products, and aiding in mass spectrometry analysis with broad applications to various fields, including drug design and drug degradation.
We create dataset consisting of 100 radical reactants paired with target molecules that are expected to be formed from the reaction. Each pair is accompanied by a specified depth, indicating the length of the mechanistic chain reactions required to reach the target. We generate this dataset by simulating atmospheric conditions, taking inspiration from the atmospheric reactions observed in the RMechDB dataset and atmospheric literature [50]. The reactants in our dataset primarily consist of Isoprene, a prevalent atmospheric compound, along with other atmospheric molecules like radical Oxygen and Hydroxyl radical. To find these targets, we expand and search the tree of the mechanistic pathways by employing breadth-firg search algorithm [5]]. To expand the tree for each of the 100 reactants, we use the top 10 predictions at each step (i.e., the breadth of 10 to expand the tree), and we expand the tree up to the given depth. Given the average inference time of the predictive models presented in Table we used the fastest method with interpretable predictions which is the contrastive model with atom descriptor. Upon running this pathway search for these 100 pathways, recovery rate of 60% meaning that for the 60% of the reactants, the given target was found within the expanded tree. These pathways along with their targets and detailed information the pathway search are presented in the Appendix.
observed significant
5.3 RMechRP: Online Reaction Predictor
We developed RMechRP as an online tool for mechanistic radical reaction prediction. RMechRP, which stands for Radical Mechanistic Reaction Predictor, is accessible at ucii wuii.duu//ee edw/mmechrp/ It offers two interfaces: (1) Single-step Predictor; and (2) Pathway Predictor. These interfaces equipped with the highest performing combination of the two-step prediction
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models and the contrastive learning model respectively to offer the best blend of interpretability. accuracy, and fast inference. The Single-Step Predictor interface, accessible at http::/deeprxx ics. ais.uci.oo wwci.eduurraeeeeeee httpp///mmeccpppsggeessseeeeeeee molecules. By specifying a few parameters, such as the number of reactive atoms, the system utilizes the best combination of the two-step prediction model described above to identify all possible radical mechanistic steps. Then all the predicted reactions will be ranked and displayed with side information, including arrow codes, SMIRKS, reactive orbitals, and plausibility scores. The Pathway Predictor interface, accessible at htttttp//eeppp for searching specific target molecule(s) within the tree of mechanistic pathways rooted at the imput reactants. Users have the option to imput a set of reactants and specify a set of target molecules, which can be provided in the form of molecular structures molecular masses. Parameters such as the depth and breadth of the mechanistic pathway tree can be configured. The system then employs the contrastive learning model described above to expand and explore the mechanistic pathway tree. Upon finding the target molecule within the expanded tree, the system will present synthetic pathways from the initial reactants to the target molecule molecules. More details on both interfaces and how to use them are presented in the Appendix.
provides users with the capability to input set of reactant
ics uci edu/ wwt.hm//ppttaaaaaaraeaaaaaaaaaaa is designed
6 Conclusion
We developed and compared multiple radical reaction predictors operating at the mechanistic level using the RMechDB database of radical elementary reaction as the source of training and evaluation data. We show that the contrastive learning approach incorporated with the graph representation of chemical structures would yield the highest accuracy. The models that merely build upon the text representation of chemical structures require a larger dataset to achieve the same level of accuracy. We show that our approach to reaction prediction capable of providing chemical interpretability down to the interaction of molecular orbitals. It also offers pathway interpretability by breaking down an overall transformation into balanced pathways of elementary steps. Considering these chemically balanced pathways would identify interesting byproducts that can potentially give rise to pathways that are usually ignored in representing overall transformation. Additionally, we developed online interfaces for performing single-step and pathway prediction of radical reactions. Our radical reaction predictor is publicly available through an online interface at wtthtp:// //tpp//eeee..../////e ics uci edu/ edu/rmechrp)
Finally, important to identify the limitations of the current method and note that the radical reaction predictor can be further extended in several aspects. First, although RMechDB provides high- quality mechanistic reaction data, it only includes 5500 mechanistic reactions. Developing purely data-driven machine learning models such LLMs, with minimum dependency on physical and chemical information requires much larger datasets. Additionally, RMechDB is focused on textbook reactions and atmospheric reactions. This focus may restrict the applicability of the proposed predictor to other types of radical reactions, such as polymerization processes. In the context of pathway search and the expansion of the search tree, employed straightforward breadth-first search algorithm. This algorithm allows us to expand and search the entire tree of pathways, however, the exponentially growing computations would impede the search process. An interesting improvement could involve the adoption of more sophisticated search methods, like those mentioned in [52], which are not only faster but also capable of exploring significant portion of the search space.
