Interpreting Molecule Generative Models for Interactive Molecule Discovery
Authors: Yuanqi Du
Xian Liu
Shengchao Liu
Bolei Zhou
Venue: ICLR 2022 Submitted (2022)
Keywords: Molecule Generation
Controllable Molecule Generation
Interpretable Molecule Generation
Molecule Manipulation

Abstract: Discovering novel molecules with desired properties is crucial for advancing drug discovery and chemical science. Recently deep generative models can synthesize new molecules by sampling random vectors from latent space and then decoding them to a molecule structure. However, through the feedforward generation pipeline, it is difficult to reveal the underlying connections between latent space and molecular properties as well as customize the output molecule with desired properties. In this work, we develop a simple yet effective method to interpret the latent space of the learned generative models with various molecular properties for more interactive molecule generation and discovery. This method, called Molecular Space Explorer (MolSpacE), is model-agnostic and can work with any pre-trained molecule generative models in an off-the-shelf manner. It first identifies latent directions that govern certain molecular properties via the property separation hyperplane and then moves molecules along the directions for smooth change of molecular structures and properties. This method achieves interactive molecule discovery through identifying interpretable and steerable concepts that emerge in the representations of generative models. Experiments show that MolSpacE can manipulate the output molecule toward desired properties with high success. We further quantify and compare the interpretability of multiple state-of-the-art molecule generative models. An interface and a demo video are developed to illustrate the promising application of interactive molecule discovery.

Under review as a conference paper at ICLR 2022
INTERPRETING MOLECULE GENERATIVE MODELS FOR INTERACTIVE MOLECULE DISCOVERY
Anonymous authors Paper under double-blind review
ABSTRACT
Discovering novel molecules with desired properties is crucial for advancing drug discovery and chemical science. Recently deep generative models can propose new molecules by sampling random vectors from latent space and then decod- ing them to a molecule structure. However, through the feedforward generation pipeline, it is difficult reveal the underlying connections between latent space and molecular properties as well as customize the output molecule with desired properties. In this work, develop simple yet effective method interpret the latent space the learned generative models with various molecular proper- ties for more interactive molecule generation and discovery. This method, called Molecular Space Explorer (MolSpacE), is model-agnostic and can work with any pre-trained molecule generative models in off-the-shelf manner. It first identifies latent directions that govern certain molecular properties via the prop- erty separation hyperplane, and then moves molecules along the directions for smooth change of molecular structures and properties. This method achieves in- teractive molecule discovery through identifying interpretable and steerable con- cepts that emerge in the representations of generative models. Experiments show that MolSpacE can manipulate the output molecule toward desired properties with high success. We further quantify and compare the interpretability of multiple state-of-ther of-tt---rtt-ttttt art molecule generative models. An interface and a demo video are developed to illustrate the promising application of interactive molecule discovery.
INTRODUCTION
Designing molecules with desired properties is a fundamental problem in chemistry, which has a variety applications in drug discovery and material science (Chen et al., 2018). Traditional pipelines require exhaustive human efforts and domain knowledge, which are difficult to scale up. Recent studies exploit deep generative models to solve this problem by encoding molecules into a latent space, from which random samples drawn and decoded to novel molecules (Walters & Barzilay, 2020). It has been widely observed that such deep molecule generative models are able to facilitate the design and development of drugs and materials from many perspectives (Lopez al., 2020: Munchez-Lengelinn
& Aspuru-Guzik, 2018).
Despite the promising results of deep generative models for molecule generation, much less effort has been made to interpret the learned representations. Most of the existing models are based on deep neural networks, which are known molecule generation domain, many attempts have been made to improve the interpretability of deep learning models from various aspects, e.g., representation space (Zhou al., 2016), model space (Guo et al., 2021), and latent space (Shen et al., 2020; Shen & Zhou, 2021). In molecule generation domain, interpretability can be studied in two ways: (1) the interpretation of learned latent space where steering the value of latent vectors could lead to smooth and continuous molecular property change and (2) the interpretation of molecular space that adjusting the molecular property could observe smooth structure change of molecules.
short on interpretability (Samek et al., 2019). Outside of
In addition, remains challenging to generate molecules with desired properties. Previous works and searching basedd methods to
mostly rely optimization- based, reinforcement achieve property control of the generated molecules (Shi et al., 2020; Jin et al., 8a). Specifically, reinforcement learning-hased based algorithm (You al., 201 8a) equips the model with rewards designed

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to encourage the molecule generative models to generate molecules with specific molecular prop- erties. Optimization-ban based algorithm takes advantage of the leartt latent space by molecule gen- erative models and optimize the molecular properties via Bayesian Optimization (Liu et al., 2018). Searching- -aaee algorithm instead searches directly from the chemical space for molecules with op- timal properties (Kwon al., 2021). However, these lines of work are designed for molecule gener- ation with optimized property and thus unable to change the property monotonically and smoothly. Besides, current methods are confined real-world applications in drug discovery and material science. For example, existing work only discover a limited set of molecular properties, such as penalized logP (octanol- water partition coef- ficient), QED Druug-likeness) DRD2 activity, etc (Jin et al., 201 8a; Shi et al., 2020; Liu et al., 2018; Fu et al., 2020). Consequently, when molecules with new properties are needed, the models must be re-trained with a different optimization goal, which is significantly time-consuming.
limited number of molecular properties, which hinders
To tackle the above challenges, we formulate new task, molecule manipulation, which aims to improve the interpretability and steerability of given molecule generative model via continuously manipulating molecular properties. Based on the observation that molecules sharing similar struc- tures/properties tend to cluster in the latent space, develop MolSpace Explorer, a model-agnostic method to manipulate molecules with continuous changes of molecular properties. Specifically, MolSpace Explorer first identifies the property separation hyperplane which defines the boundary for molecular properties (e.g., drug-like or drug-unlike) in the latent molecular space learned by a given generative model. Based on the property separation hyperplane, we estimate the latent di- rections that govern molecular properties, which are in turn used to enable continuous change of the molecular structures and properties without re-training the given molecular generative model. To the best our knowledge, this work is one of the earliest attempts to achieve interactive molecule discovery through the steering of pretrained generative models.
The experiments demonstrate that our method can effectively quantify the interpretability and steer- ability of state-of-the-ard molecule generative models. To measure the ability of generative models in interpreting molecular properties and generating molecules with continuous property control, we design a new evaluation metric named success rate, which evaluates the percentage of success- ful manipulations with continuous property-changin molecules over manipulations of group of molecules. To visualize our method and facilitate interactive molecule discovery for scientists, we develop an interactive system with visualization of real-time molecule manipulations and smooth molecular structure/property changes. Our main contributions are summarized as follows:
We formulate molecule manipulation, a new task which measures the interpretability and steerability of molecule generative models via the ability to manipulate the molecular prop- erties of molecules in the latent space.