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7 Appendix
In this appendix, we provide a comprchensive description of the experimental details and environments in which the experiments were conducted. Additionally, we present detailed information and data pertaining to the pathway search. Furthermore, we offer an explanation of the various interfaces of the RMechRP software, which serves the pioneering online radical reaction predictor. Each section this appendix corresponds to the section with the same title in the main article. Finally, all the experiments conducted using a single NVidia Titan X GPU.
7.1 Reactive Sites Identification
For the Atom Fingerprint model, we constructed a fingerprint of length 800 for each atom. This fingerprint includes 700 graph topological features explained in [20] and 85 atomic features including a one-hot vector for atom type, and chemical features of the atoms such as valance and electronega- tivity. The graph topological features are extracted using a neighborhood of size three. The extracted fingerprints are fed into fully connected model with an output layer for binary classification. For the GNN model, we used the atomic feature for the initial representations of atoms. The model consists of four GNN layers with output layer for binary classification.
Combining both training sets presented in RMechDB (25), we extracted over 51000 atoms train each of the models above. Both models are evaluated using combination of two test sets in RMechDB and the top N accuracy of models is reported in Table 2 of the main article. Table represents the parameters used for training the models.
Table 5: The parameters used for training the models for reactive sites identification.
Model
Batch Size
Num Layers
Layers Dim
Act GELU ReLU
Reg Lz(5e-5)
Num Att Heads
Atom Fingerprint
32
3
512-256-1 64-64-64-1
GNN
32
4
Dropout (0.3)
6
7.2 Plausibility Ranking
For the plausibility ranking experiments, used the following four methods for representing chemical reactions:
Feature Extraction:
We use the same features explained in 20 which results in extracting a vector
of length 3200 for each reaction.
reactionfp: We use the RDKit 53 implementation of reactionfp B3
For all three fingerprint types fingerprint of size 2048, with bit ratio of
(Atom Pair, Morgan2, and Topological Torsions), 0.2. We considered nonagent molecules with a weight of 0.4 and agent molecules with weight of 1.0.
DRFP: We use the DRFP fingerprint [37 with size of 2048 with a min and max radius zero and four, while including the hydrogen atoms and rings.
rxnfp: We use the default tokenizer and pretrained model for the rxnfp 38 which results in finger- prints of length 256.
combination of both training sets in RMechDB. For each sample of the training data (productive reaction), we generate (at most) 40 negative samples (unproductive reactions) by randomly sampling molecular orbitals other than the reactive MOs (mi m2). This results in data set of over 185000 pairs of productive and unproductive reactions. To train the plausibility rankers for each method, we use the parameters explained in Table
For training,
7.3 Atom Pairs and Atom Descriptor
For the contrastive learning method using atom descriptors, we use the same atomic feature and graph topological features above to represent one single atom. Specifically, for the graph topological features, we use the neighborhood of size one. These features plus the atomic features result in
14

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Table The parameters used for training the models for the plausibility ranking.
Model
Batch Size
Num Layers
Layers Dim
Act
Reg
Feature Extraction
32
3
512-256-1
GELU
Dropout (0.5)
reactionfp
32
3
400-200-1
GELU
Dropout (0.5)
DRFP
32
400-200-1
GELU
Dropout (0.5)
rxnfp
64
128-1
GELU Dropout (0.5)
vector of length 140 for atom representation. Using these vectors, we train contrastive model
depicted in Figure 2 (left) of the main article. The objective function to train this contrastive model is
as follows:
L = 1- a (1((1) x (((5)) (f(a)) x a((ğ))
Score [(seaa x g(a2)))
(8)
Where aj and az are the atoms of the reactive MOs m and m.. while af are randomly chosen atoms. Both f and g functions are characterized by a fully connected neural network. The first reactive atoms in both productive and unproductive reactions are fed through the same network f. and similarly, the second reactive atoms are fed through the same network g. The outputs of both f and are single real-valued numbers, which, when multiplied together, yield score for the respective reaction. These scores are then utilized to construct the objective function, aiming to maximize the score the productive reaction compared to the unproductive reactions using the same reactant set. Once the model trained, the normalized score (Equation S can interpreted as the reactivity of an atom pair.
We use a combination of both training sets in RMechDB train f and g. For each productive reaction, we form unproductive reactions by considering at most 15 samples of (a1, az). (ai, d4), and (a', a²)) This negative sampling results dataset of over 200000 pairs of productive and unproductive atom pairs. We use this training dataset to minimize the objective function 7
Both f and g have similar architectures that consist of three fully connected layers with GELU activation function and a dropout with a rate of 0.5 applied to all layers. The dimensions of the layers 1 128, 64, 1.
7.4 Rxn-Hyyergraph
We use the Rxn-Hypergraph to replace form atom descriptors that are extracted automatically for minimizing the objective function After processing the Rxn-hypergraph for N layers, the generated atom descriptors are used in the same setting above for the same minimization objective. Here in Table 7 we describe the parameters we use for training the Rxn-Hyyergraph.
7.5 Text Representation and Sequence to Sequence Models
In order develop text-based radical reaction predictor, we utilize the pre-trained molecular transformer which was trained using the USPTO_MI m_ixmmixee dataset. We also used the tok- enizer developed by the molecular transformer. This tokenizer yields 523 distinct tokens for the USPTO MIT mixed dataset. There are nine tokens from the RMechDB dataset that do not match the 573 tokens of the USPTO. Therefore, we used the "unknown token" to represent these nine tokens.
For fine-tuning the pre-trained model, we used the combination of both RMechDB training sets. We fine-tune the model using simple data augmentation described in Section 4.5 for 10 epochs. Finally, for the evaluation of the text-based models, we considered all the generated "unknown token" as correct tokens.
Table 7: The parameters used for training the Rxn- Rxn-Hypergraph for the contrastive model.
Batch Size
Num Layers
Layers Dim
Act GELU
Reg (z2(5-55)
Num Att Heads
Learning Rate
32
5
all 64
6
0.001
15

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7.6 Pathway Search
In the Pathway Search section, we conducted an experiment involving the execution of the pathway search for set of specific reactants. Each of these reactants was associated with a desired target molecule, which was expected to be found within the mechanistic pathway tree. Additionally, a set of distinct parameters such the context, depth, and breadth is associated with each reactant.
To provide detailed information and facilitate reprodueibility, we have included supplementary materials accompanying this work. The full list of these reactants used for the pathway search is accessible at
This list contains the reactants, corresponding targets, the provided context (if any), and the anticipated depth at which the target molecule is expected to appear within the mechanistic pathway tree. For each entry of this list, include visualization of the identified pathways leading to the specified target molecules. It presents the discovered pathways that were found during the experiment. These materials serve to provide comprehensive insights into the pathway search process and its outcomes, enabling readers to reproduce and further explore the obtained results.
ics. uci edt/rmechhpppathaaae
7.7 RMechRP: Online Reaction Predictor
In addition to the methods and results presented in the main article, we have developed an online web server that enables users to utilize the trained models for predicting the outcomes of mechanistic radical reactions with the highest levels of interpretability of the outcome. R.McchRP (Radical Mechanistic Reaction Predictor) accessible via the anonymized link http: http::// //deprx/e ics. uci edu/rmechrp RMechRP offers two interfaces: Single-step prediction and Pathway search.
Single-Step Predictor predicts the outcome of mechanistic reaction with a single transition state. Users have the option to either input the reactants in written form or draw them using a drawing tool provided on the web server. Additionally, users can specify the reaction conditions, with the current option being standard temperature and pressure. The number of reactive molecular orbitals (MOs) to be considered can also be specified by the user.
To ensure flexibility, users can choose to filter out reactions that violate specific chemical rules, such as Bredt's rule [54]. Once the input and conditions are set, the user can click the predict button. The system will then run the two-step prediction model, as described above, to generate and rank the potential products. These predicted products will be displayed, accompanied by additional information such as arrow codes, reactive MOs, and the mass of the products. The single-step predictor accessible via the anonymized link http: bttp://deeprxnn ics. uci, edu/ sdu/rmechry singlestep Figure 4 shows the single-step interface and the displayed predictions for a simple reaction.
kear in rndtrraae de and vociee andiaanbeeeee subie sake tu mucils wll derlly raeracie
sis posble nacion..
Figure 4: The single-step interface with the predictions of simple reaction, Left: the input panel. Right: the table displaying the ranked predictions.