We develop a simple yet effective model-agnostic method named MolSpace Explorer for molecule manipulation, which further analyzes current molecule generative models in terms of their interpretability and steerability.terpretttoo
Comprehensive experiments demonstrate the effectiveness of our method in quantifying the interpretability and steerability of various molecule generative models. An interactive system is developed for real-time molecule manipulation.
RELATED WORK
Molecule Generation. Recent studies have explored variety of deep generative models for molecule generation. Specifically, GrammarVAE (Kusner et al., 2017) designs a variational autoencoder-basse model that represents molecules SMILE strings. With the advancement of graph neural networks (GNN), a surge of GNN-based generative models have been proposed to tackle the problem, by combining GNN with variational autoencoders (VAEs), generative adversar- ial networks (GANs), normalizing flows, energy-based models (EBMs), and reinforcement learn- ing (Olivecrona et al., 2017: De Cao Kipf, 2018; Jin et al., 2018a; Zhou et al., 2019; Madhawa et al., 2019; Shi et al., 2020; Luo et al., 2021; ; Liu et al., 2021; Yang al., 2021). To be specific, JT-VAE (Jin et al., 2018a) 8a) proposes a VAE-based architecture to encode both atomic graphs and structural graphs for efficient molecule generation. MolGAN (De Cao & Kipf, 2018) exploits GANs for molecule generation, where discriminators are used to encourage the model to generate realistic and chemically-vali molecules. MRNN (Popova al., 2019) extends the idea of GraphRNN (You
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et al., 2018b) to formulate molecule generation as auto-regressive process. GCPN (You et al., 80) formulates the molecule generation process as a reinforcement learning problem where obtains molccule step by step by connecting atoms and reward is used for controllable genera- tion. GraphNVP (Madhawa et al., 2019) first introduces normalizing flows for molecule generation, where the generation process is invertible. Later work improve the flow-based models via auto- regressive generation (Shi ct al., 2020), valency correction (Zang & Wang, 2020), and discrete latent representation (Luo al., 2021). GraphEBM (Liu et al., 2021) introduces energy-based models which models the density of molecule data.
Controllable Molecule Generation. Another key point for molecule generation is controllable generation where the generated molecules are expected to possess certain properties. Early work (Segler et al., 2018) bias on the distribution of the data and fine-tune the generative mod- els with known desired properties to generate molecules with desired properties. The recent work mainly leverage optimization-bin based (Shi et al., 2020; You et al., 2a; Hoffman et al., 2020; Win- ter et al., 2019;?), reinforcement learning-based (Zang & Wang. 2020; Jin et al., 2018a; Blaschke et al., 2020), and searching-based (Brown et al., 2019; Yang et al., 2020; Kwon et al., 2021) ap- proaches to generate molecules with desired properties. Optimization- based methods are quite flex- ible and can both directly work on the molecules (Renz et al., 2019; Fu et al., 2020; Xie et al.. 2021; Maziarz et al., 2021) and work on the learnt latent vectors of the molecules et al., 2018; Jin et al., 201 8b; Winter et al., 2019; Griffiths & Hernández-Lobato, 2020; Notin et al., 2021). Reinforcement learning-basse -baed methods usually formulates controllable generation as a se- quential decision-making problem and requires score-function to provide rewards to the agent. Searching-based approaches (Brown et al., 2019; Yang et al., 2020; Kwon et al., 2021) are also capable of searching molecules with optimized properties. Besides, few work (Chenthamarakshan et al., 2020; Das et al., 2021) leverage the learnt latent space and achieve controllable generation by accepting/rejeccing sampled molecules based on molecular property predictor. Despite the ability to generate molecules with optimized properties, it is challenging for existing methods to interpret the generation process and cannot generate molecules with monotonically and smoothly changing molecular properties.
3 PRELIMINARIES
Molecule Graph. Molecules can be presented as graphs X = (V,E,,,F, where V denotes a set of N vertices (i.e., atoms), S V x V denotes set of edges (i.e.. bonds), F 10 denotes the node features (i.e., atom types) and E E {0, 1 N denotes the edge features (i.e., bond types). The number of atom types and bond types are denoted by D and K, respectively.
Deep Molecule Generative Models. In molecule generation, a generative model M encodes the molecular graph X as a latent vector Z E R' with / being the dimension of the latent space, and is capable of decoding any latent vector back to the molecular space. Specifically, variational auto- encoder (VAE) (Kingma & Welling, 2013) and flow-based model (Flow) (Rezende & Mohamed, 2015) are the two most commonly used models for molecule generation tasks. Both of them encode the data from molecular space to latent space, which is usually modeled as Gaussian distribution; then they decode the latent code back to molecular space. They can be formulated as:
f(z),
z' 922),
where x and a' are the ground-trudt truth and reconstructeddssmplll data respectively, and E Z repre- sents latent vector in the latent space.
4 PROBLEM FORMULATION OF MOLECULE MANIPULATION
To improve the steerability and interpretability of molecule generative models, we propose a new research task, molecule manipulation, which interprets the generative model and steers the properties of the output molecules. To be specific, a deep generative model contains a generator g: Z + X, where Z R' stands for the I-dimensional latent space, which is commonly assumed to be Gaussian distribution (Kingma& & Welling, 2013; Rezende & Mohamed, 2015). There exist property functions fr which define the property space P via P (X(X..
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Identify Separation Boundary
drug-like mols
drug-unlike
drug-unlike mois
Generate
Predict
drug-like mols
separationboundary
2. NO, I) Latent Space
gz)(z)
p
ff(((x)
Structure/Molecular Space
Property Space
Figure 1: MolSpacE framework: (1) the tested molecule generative model generates novel molecules by sampling random vector from the latent space and then feeding it into the generator, (2) off- the-shelf property prediction function is used predict molecular properties from the molecular space, (3) MolSpacE: identifies latent directions which govern molecular properties via the property separation hyperplane.
Formulation. The input to molecule manipulation is list of n molecules X = 22, and a list of m molecular properties
{p1. P2. ... " Pm.) We aim to manipulate one or more molecular properties of given molecule in k consecutive steps and output the manipulated molecules with properties p' = (p(1), p(2), p ((()). By manipulating the given molecule, we can observe the alignment of Z + X + P, where the relationship between Z and X' explains the latent space of molecule generative models. The relationship between X' and P reveals the correlations between molecular structures and properties. By traversing latent space, we can generate molecules with continuous structure/property changes.
Evaluation. For the molecule manipulation task, we design two new evaluation metrics named success rate (SR) and soft success rate (SSR) that measure the performance in discovering latent molecular property directions. To be specific, we consider manipulation be successful only if we generate molecules with monotonically-changin properties in a consecutive k steps of manipu- lation, follows:
Pucceess (I, = 1[ e sk), fx("")) fo (z((t)))) s 20,
where fp is a property function which calculates certain molecular property and (1), (++1) repre- sents molecules generated in two adjacent steps. As monotonicity is rather strict, we propose more flexible definition of success, namely soft success, as follows:
Øsoft success (I, k) 1 E [k), fp(()) ffp--fpaaaaa (o((i)))) s <..
where e predefined tolerance threshold that weakens the monotonicity requirement. To extend the evaluation metric for [P] molecular properties and X candidate molecules to manipulate, calculate the overall SR as:
SR(P, X,K) EpEP,EEX L[dracoess (Ip, k) A (SD(I,, A (D k)]
(4)
x X
where Vp represents manipulating property p of molecule x which results in a manipulation path Tp ( E [k]). Since the molecular space is essentially discrete, allow the model to generate duplicate molecules during manipulation, but the model has to generate at least one dis- tinct molecule from the base molecule (diversity DIV) and the structure difference (SD) enforces monotonically-derre -ecreasing structure similarity along the manipulation sequence, as follows:
ΦDIv k) 1[3 E [k], s.l., (0) # so()))
(5)
ksp(a, 1 i (k), (0)
(1+() ((11) 0),
(6)
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Structure Similarity
640
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0.56
100
073
641
641
627
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Molecule Cluster
(b) Interpolation
Figure 2: (a) Molecule clusters in the latent space, the number represents structure similarity (Bajusz al., 2015), where the red box represents the base molecule, and y axes denote two random orthogonal directions to manipulate. (b) Linear interpolation two (top and bottom) molecules.
where å measures the structure similarity between the two input molecules. Similarly, provide soft structure difference (SSD) with a predefined tolerance threshold ry as follows:
ΦSSD (x. k) 1 E [k], s.t., ,.t.,,(x..)))) 2(11) 5(x (1)+)))x)1)))))))))))))))))))) 7/.
Finally, we calculate the overall SSR as:
SSR(P, EpePiRex 1lgsoft success (I k) A @SSD(Ip, k) A @DIV k)
(8)
P| x X
MOLECULE MANIPULATION THROUGH MOLSPACE
5.1 LATENT CLUSTER ASSUMPTION
We examine the property of latent space learned by the generative models and have the following observations, (1) molecules with similar structures tend to cluster in the latent space, (2) inter- polating molecules 2 and Ø2, represented by latent vectors 2 and 22, intermediate molecules whose structures gradually change from æ to 22. As molecular structures determine molecular properties (Seybold et al., 1987), the observations imply that molecules with similar molecular properties cluster together and interpolating two molecules could lead to gradual changes in structure. As shown in Fig. 1, there may exist two groups of molecules, drug-like and unnkeu where each group cluster together and linear interpolating two latent vectors with one molecule from each group could lead direction that crosses the separation boundary. These observations also match the analysis from the prior work (Zang & Wang, 2020). To verify our as- sumption, we visualize the latent space of the pre-trained MoFlow model trained on QM9 dataset in Fig. 2. The left figure shows that molecules that are close in the latent space are similar in struc- tures. The right figure shows that interpolating two molecules the latent space could lead to smooth structure changes.
lead list of
5.2 IDENTIFYING LATENT DIRECTIONS
Latent Separation Boundary. With the verifications above and the previous work of analyzing the latent space generative models (Shen et al., 2020; Bau et al., 2017; Jahanian et al., 2019; Plumerault et al., 2020), assume that there exists separation boundary which separates groups of molecules (e.g., drug-like and drug-unlike) and the normal vector of the separation boundary defines latent direction (in Fig. 1). When moves toward and crossing the boundary, the molecular
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properties change accordingly (e.g.. from drug-unlike to drug-like). A perfect separation boundary would have molecules with different properties well separated on different sides. From that, we can find hyperplane for each molecular property with unit normal vector n E RR, such that the distance from any sample 2 to the hyperplane as:
d(2,n) = n²
(9)
Latent Direction. In the latent space, the molecular structure and property change continuously towards the new property class when moves towards the hyperplane and vice versa, where assume linear dependency between and
fr(g(z)) = a d(z,n).
(10)
where fp is a property function and a is a degree scalar that scales the changes along that corre- sponding direction. Extending the method to multiple molecular properties manipulation, we have:
fp(g(=)) AN 2,
(11)
where A Diag(as, "..."........................... ,am) is the diagonal matrix with linear coefficients for each of the m molecular properties and = [M1,... nm represents normal vectors for the separation boundaries of m molecular properties. We have the molecular properties /' following a multivariate normal distribution via:
Pp E(AN"2) AN" O, Pp E(AN DE N=² = AN B(==))NAA NA
(12)
(13)
We have all disentangled molecular properties in P if and only if Ep is diagonal matrix and all directions in N are orthogonal with each other. Nevertheless, not all molecular properties are purely disentangled with each other. In that case, molecular properties can correlate with each other and n ny is used to denote the entanglement between the i-th and j-th molecular properties in P.
5.3 IMPLEMENTING THE MOLECULE MANIPULATION
After we find the separation boundary and identify the latent direction, to manipulate the generated molecules with desired properties, we first move from latent vector z along the direction with degree scalar a, and the new latent vector is
2 + an
(14)
To this end, the expected property of the new manipulated molecule is
fr(g(2 + am)) fr(g(2)) ka
(15)
where k is scaling factor between molecular vector space and property. Based on our assumption to find a separation boundary for each molecular property, we utilize linear Support Vector Ma- chine (SVM) (Cortes & Vapnik, 1995) to find the separation boundaries which best separate the two classes the data. For each molecular property, we train an individual linear SVM from a group of randomly sampled latent vectors and utilize property function fP calculate the corresponding molecular properties. Then, we find the separation boundaries for each molecular property. The nor- mal vectors N of separation boundaries are finally utilized identified latent directions that govern the molecular properties. Additionally, our method is highly efficient thanks to leveraging shallow models compared optimization-base methods which require re-training of deep models for each of the specific molecular properties.
6 EXPERIMENTS
6.1 EXPERIMENT SETUP
Datasets. We use three molecule datasets, QM9 (Ramakrishnan et al., 2014), ZINC250K (Irwin & Shoichet, 2005), and ChEMBL (Mendez et al., 2019). QM9 contains 134k small organic molecules with up to 9 heavy atoms (C, O, N. F). ZINC a free database of commercially-availan pounds for drug discovery. On average, the molecules in ZINC are bigger (~23 heavy atoms)
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and structurally more complex than QM9, We take sampled 250K molecules version (Gómez- Bombarelli et al., 2018) from the larger database. ChEMBL is a manually curated database of bioactive molecules with drug-like properties and contains 1.8 million molecules.
Baselines. We include two baseline methods of identifying latent direction that governs the molecu- lar property for comparisons. Random manipulation randomly samples latent directions for molec- ular properties. Largest range manipulation draws latent vectors from the training set and defines the directions via calculating the direction between one molecule with the largest property score and another molecule with the smallest property score for each molecular property.