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Pathway Predictor forms the tree of the mechanistic pathways to given depth and breadth. Users have the option to either input the reactants written form or draw them using a drawing tool provided on the web server. Users must also input set of targets (either mass or chemical structure) look for within the expanded tree of the mechanistic pathways. users have the ability to provide context for the reactions. The context consists of a set of molecules along with their corresponding frequencies appearance within the mechanistic pathway tree. When a molccule from the context is consumed in reaction, the system can automatically reintroduce that molecule back into the pathway tree. The frequency of appearance indicates how many times a molecule can be added mechanistic pathway tree.
condiiora risiio,
Targo mnlvrades of stringa mepoas, sepenteed by rommeee
Rraction conteot
2928 Water
-0.0
Figure 5: The pathway search interface.
In addition to the context, there are several additional parameters that can be specified by the user. These parameters include:
Depth Pathway Search: Users can define the depth of the pathway search, which determines how many reaction steps will be explored in the mechanistic pathway tree.
Breadth (Branching Factor) of Pathway Search: This parameter controls the branching factor of the pathway search, influencing the number of alternative reaction pathways that will be considered.
Application of Chemistry Rules: Users have the option apply certain chemistry rules during the pathway search. These rules can be used to filter out reactions that violate specific chemical principles or constraints.
Score Threshold: Users can set a threshold value to consider only reactions with scores higher than the specified threshold. This helps narrow down the focus more favorable promising reactions.
These additional parameters allow users to customize their pathway search and refine the results based on their specific requirements and preferences. By leveraging these features, users can gain deeper insights into the mechanistic pathways and explore wider range of possible reaction outcomes. The pathway search interface is accessible via the anonymized link attt:://eepps ics uci.edu amechrp/pathrar Figure shows the pathway interface and the required parameters.
17


Reviews:
[{"review_id": "QKAbEv0QEH", "reviewer": "Reviewer_n7xM", "summary": "Authors present two models that predicts radical chemistry reactions. The first model 'OrbChain' is comprised of two components 1) one GNN model for predicting pairs of reacting atoms/groups, 2) a model which ranks the plausibility of these pairs. The second model is a a fine-tuned Rxn-Hypergraph model, adapted for the task of predicting radical mechanism by using an atom classifier model.\n\n", "questions": "- Could you provide more information on the train test split used in the RMechDB? Are there any splits that are used to measure generalizability of predictions to out of domain reactions (e.g. by reaction type, atom types, structure similarity)\n\n- Since interpretability is one of the benefits highlighted by this modeling approach, it would be interesting to see more examples of mechanism prediction by the proposed model in the main text.\n\n- For the Pathway Search task, the results say that 60% of the reactants were found in the expanded reaction trees (of 10 step mechanisms). How well do current non-ML techniques perform on this task? How many of the proposed reactants are false positives; are false positive predictions of reactants detrimental to the problem prediction?\n\n- What is the final model used in the RMechRP Software?", "limitations": "I do not identify any negative societal implications.\n\nBy my understanding, the presented model is intended to be limited for only radical based reactions, as it is trained on this domain of reactions, and not for other types of chemical reactions.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "+ Selects an interesting problem domain, specifically radical based chemistry.\n+ Authors plan to open source the Radical Mechanistic Prediction model and release software for easier use.\n+ Compares against relevant baselines, such as fingerprint representations, MolecularTransformer.", "weaknesses": "The presentation of results could be more clear. In particular:\n- It would be helpful to have a clear statement of the key contributions provided by this work. The list of desiderata provided at the end of section 2 are important, but I believe that these properties have already been provided by previous models, especially references [20, 21] for radical reactions.\n- If I understand correctly, OrbChain is the name of the two part model, but components of the model are still used for the second modeling approach using the fine tuned Rxn-Hypergraph model.\n- The table formatting makes the results somewhat difficult to parse, it would be helpful to have more spacing between the caption and the table, and for the \n- Table 3, where there is one column with 'AP \\n Morgan2 \\n TT' and it wasn't immediately obvious that these are different molecular descriptors.\n-  For Figure 3, I believe the reaction type should be 'Homolysis' rather than homolyze. \n- For Figure 3, It would be helpful to have a sense of the number of reactions in each class to better compare the relative performance by the model between reaction classes.\n- There are several typos in the manuscript, e.g. a missing close parenthesis in lines 48-49 of page 2, 'weather' instead of 'whether' on line 188 on page 5, some tense mismatches. Please review for grammar errors.