Implementation Details. We take the publicly available pre-trained models from the GitHub Repos- itory of HierVAE, and MoFlow, respectively. For CGVAE, we take the code implementation from the public GitHub Repository and train using default hyper-parameters provided by the authors. We utilize the SVM implementation from Scikit-Learn'
6.2 EVALUATION PROTOCOLS
Pre-trained Models. We apply MolSpacE, as well as baselines, three state-of-the-art molecule generative models with publicly available pre-trained models or code implementations. Specifically, CGVAE (Liu et al., 2018) implements VAE-based model and generate molecular graphs in sequential manner; Vie (Jin et al., 2020) embeds molecular structure motifs into a hierarchical VAE-based generative model; MoFlow (Zang & Wang, 2020) designs normalizing flow-based model which learns an invertible mapping between input molecules and latent vectors.
Molecular Properties. We study molecular properties identified in the chemistry community through open-source cheminformatics software". In total, we analyze 154 molecular properties from multiple dimensions, including distributions, inter-correlations, etc. Details can be found in Appendix B. Due to the page limit, we mainly report results for 7 molecular properties, including very common yet important ones, drug-likkness (QED), molecular weight (MolWt), partition co- efficient (LogP), and 4 randomly selected ones, including topological indexes, BalabanJ, BertzCT, and BCUT2D descriptors (Bayada et al., 1999) CHGHI, CHGLO.
Quantitatively, we evaluate the ability of the model to manipulate the given molecular property of molecules with the proposed strict success rafe and success rate metries (see Sec. 4). Qualitatively, we visualize molecule manipulation including property distribution shift during manipulation, single and multiple property manipulations.
6.3 QUANTITATIVE EVALUATION OF MOLECULE MANIPULATION
In Table 1, we report the quantitative evaluation results for molecule manipulation with both strict success rate and success rate, which are evaluated 154 molecular properties. According to the table, we can obtain the following insights.
(1) Our proposed method, MolSpacE, the first attempt for molecule manipulation, achieves excel- lent performance to manipulate properties of molecules with three state-of-the-an molecule genera- tive models. For some important molecular properties (e.g., QED), (with HierVAB) achieve 68% manipulation success rate in QM9 dataset. We outperform the baseline methods 80% on average.
(2) Generally, HierVAE performs the best in all three datasets. We conjecture this is because Hi- erVAE models not only atomic level but also structural level information, which is critical in de- termining molecular properties. MoFlow ranks second due to the invertible constraint which maps molecular space to latent space that reflects the property space. CGVAE underperforms on three datasets. One possible explanation its simple architecture which struggles to map molecular space to semantically meaningful latent space, where the molecular property is not well-reflected.
(3) CGVAE has the largest gap between SSR and SR, which demonstrates that CGVAE tends to repeatedly sample molecules with the same properties during manipulation, and this is possible when the latent space does not well separate molecules via properties. The SSR and SR gaps in MoFlow and Hier VAE are similar and ~ 25% smaller than QM9. For individual molecular properties,
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Table 1: Quantitative Evaluation of Molecule Manipulation over variety of molecular properties (numbers reported are strict success rate / success rate in %. -R denotes model with random manip- ulation, -L denotes model with largest range manipulation. -M denotes model with MolSpacE. The best performances compared to baseline models are bold and the best performances across models in the same dataset are marked as red).
Datasets
Models
Avg.
QED
MW
LogP
Balabani
BertaCT
CHGH
CHGLO
COVAE-R R
1.75/16.11
0.70/0.70
1.50/ L,50
0,90/ 1,10
1 1660
1,50/ 1,50
0.70/0.30 0.70
1.J0/ 1.10
COVAE-L
1.1488211
0.000.000
0/0//:40
0.00/0000
0.20/0.20
0.20/0.20
0.10/0.10
COVAE-C
CGVAE-M
7.98/57.9
12,00
11.20 12.10
11.10 11.30
16,30/ 18.40
11.40/ 11.80
14,10/ 14.10
10.30 10.30/10.50
QM9
MoFlow-R
10.40/47.97
4.4074,40
13,0/1 13.60
9.30/9.50
20,30/ 22.50
7.30/ 7.30
660/9.60
9.10/
MoPlow-1.
0.35/0.41
1.20/1.20
0.00/0.00
4.10/4.10
0.00/0.00
0.001 0.000
0.00/000
0.00/0.00
MoFlow-M
16.2//11.8
39.90 39.90
44.10 44.20
40.40 40.40
21,00/ 27.50
30.90 32.20
29.10 29.10
37.00 37.00/37.00
HiesVAE-R
0.19/116 1.16
0.10/0.10
0.00
0.10
0.30/0.30
0.30/ 0.30
0.40 0.40
0.20/
HierVAE-L
0.24/0.89
0.10/0.10
0.20 0.20
0.00:0.00
0,10/0,10
0,30 0.30
0.00 0.00
0
HirYVAEMM
27.30/68. /8.884
50,20/50.00
48.50 51,20
53.80 54,70
49,60 50.60
53.40/ 54,80
55,60 55.60
53.50/ 53,50
CGVAE-R
0.72/6.18
0.10/0.10
0.10
0.10/0.10
0.00/0.00
0.20/ 0.20
0.10/0.10
0.30 0.30
CGVAE-L
0.49/2.94
0..0
0.00 0.00
0.00/0.00
0.00/0.00
0.00/0.00
0.10/
0.10/0.10
CGVAF-
CGVAEM
0.50
ZINC
MoFlow-R
2.96/ 18.00
0.90
0.80
1.50/1.50
1,90/2,00
2.00/ 2.00
0.50/ 0.60
0,60 0.66
MaFlow-L L
0.03
0.03/0.11
00.00
0.00 0000
0.00:0.00
0.00/0.00
0.00/0..0
0.00 / 0.00
0,00 0.00
MoFlow-M
9.61 24.43
21.80 /2180
25,00 25.10
28.50 29,00
16.80/21.J8
21.70/ 22,00
19.30 19.30
19.20 19.20
HieVVAE-R
0.45/1.26
0.50/0.50
0,400
HizrVAE-1.
0.90/0.00
0.50
1,10/ 1.10
1.00/ 1.00
0.70/ 0.70
HierVAE-M
19.40 / 42.67
30,80 / 31.90
32.60 33.90
27.00 27.50
35.00 35.90
27,80 28.10
35.30 35.30
32.20 32.20
HierVAE-R
0.13/0.93
0.100
0.007
0,10/0
0.30
CBEMBL
HiezVAE-L
0.36/
0.20 00.00
0.50 0.50
0.10/0.10
0 0.00
0.30/ 0.30
0.00/ 0.00
0.00 0.00
HierVA-MM
19.59/ /2.38
17.40 17.50
28.30 28.40
22.50 / 22.80
15,40 15.60
25.00 25.20
23,70 23.70
24.80 24.80
can observe that the gaps between SSR and SR are small, while the average gap is large, we find that the gaps are large in categorical properties shown in Appendix D.3 Table and 8.
(4) The baseline (random manipulation) method sometimes "finds" directions that control molecular properties. As shown in Fig. 2, the molecules are well-clustered in the latent space with respect to structures that determine molecular properties (Seybold et al., 1987). However, the largest range manipulation works worse possibly due to its strong assumption in determining the direction via the two extreme (largest property and smallest property) molecules in the dataset.
0.8
Dersity
5.5
0.20
0.4
0.3
2-
0.10
0.2
0.1
0.0
0.1
0.2
0.3
0.4 0FD
0.5
0.6
0.7
0.00
105
110
115
120
130
135
-3
-2
-1
1
MolWt
Moll.ngP
Figure 3: Visualization of Molecular property distribution shift while manipulating molecules with MoFlow on QM9 dataset (0 denotes the randomly sampled base molecule and +- and -r denote manipulation directions and steps).
6.4 QUALITATIVE EVALUATION MOLECULE MANIPULATION AND INTERPRETATION
In Fig. I1, we visualize the property distributions of QED, MolWt and LogP along a 7-step manip- ulation path. For each step, we draw a property distribution. The candidate molecules and we attempt to manipulate the molecular property to the left (lower) and the right (higher). From the figure, we can clearly observe that the property distribution shifts the left and right accord- ingly when we manipulate the molecule to the left and right. For example, when we manipulate the molecules three steps to the left, the range of QED shifts from [o, 0.7] [0, 0.5]; when the molecules are manipulated three steps to the right, there are much more molecules that have QED > 0.5 than the base distribution. Similar trends can also be seen for MolWt and LogP properties.
place
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048
039
030
84
046
948
0.0
051
BA5
2
X
1X
X
MolWI
12.22
12h28
127.99
13012
13012
9839
1007
Log?
4.04
0.79
6.70
1.77
MoFlow
HierVAB
Figure 4: Manipulating QED. MolWt and LogP properties of sampled molecules. The backbone
model is CGVAF trained on QM9 dataset.
Single Property Manipulation. To qualitatively evalu- ate the performance of our method for molecule manip- ulation, we randomly select the successful manipulation paths from all three generative models in Fig. 4. The fig- ures have shown that our method succeeds in learning interpretable and steerable directions. For example, for HierVAR Fig. 4, we can find that gradually increasing LogP of molecule may lead the removal of the heavy atoms O and N from the structure. With respect to QED, the molecule drops double bonds, well as heavy N and O atoms, when increasing QED for the HierVAE model. similar trend can be observed in the MoFlow model that increasing QED drops double bonds and O atoms on the left of Fig. 4.
QUED bu4r Legh-11
QED-0.00
QUDD Lagi
QEDO
LogPosse5
LegPo.300
Lagh Ouii
QET
Figure 5: Manipulating QED and LogP properties of sampled molecules simul- taneously with MoFlow model trained on QM9 dataset (the repeated molecules are removed for better visualization).
Multi-Propente Manipulation. When it comes to multi- property manipulation, the goal is to control multiple molecular properties of given molecule at the same time. In Fig. 5, we show how our method can manipulate multiple molecular properties. For simplicity, remove the duplicate molecules and only leave the different molecules during the manipulation. From the figure, we can observe that there are some correlations between LogP and QED since when we increase QED, LogP also increases accordingly. However, it is always the case as moving the molecules to the right in the second row does not increase the QED scores. One potential reason is that the chemical space is vast, discrete and complex, and nontrivial to manipulate multiple properties of a molecule at the same time.
7 CONCLUSION
In this paper, we formulate new task, molecule manipulation which allows interacting with molecule generative models, interpreting molecular space and generating molecules with smooth and monotonic property control. We present MolSpacE, a simple yet effective model-agnostic molecule manipulation method that achieves a high success controlling and interpreting variety of molecular properties. The in- terface illustrates the promising application of interac- tive molecule discovery. In the future, plan to study how molecule manipulation can be done with unsuper- vised discovery.
MolSpaace Explorer
Figure 6: A Real-time Interactive System Interface. Please refer to Appendix demo video for interactive molecule discovery.
Reproducibility Statement. To increase the repro- ducibility of our paper, we provide detailed experiment setups section 6.1 including datasets, baselines, im- plementation details. Additional experiments be

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found in Appendix C and D. We will put link to code in the comment when the discussion fo- rum open as suggested by the Author Guide. The designed interactive system along with a demo video to demonstrate how to use the system is shown in Appendix E.
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Appendix for "Interpreting Molecule Generative Models for Interactive Molecule Discovery"
A MOLECULE GENERATIVE MODELS
In Table summarize a list of representative molecule generative models, which span various types of deep generative models, including the type of generative models, the type of generation process and whether latent space is learned. We also provide the formulation for two types of deep generative models (VAE and Flow) in Section A that are very popular for molecule generation task.
Table 2: A summary mainstream molecule generative models.
Prior Work
Generative Model
Sequential
Latent Space
JT-VAE (Jin et al.. 2018a)
VAE
CGVAB (Liu et al., 2018)
VAE
MRNN (Popova al., 2019)
RNN
GraphNVP (Madhawa et al., 2019)
Flow
GCPN (You et al., 2018a) 8a)
RL
GraphAF (Shi et al., 2020)
Flow
MoFlow (Zang & Wang, 2020)
Flow
HierVAE (Jin al., 2020)
VAE
GraphEBM (Liu et al., 2021)
EBM
GraphDF (Luo et al., 2021)
Flow
A.1 MOLECULE GENERATIVE MODEL FORMULATION
VAE gets a lower bound (ELBO) for the data log probability by introducing proposal distribution.
p(r) = log / p(xl=)p(2)dz
(16)
log [p<===]][[([[[]]] KL(g(s\=)lp<<))
Flow. The key of Flow model is to design invertible function with the following nice property:
20 po (=0)
2, = f((2-1)
2 = (2s)
(17)
df.