\n\n\nIn Section 2, authors state that 'None of the currrent reaction predictors can offer ... chemical interpretability, pathway interpretability, or balanced  atom mapping'. There are actually several models that provide interpretability for reaction mechanisms/reaction type. In addition to the works on radical mechanism prediction cited by the authors as references 20 and 21:\n\n- In https://arxiv.org/pdf/1805.10970.pdf, Bradshaw et al. predict electron pair pushing mechansims with a generative model.\n- The MolecularTransformer model has also been shown to provide atom mapping by visualizing attention weights (https://arxiv.org/pdf/2012.06051.pdf, Figure 2)./\n\nFor text based models such as Molecular Transformer, could you quantify the percentage of reaction predictions that suffer from a 'balance problem'? It isn't clear to me that this is a big issue with MolecularTransformer or other text based models.\n\n\n\n\n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the comments from the reviewer. We have addressed most of their comments in the revised version of the manuscript.\n\n**Comments on the weaknesses**\n* We agree that references [20, 21] followed a similar approach toward reaction prediction. Nonetheless, our reaction predictor marks a substantial advancement across various facets, including training data, reaction modeling, and machine learning techniques. Notably, while [20, 21] present radical predictors built upon basic machine learning models trained on a limited dataset comprising only 96 radical reactions. These references cannot provide chemical and pathway interpretability.\n* It is important to note that OrbChain is not a prediction method. As explained in Section 4.1, OrbChain is the name of the method we developed to model a radical mechanistic reaction based on idealized molecular orbitals. Using OrbChain as a tool for modeling mechanistic reactions, we are able to develop the first two prediction methods called: two-step prediction and Contrastive learning.\n* Within the revised manuscript, we have fixed the spacing between the caption and the table to improve readability.\n* Within the revised manuscript, we have added information on AP, Morgan2, and TT used for the reactionFP method in Section 4.2.2.\n* About Figure 3, the reviewer is correct regarding the name of the reaction type. Within the revised manuscript, we have changed “Homolyze” to “Homolysis”.\n* In Figure 3, the number of reactions in each class is shown by the blue bar which is labeled “All Reactions”.\n* In addition to all the typos mentioned by the reviewer, we have fixed all the remaining typos in both the main manuscript and the Appendix.\n* In Section 2, where we delve into the advantages of our proposed reaction prediction method, we introduced the concepts of \"chemical interpretability\" and \"pathway interpretability.\" Based on our definitions, existing reaction prediction models fall short of delivering these two benefits. This deficiency arises because these models are either not trained with orbital level information (for chemical interpretability) or lack training on mechanistic reaction data (for pathway interpretability). Although the current reaction prediction models can offer interpretability within the context of machine learning (e.g., attention weights), it's important to distinguish this from the chemical and pathway interpretability as per our definitions. The third advantage of our reaction prediction model lies in its ability to maintain balance. The concern of balance preservation is seldom addressed in models trained on USPTO data, given the inherent imbalance of the data source. Therefore, the qualitative assessment of the reviewer that the balance effect “is not a big issue” lacks substantiating evidence. Conversely, when applying a reaction predictor to specific real-world scenarios like drug degradation and mass spectrometry, preserving the balance is an essential consideration. Since our reaction predictor is capable of predicting balanced reactions, it has the potential to be applied to a range of problems where the use of current reaction prediction models is not practical.\n\n\n**Answers to the questions**\n* The RMechDB article (reference 25) released an online platform of radical mechanistic reactions with splits into train and test sets. Quoting from the RMechDB paper, “the reaction data are carefully split into train and test data where the test set replicates the distribution of the reaction type and reactive orbitals of the training data”. Also, RMechDB data (both train and test sets) are extracted from the radical chemistry textbooks and research articles on atmospheric chemistry. Therefore, there is no test set to explicitly measure the generalization capability of the model for out-of-domain reactions.\n* We agree that the description of the pathway search experiment is too succinct, especially as we believe that the ability to do pathway searches is one of the main contributions of this work. To address this point, we have now updated the manuscript by including a better explanation of the pathway searches. Within the revised Appendix, we also include the complete list of 100 pathways tested together with the information on whether the intended target was recovered or not  (60% of the time). In addition, we are adding a one-page pdf that contains an example of a pathway search, with a complete visualization of the reaction tree leading to the target product. \n* The final model used in the RMechRP software is the best combination of the two-step prediction method to provide the orbital and pathway interpretability. Within the revised draft, we have added this information to Section 5.3.