P:-1(1; det df. dz,
pT(2,) Pi- det dz,
where fi is invertible function. To be more concrete, we can take 2g as some tractable noise distri- bution, like Gaussian distribution, and repeating this for K steps will lead to the data distribution, ie.::
=ボ JK sK-1 o... o f1(2))
Thus, the log likelihood the data as follows:
log p(x) log Pk (=K)
log PK 1IPPPKI-K--- log det df x
džk - dfK-1
det dfk dzx
=logpk-((K--)) log del dz
log
(18)
K
log det dfr
log (20) - -
d
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B STUDY OF MOLECULAR PROPERTIES
List of Molecular Properties. In total, study 154 molecular properties from the open chemistry library RDKit", They are MaxEStatelndex, MinEStateIndex, MaxAAssEStatennde, MinAbsES- lateIndex. ged, MolWt, HeavyAtomMollWt ExactMolWt, MaxPartialCharge, MinPartialCharge, MaxAbsPartialCharge MinAAbsaarialCharge. FpDensityMorganl, FpDensityMorgan2, FpDensi- tyMorgan3, MWHI, MWLOW, CHGHI, CHGLO, LOGPHI, LOGPLOW, MRHI, MRLOW, Bal- abanJ, BertzCT, ChiO, ChiOn, ChiOv, Chil, Chiln, Chily, Chi2n, Chi2v, Chi3n, Chi3v, Chi4n, Chi4v, Hall Ipc, Kappal, Kappa2. Kappa3, LabuteASA, VSA13, VSA14, TPSA, ES- tatel, EState EState2, EState3, EState4, EState5, EState6, EState7, EState8, EState9, Frac- tionCSP3, MolLogP. MolMR, Num Vlleeeelnnellsttsonns, NumRadiccalElectron HcavyAtomCount, NHOHCount, NOCount, Num.AliphaticCattoeyyyl Num-LipphaicHeeeeocyyylss NumAliphati- cRings, NumArromatiCCathoeyehe NumaAromaiicHeetencyylle,, Num.rromaticRing, NumHAc- ceptors, NumHDonors, NumHettrootooms, NumRotatableBonds, NunsSauratealCarhoyylle,,, Num- SaturatedHeetrocyylees NumSaturatedKinng, RingCount, noTerr, ArN, N, NH, COO2, noCOO, S, HOCCN, Imine, NHO, NHI, NH2, Ndealkylation1, Ndealkylation2, Nhpyrrole, SH, aldehyde, car- bamate, halide, oxid, amide, amidine, aniline, methyl, azide, azo, barbitur, benzene, benzodiazepine, bicyclic, diazo, dihydropyridine, epoxide, ether, furan, guanido, halogen, hdrzine, hdrzone, imida- zole, imide, isocyan, isothiocyan, ketone, Topliss, lactam, lactone, methoxy, morpholine, nitrile, nitro, arom, nonortho, nitroso, oxazole, oxime, hydroxylation, phenol, noOrthoHbond, acid, piper- dine, piperzine, priamide, prisulfonamd, pyridine, quarN, sulfide, sulfonamd, sulfone, acetylene, tetrazole, thiazole, thiocyan, thiophene, alkane, urea.
Among the studied 154 molecular properties, 59 of them are continuous: MaxEStateIndex, MinES- tateIndex, MaxAbsESStatelndex, MinAAssEStatelndex, qed, MolWt. HeavyAtomMolWWi Exact- MolWo, MaxPartialCharge, MinPartialCharge, MaxAbsPartialCharge, MinAAbsPartaalCaarg,, Fp- DensityMorganl, FpDensityMorgan2,, FpDensittMoorann,, MWHI, MWLOW. CHGHI, CHGLO, LOGPHI, LOGPLOW, MRHI, MRLOW, BalabanJ, BertzCT, Chi0, ChiOn, ChiOv, Chil, Chiln, Chily, Chi2n, Chi2v, Chi3n, Chi3v, Chi4n, Chi4v, Halh Ipc, Kappal, Kappa2, Kappa3, ASb, VSA13, VSA14, TPSA, EStatel, EState 10, EState2, EState3, EState4, EState5, EState6, EState7 EState8, EState9, FractionCSP3, MolLogP. MolMR, 95 of them are cat- egorical: Num ValenccElecttrons NumRadicalEElectronn, HeavyAtomCount,, NHOHCount, NO- Count, Nuu.AliphaticCarrocyyyyoo NumnAliphaticeetterooyolss NumAAlpphaticRings NumAromat- ieCarbocycles, NumAAtomaticHeterooyckee NumAAromaticRings, NumHAcceptors, NumHDonors, NumHeteroatoms, NumRotatableBonds, NunSSauraaeeCaarrcyylle,,, NumSSturaaeddHeeenccyddd. NumSaturatedRings, RingCount, noTeert, ArN, N, NH, COO2, noCOO, S, HOCCN, Imine, NHO, NHI, NH2, Ndealkylation1, Ndealkylation2, Nhpyrrole, SH, aldehyde, carbamate, halide, oxid, amide, amidine. aniline, methyl, azide, azo, barbitur, benzene, benzodiazepine, bicyclic, diazo, di- hydropyridine, epoxide, ether, furan, guanido, halogen, hdrzine, hdrzone, imidazole, imide, isocyan, isothiocyan, ketone, Topliss, lactam, lactone, methoxy, morpholine, nitrile, nitro, arom, nonortho, ni- troso, oxazole, oxime, hydroxylation, phenol, noOrthoHbond, acid, piperdine, piperzine, priamide, prisulfonamd, pyridine, quatN, sulfide, sulfonamd, sulfone, acetylene, tetrazole, thiazole, thiocyan, thiophene, alkane, urea.
However, all of the molecular properties are varied in the three datasets. Specifically, QM9 con- tains 22 frozen molecular properties, NunlaadialFEleetronn, 100 S, SH, azide, azo, barbitur, benzodiazepine, diazo, hdrzine, hdrzone, isocyan, isothiocyan, nitroso, acid, prisulfonamd, sulfide, sulfonamd, sulfone, thiazole, thiocyan, thiophene, ZINC contains 2 frozen molecular properties, NumRadicalEleetron. and prisulfonamd and ChEMBL contains only 1 frozen molecular proper- ties, framul.
Infer-correlations of molecular properties. In Fig. 7, we visualize the linear correlations between each pair of molecular properties across three datasets. From the heatmaps, we can observe that there are no linear correlations between half of the molecular properties, and similar patterns are observed in ZINC and ChEMBL datasets.
Molecular Property Distributions. We visualize 7 molecular property distributions reported in section 6 in Fig. 8. From there, we can observe that the property distribution may vary lot in terms
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Figure 7: Inter-correlation heatmaps for studied molecular properties in QM9, ZINC and ChEMBL datasets.
of different datasets. Notably, the distributions of some properties, egg. QED, are very similar in ZINC and
while some are quite different, e.g., MolWt.
Figure 8: Property distributions of randomly selected molecular properties on QM9, ZINC and ChEMBL datasets. QED
MolWt
LogP
BalabanJ
BertzCT
CHGHI
CHGLO
QM9
ZINC
ChEMBL
Table Quantitative Evaluation of Disentanglement on Latent Space.
Datasets
Models
Disentanglement
Compleness
Informativeness
CGVAE
0.15
0.49
0.79
QM9
MoFlow
0.24
0.57
0.83
HierVAE
0.13
0.27
0.75
CGVAE
0.28
0.45
0.91
ZINC
MoFlow
0.40
0.62
0.87
HierVAE
0.12
0.32
0.77
ChEMBL
Hie
0.14
0.41
0.81
C LATENT SPACE EVALUATION
To evaluate the quality of the learned latent space, we utilize three disentanglement evaluation met- rics, disentanglement, completeness and informativeness (Eastwood & Williams, 2018). To be spe- cific, disentanglement measures the degree to which each latent dimension controls at most one molecular property, completeness measures the degree to which each molecular property is gov- emed by most latent dimension, and informativeness measures the prediction accuracy of molecular properties given the latent representation. From Table 3, we find MoFlow learns better and more disentangled latent space than CGVAE and HierVAE. One possible reason is that MoFlow
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(369) has larger latent space than CGVAE (100) and HierVAE (32) since Flow restricts the latent size to be equal to the input size. Similarly, CGVAE ranks the second likely because its latent space size is larger than HierVAE.