\n"}, {"review_id": "5QHBuX7pPN", "reviewer": "Reviewer_URj3", "summary": "- a new model is described for prediction of radical chemical reactions\n- the model is trained on a dedicated database of radical reactions for atmospheric chemistry, an important application\n- several, reasonable baselines are evaluated", "questions": "no questions, this is a solid, straightforward paper in my opinion", "limitations": "n/a", "rating": 7, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "- reasonable, state of the art ML modelling (contrastive learning, attention GNNs, reasonable reaction representations inspired from molecular orbitals, building on previous work by Baldi's group)\n- reasonable strong baselines (transformers)\n- compelling results\n- important application", "weaknesses": "- other baselines, like MEGAN https://pubs.acs.org/doi/abs/10.1021/acs.jcim.1c00537 or https://www.nature.com/articles/s42256-022-00526-z could be considered \n\n\n### Related work \nSeveral references in the introduction are not correct:\n\nThe Cao & Kipf MolGAN paper should be removed, because it does not deal with chemical reactions.\nsimilarly, the Rogers et al ECFP does not deal with reaction prediction, and should be removed in the intro.\n\nOn the other hand, the Segler et al paper should be cited as an ML paper.\n\nThe ELECTRO paper by Bradshaw et al should be added. https://arxiv.org/abs/1805.10970\n\ncontrastive learning to distinguish between plausible and implausible reactions has already been used in https://www.nature.com/articles/nature25978 (called in-scope filter there), which should be referenced as well", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the fair comments from the reviewer and the fact that they acknowledged the importance of this work. We agree with all the suggested changes. We have revised our manuscript by implementing all the comments from this reviewer. Specifically, within the revised draft, we have removed the Cao & Kipf MolGAN and Rogers et al (ECFP) papers from the introduction. In addition, we have added the two suggested references (Bradshaw et al. 2018 and Siegler et al. 2018) to the introduction. Furthermore, we also cite Segler et al. again in Section 4.3 which is on contrastive learning. "}, {"review_id": "1ZxyFvUGiy", "reviewer": "Reviewer_PKyZ", "summary": "The authors provide a reaction predictor system that provides an accurate and interpretable prediction of radication reactions. Due to the lack of training data, there is a dearth of reaction predictors for radication reactions. The authors present 3 deep-learning-based approaches. The first approach is a two-step process that identifies possible reactive sites and then ranks the reactive site pairs. The second approach uses a contrastive learning approach to identify the most reactive site pairs. Finally, the authors also show a transformer-based approach to perform sequence-to-sequence translation from products to reactants. In the two-step, OrbChain approach, the authors present a GNN-based approach to identify reactive sites and a siamese network-based approach to rank the plausible reactive sites. Multiple reaction representations to perform plausibility ranking. The contrastive learning approach also uses a GNN and both a custom atom pair representation and a hypergraph representation are evaluated. Finally, a pre-trained MolGPT on USPTO dataset is used as well. The authors find that the graph-based methods outperform MolGPT and the contrastive learning methods yield the most accurate results. \n", "questions": "- Line 100: How is the arrow-pushing mechanism A represented in OrbChain?\n- In Table 2, what is the Atom Fingerprint method? Morgan fingerprint? ECFP?\n- What is the loss function for the contrastive learning approach? (Might be in the appendix)\n- Link 334: contrstive -> contrastive?\n", "limitations": "- Limited comparison as the authors don’t include a related works section so it is difficult to contextualize the scope of their current work to the field.\n- Is there a reason the MolGPT model could not be trained on the new RMechDB dataset for evaluation rather than only fine-tuning?", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The authors compare multiple models to show the efficacy of different types of models such as GNNs and text-based Transformers for reaction prediction\n- The authors also use multiple representations and model architectures for a very thorough evaluation of the proposed reaction \n", "weaknesses": "- It is not clear how or which of the three algorithms described is used in RMechRP. \n- The presentation of the paper could be improved. There are 3 approaches described with multiple models and representations for some approaches. A short summary of the findings and comparisons or a visualization of the approaches could significantly improve the presentation\n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the comments from the reviewer. We have addressed most of their comments in the revised version of our paper.\n\n**Comments on the weaknesses**\n* We agree that it is not obvious which model was used for RMechRP. The model used is the best combination of the two-step prediction method (as shown in Table 4), which provides the best compromise between speed, accuracy, and chemical interpretability. We have clarified this point in the revised version in Section 5.3. \nWe agree with the reviewer that the number of models and representations might be confusing. To clarify this point, we have prepared a new figure that we have added to the revised manuscript (see also attached pdf).\n\n**Answers to the questions**\n* The arrow-pushing mechanism is represented using the numbers associated with the atoms and bonds involved in the arrow-pushing mechanism. It is best to illustrate this with an example. \nReaction SMIRKS: CC(C)[O:10][N+:20]([O-])=O.[Ar]>>CC(C)[O:10].[O-][N+:20]=O.