D MOLECULE MANIPULATION EXPERIMENTS
D.1 MOLECULE GENRATION EVALUATION
We evaluate the Validity, Novelty and Uniqueness of the generated molecules as proposed in Kus- ner et al. (2017) in Table 4. We can observe that MolSpacE not only improves the success rate from the baseline methods, but also in general improves the validity, novelty, and uniqueness.
Besides, in Fig. 9, we also report the SSR curves of molecule manipulations over three models on QM9 and ZINC datasets with multiple manipulation ranges (distance in the latent space), [-1, 1], [-5, 5]. 10, 10] and (-20,20) From the figure, we can observe that the trends in each of the curves remain still when the manipulation range changes. general, either too large or too small range is not desired, we set it as hyper-
hyper-paraneen parameter and we observe that [-1, 1] is reasonably good default value. More experiments on molecule manipulation can be found in Appendix D.
14
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40
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8
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property
property
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Hollovil CL
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Morfos $20.50
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propeity
Figure 9; Molecule manipulation performance (average) with various manipulation ranges with three models on QM9 (top) and ZINC (bottom) datasets.
COAeE
HarSEC
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Figure 10: Molecule manipulation performance with various manipulation ranges with three models on QM9 (top) and ZINC (bottom) datasets (better seen in color).
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Under review as a conference paper at ICLR 2022
D.2 MOLECULE MANIPULATION EVALUATION
In this section, we report detailed results for all manipulation ranges [-1,1] [-5, 5). [-10, 10). [-20, 20] terms of success rate and strict success rate in Table and 5. Additionally, the SSR curves of molecule manipulations over three models on QM9 and ZINC in Fig. 10 and SR/SSR curves of molecule manipulation with HierVAE on ChEMBL datasets in Fig. 12. The manipulation visualization of CGVAE on QED, MolWt and LogP is provided in Fig, 13.
visualize
0.8
8
0.35
0.7
0.30
0.6
Denaity
6
0.25
0.5
0.20
0.4
0.15
2-
0.10
0.05
0.1
0.0
0.1
0.3
0.4
0.5
0.6
0.7
0.00
105
115
120
125 MalWi
130
135
0.0
-3
-1
Moll.ngP
Figure 11: Visualization of Molecular property distribution shift while manipulating molecules with MoFlow on QM9 dataset (0 denotes the randomly sampled base molecule and + and -r denote manipulation directions and steps).
lervAL-R
LieeVAL-R
HierVAE-L
HierVA-L
60
60
HiervAE M (:3,5)
HiervAE-M M (-5.3)
HlerVAE-M (-10
HierVAE-MM-33.)
40
"lerVAE-M IM (:20,20)
40
HiervAE (:23,23)
g
20
20
0-
2
5
1
6
property
property
Figure 12: Molecule manipulation performance with various manipulation ranges with HierVAE on ChEMBL dataset (left SR, right SSR) (better seen in color).
D.3 MOLECULE MANIPULATION EVALUATION (CATEGORICAL)
To extensively demonstrate our results, we also randomly select four categorical molecular proper- ties and show the results in terms of SR and SSR in Table 7 and 8, respectively. We observe that the gaps between SR and SSR are much larger than the gaps in continuous molccular properties since it casier for the method generate molecules with same molecular property scores for the categorical data.
E MOLSPACE DEMO
As shown in Fig. 6, we design an interactive real-time system for molecule manipulation, where the user can click random randomly sample molecule and freely select which model interpret, which property to interpret, and tuning the slide bar manipulates the molecule accordingly. The demo video anonymously provided at httpp:://rriii www.eooooo....oo.o.ooooo.....o www.o///w////////ocoooooocoeeeee foreeefodeeee (are66_5ofvvyyyyge www.
18

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Under review as a conference paper at ICLR 2022
Q4)
MolWt
LogP
Figure 13: Manipulating QED, MolWt and LogP properties of sampled molecules with CGVAE model trained on QM9 dataset.
19

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Under review as a conference paper at ICLR 2022
Table 4: Quantitative Evaluation of Latent Manipulation.
Datasets
Models
Success (%)
Validity (%)
Novelty (%)
Uniqueness (%)
CGVAE
N/A
100.00%
76.54%
93.68%
CGVAE-R (-1,1)
16.64
100.00
99.55
13.69
CGVAE-L (-1,1)
8.21
100.00
99.58
40.92
QM9
CGVAE-M (-1,1)
57.59
100.00
73.81
56.60
CGVAE-M (-5,5)
57.52
100.00
75.11
81.27
CGVAE-M (-10,10)
55.71
100.00
76.79
89.06
CGVAE-M (-20,20)
53,29
100.00
79.53
87.52
MoFlow
N/A
100.00
98.23
98.27
MoFlow-R (-1,1)
47.97
91.60
91.60
8.06
MoFlow-L (-1,1)
0.41
40.75
40.75
9.32
QM9
MoFlow-M (-1.1)
41.38
91.63
88.71
24.23
MoFlow-M (-5,5)
56.84
91.93
89.13
51.21
MoFlow-M (-10,10)
48.66
91.64
88.80
67.65
MoFlow-M (-20,20)
37.25
90.15
87.27
78.42
HierVAE
N/A
100.00
79.39
95.14
HierVAE-R (-1,1)
1.16
100.00
84.53
28.91
HierVAE-L (-1,1)
0.87
100.00
84.05
27.26
QM9
HierVAE-M (-1,1)
68.84
100.00
79.66
34.81
HierVAE-M (-5,5)
64.47
100.00
80.28
73.50
61.61
100.00
81.29
78.77
HierVAE-M (-20.20)
60.68
100.00
82.75
63.82
CGVAE
N/A
100.00
99.99
98.51
CGVAE-R (-1,1)
6.18
100.00
100.00
64.76
CGVAE-L (-1,1)
2.94
100.00
100.00
82.26
ZINC
CGVAE-M (-1,1)
46.91
100.00
100.00
89.13
CGVAE-M (-5,5)
44.65
100.00
99.99
95.17
CGVAE-M (-10,10)
43.63
100.00
99.99
96.91
CGVAE-M (-20,20)
44.10
100.00
99.99
96.83
MoFlow
N/A
100.00
100.00
100.00
MoFlow-R (-1,1)
18.00
69.98
69.98
29.04
MoFlow-L (-1.1)
0.11
43.36
43.36
24.87
ZINC
MoFlow-M (-1,1)
24.43
71.26
71.26
15.82
MoFlow-M (-5,5)
40.66
70.72
70.72
31.72
MoFlow-M (-10,10)
36.05
70.43
70.43
44.03
MoFlow-M (-20,20)
27.12
70.07
70.07
56.62
HierVAE
N/A
100.00
100.00
3.27
HierVAE-R (-1,1)
1.26
100.00
100.00
1.22
HierVAE-L (-1,1)
0.91
100.00
100.00
2.43
ZINC
HierVAE-M (-1,1)
42.67
100.00
100.00
3.07
HierVAE-M (-5.5)
55,41
100.00
99.99
4.85
HierVAE-M (-10,10)
52.68
100.00
99.99
6.26
HierVAE-M (-20.20)
46.99
100.00
99.99
7.85
HierVAE
N/A
100.00
94.03
99.45
HierVAE-R (-1,1)
1.16
100.00
84.53
28.91
HierVAE-- (-1,1)
1.02
100.00
93.00
55,09
ChEMBL
HierVAE-M (-1.1)
62.38
100.00
94,24
$6.58
HierVAE-M (-5.5)
56.33
100.00
92.65
92.51
HierVAE-M (-10,10)
61.38
100.00
91.39
89.72
HierVAE-M (-20,20)
59.83
100.00
91.49
75.77
20

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Under review as a conference paper at ICLR 2022
Table 5: Quantitative Evaluation of Molecule Manipulation over variety of molecular properties (numbers reported are success rate in %, -R denotes model with random manipulation, -L denotes model with largest range manipulation, -M denotes model with MolSpacE).