[Ar]\nArrow codes: 10,20-10;10,20-20\nThe arrow codes above are representing two arrows (separated by ‘;’). The first arrow starts at the bond between atom 10 and atom 20 and ends at atom 10. The second arrow starts at the bond between atom 10 and atom 20 and ends at atom 20. This information is already explained in the original RMechDB article (reference 25 in the manuscript) which we now cite also in line 100 to clarify this point.\n* Within Table 2, Atom Fingerprints refers to the method that is described in line 138. To avoid any confusion with other kinds of fingerprints, we have changed the name of this method to Atom Descriptor at line 138 and we have revised Table 2 accordingly.\n* The loss function of the contrastive learning approach is provided in line 48 of the appendix (Equation 1). For even greater clarity, we have also added this equation to Section 4.3.1 of the revised main manuscript.\n* In addition to the typo in line 334, we have fixed all the remaining typos in the main manuscript and the appendix in the revised version.\n\n**Comments on the limitations**\n* As the reviewers noticed, we merged the related work and introduction into one section where we reviewed the other predictors. As stated in the second paragraph of that section, most of the reaction predictors are operating at the level of multi-step transformations, not at the level of single mechanistic steps. To the best of our knowledge, the only other reaction predictor focused on predicting mechanistic steps is the one by Bradshaw et. al. 2018, which however is focused on non-radical reactions. So, our proposed reaction predictor is the only predictor with the scope of predicting radical reactions at the mechanistic level. \n* Other text-based models can be trained on the RMechDB data. However, it is essential to realize that RMechDB consists of a relatively small set of 5500 mechanistic reactions. Large language models (e.g., MolGPT) require a much larger number of training samples. While we did try to train an LLM from scratch, we found that it did not work well and thus adopted the more practical strategy of pretraining the LLM on the larger US PTO dataset, followed by fine-tuning it on the RMechDB dataset.\n"}, {"review_id": "DQ5rUW7mE3", "reviewer": "Reviewer_G4db", "summary": "The paper proposes a set of methods for modeling chemical reactions that involve radicals during the reaction process. The authors first introduce the current landscape of chemical reaction datasets based primarily on the USPTO and discusses the USPTO shortcomings in terms of reaction interpretability and its inability to showcase reaction involving multiple steps. Next the authors briefly describe the RMechDB dataset, which does contain pathways with radicals, followed by a description of their methods. The methods are based on OrbChain, which provides a standardized way of describing chemical reactions with radical and arrow pathways. After that, the authors introduce their predictive methods, including two-step prediction, plausibility ranking, contrastive learning with a reaction hypergraph and text-based sequence to sequence models. The authors conduct experiments for all the aforementioned methods to further understand their capability in accurately describing radical reaction pathways on the RMechDB dataset. The performances of the different methods vary across different settings of the conducted experiments. The authors then provide a pathway search example, further description of their package and a conclusion. ", "questions": "* Could you clarify if the RMechDB paper provided any modeling methods?\n* Would it be possible to provide more context for the results, ideally in the figure and tables themselves, to further understand the experiments? How good does a Top1, Top2, Top5 score need to be practically useful, for example?\n* Is it possible to have text-based methods also express the intermediate steps in reactions involving radicals? Why or why not?", "limitations": "The authors do not provide a detailed discussion on limitations. A discussion on limitations would make the paper stronger.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The paper provides has the following strengths:\n* Originality: The papers provides a new perspective on chemical reaction modeling that involves radicals and is also more interpretable for classically trained chemists. \n* Quality: The paper describes and analyzes four different and relevant methods for the radical modeling problem and provides clear motivations for their importance.\n* Clarity: The paper motivates the problem they address quite and describe the necessary background.\n* Significance: Expanding the capabilities of machine learning models to provide more interpretable reaction models with more steps in the reaction process could have significant impact on various chemistry related problems.", "weaknesses": "The paper could be further improved:\n* Providing a clearer description of the context of the results related to original problem the authors motivated. How well do the described methods provide more interpretability to chemical reaction modeling? How do the metrics the authors measure relate to that original premise? [quality, clarity, siginificance]\n* The authors only briefly describe pathway search, but provide little context for what their results mean. What does a recovery rate of 60% imply? How does the reaction tree look like and how interpretable is it? [clarity]\n* The authors refer the reader to the appendix very often, which I think contains a lot of significant information needed to fully understand the experimental results. I recommend putting more of that information in the main paper. [quality, clarity significance]\n* The authors only provide a brief description of the RMechDB dataset and its unclear if that paper had any modeling methods the authors could compare their proposed methods to. Further clarification on this would be helpful. [clarity]", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the constructive comments from the reviewer. We have addressed most of their comments in the revised version of the manuscript.\n\n**Comments on weaknesses**\n* We agree that the description of the pathway search experiment is too succinct, especially as we believe that the ability to do pathway searches is one of the main contributions of this work. To address this point, we have now updated the manuscript by including a better explanation of the pathway searches. Within the revised Appendix, we also include the complete list of 100 pathways tested together with the information on whether the intended target was recovered or not  (60% of the time). In addition, we are adding a one-page pdf that contains an example of a pathway search, with a complete visualization of the reaction tree leading to the target product. \n\n**Answers to the questions**\n* The RMechDB article (reference 25) introduces a data set with a corresponding database and web server for radical mechanistic reaction steps. It does not involve any reaction modeling or prediction.  However, as a service to the machine learning community, RMechDB comes with a standard train-test data split that can be used by anyone to compare different methods. We have added this information to Section 3 of the revised manuscript.\n* All the tables report the top N metrics (e.g. N=1, N=2, N=5, N=10) for the corresponding prediction in particular for single-step prediction. These are the standard metrics in the field (Schwaller, Philippe, et al 2019, Coley, Connor W., et al 2017, Irwin, Ross, et al. 2022, Tu et al 2023). As an intuitive example, the significance of the top N metric for single-step prediction (Table 4) is best understood in the context of the pathway (sequence of single steps) search problem. For instance, with a pathway search of length 3, a top 5 metric of 90% results in a 73%=(90%)^3 probability of recovering the target with a tree with a branching factor of 5 provided the pathways considered are congruent with the training set. Additional results are presented in Figure 3 where we assess the robustness of the predictors with respect to reaction type (left) and reactant size (right) using the top-5 metric, which are often important considerations in practical applications. For instance, atmospheric reaction predictions require a predictor to be robust with respect to the six radical reaction types described in the paper, as all six types occur frequently in the atmosphere. We have clarified all these points in the revised version. \n* Regarding the reviewer’s question in the third bullet above, the short answer is yes, with a caveat regarding the size of the training datasets. The RMechDB dataset consists of 5500 reactions only, which is in general too small for training a large text model from scratch. However, as stated in Section 4.4, we are able to use a pre-trained model (pre-trained on the US PTO dataset) and fine-tune it using the RMechDB dataset. This Molecular Transformer (Schwaller, Philippe, et al 2019)  approach is capable of predicting radical mechanistic reactions and provides intermediate byproducts. However, the existing text-based models do not predict orbital interactions (i.e. arrow codes). Furthermore, the pretraining is done on the reactions in the US PTO data set which are often non-balanced and report only the major product, resulting in biased predictions. \n\n**Comments on limitations**\n* We agree with the reviewer that the limitations of our work are not sufficiently addressed.  In the revised version of the manuscript, we have added a section discussing the limitations of our reaction predictor. In particular, we itemize the limitations in the following three categories:\n1. Limited training data: RMechDB, as the first public database of radical mechanistic reactions, includes only 5500 mechanistic reactions.  Although these reactions are carefully curated, developing more advanced machine learning models such as LLMs typically requires datasets that are several orders of magnitude larger. \n2. Limited range of radical chemistry: RMechDB, is focused on textbook reactions and atmospheric phenomena. This focus may affect the generalization capability of the proposed predictor in completely different areas that involve radical chemistry. \n3. Limited search methods: Here, we used breadth-first search to search and expand the pathway tree of reactions. Although this approach guarantees the exploration of all the possibilities within the reaction tree, more intelligent search methods [Agostinelli, Forest, et al. 2021] may be capable of speeding up pathway searches.  \n"}]

Author rebuttal:
This one-page pdf file includes three figures that are generated to improve the clarity of the paper and also to provide a better response to the reviewers' comments.

Meta-review: Radical chemistry is an exciting application area for reaction prediction models due to its focus on elementary steps, understanding of which is an achilles heel of currently used deep networks. As agreed by most reviewers, the paper has solid modeling and establishes useful benchmark results for the task. The main weakness of the paper is its lack of clarity. In particular, it is challenging to understand that OrbChain model is not actually a neural network. Its also challenging to draw meaningful conclusions from the multi-step results section. All in all, I am happy to recommend acceptance. I would like to ask the Authors to try to further improve clarity in the camera ready version.
Decision: Accept (poster)