Datasets
Models
Avg.
QED
MW
logP
Balaban]
BertzCT
CHGHI
CHGLO
CGVAE-R
16.64
0.70
1.50
1.10
1.60
1.50
0.70
1.10
CGVAE-L
8.21
0.00
0.40
0.00
0.20
0.00
0.20
0.10
CGVAE-M (-1,1)
$7.59
12.00
12.10
11.30
18.40
11.80
14.10
10,50
COVAE-M (-5,5)
57.52
4,50
3.00
7.20
5.80
3.00
7.20
3.60
CGVAE-M (-10,10)
55.71
1,30
2.70
3.00
5.00
2.20
9.00
1.70
CGVAE-M (-20,20)
53.29
1.10
1.50
2.00
1.90
1,50
9.70
3.70
MoFlow-R R
47.97
4.40
13.60
9.30
22.80
7.30
9.60
9.10
MoFlow-L.
0.41
1.20
0.00
4.10
0.00
0.00
0.00
0.00
MoFlow-M (-1,1)
41.38
39.90
44.20
40.40
27.50
32.20
29.10
37.00
QM9
MoFlow-M (-5.5)
56,84
15,90
26.90
16.20
41.20
32.70
30.40
25.90
MoFlow-M (-10,10)
48.66
3.10
11.60
8.40
26.20
13.80
12.10
7.70
MoFlow-M (-20,20)
37.25
0.60
6.70
6.80
8.50
2.10
1.70
1.40
HierVAE-R
1.16
0.10
0.00
0.10
0.30
0.30
0.40
0.20
VierVAE-L
0.87
1.70
2.00
0.00
0.00
HierVAE-M (-1,1)
68.84
50.70
51.20
54.70
50.60
$4.80
$5.60
53.50
HierVAE-M (-5,5)
64.47
8.60
19.00
15.40
20.30
18.60
28.20
25.70
HierVAE-M (-10.10)
61.61
6.20
22.20
6.90
14.60
23,80
26.50
28.70
HierVAF-M (-20,20)
60.68
9.90
42.80
8.70
9.70
47.00
37.20
42.60
CGVAE-R
6.18
0.10
0.10
0.10
0.00
0.20
0.10
0.30
COVAE-L
2.94
0.00
0.00
0.00
0.00
0.00
0.10
0.10
CGVAE-M (-1,1)
46.91
1.00
0.40
0.70
1.00
0.80
0.40
0.20
CGVAF-M (-5,5)
44.65
0.30
0.50
0.70
0.70
0.40
0.40
0.60
CGVAE-M (-10,10)
43.63
0.20
0.80
0.90
0,70
0.20
1.20
0.80
CGVAE-M (-20,20)
44.10
0.30
2.00
0.20
0,70
0,20
1.70
1.20
MoFlow-R
18.00
0.90
0.80
1.50
2.10
2.00
0.60
0.60
MoFlow-L
0.11
0.00
0.00
0.00
0,00
0,00
0,00
0.00
MoFlow-M (-1.1)
24.43
21.80
25.10
29.00
21.10
22.00
19.30
19,20
ZINC
MoFlow-M (-5,5)
40.66
24.90
24.70
19.10
28.80
26.30
25.20
24.30
MoFlow-M (-10,10)
36,05
11.70
13.50
8.50
19.90
15.30
15.10
14.30
MoFlow-M (-20,20)
27.12
3.00
3.90
6.20
6.90
7.20
5.70
5.60
HierVAE-R
1.26
0.50
0.80
0.90
0.80
1,20
0.40
1.00
HierVAE-L
0.91
0.90
0.50
0.50
1.10
1.00
0.70
0.70
HierVAF-M (-1_1)
42.67
31.90
33,90
27.50
35.90
28.10
35.30
32.20
HierVAF-M (-5,5)
55.41
1.90
9.60
46.30
18.60
49.10
25.80
9.60
HierVAE-M (-10.10)
52.68
1.20
22.90
31.50
15.50
37.00
11.80
1.10
HierVAE-M (-20,20)
46.99
1.30
0.50
5.80
9.70
10.50
9.30
0.10
HierVAE-R
0.93
0.00
0.10
0.00
0.10
0.20
0.10
0.00
HierVAE-L. HierVAE-M (-1.1)
1.02
0.20
0.50
0.10
0.00
0.30
0.00
0.00
62.38
17.50
28.40
22.80
15.60
25.20
23.70
24.80
ChEMBL
HierVAE-M (-5.5)
56.33
3.40
37.40
6.30
7.10
30.10
7.90
8.00
HierVAE-M (-10,10)
61.38
0.10
57.00
17.20
9.60
$3.40
10.90
14.30
VAE-M (-20,20)
59.83
0.40
51.90
25.00
2.10
72.10
11.40
10.60
21

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Under review as a conference paper at ICLR 2022
Table 6: Quantitative Evaluation of Molecule Manipulation over variety of molecular properties (numbers reported are strict success rate in %, -R denotes model with random manipulation, -L
denotes model with largest range manipulation, -M denotes model with MolSpacE)..
Datasets
Models
Avg.
QED
MW
logP
Balaban]
BertzCT
CHGHI
CHGLO
CGVAE-R
1.75
0.70
1.50
0.90
1.60
1.50
0.70
1.10
CGVAE-L
1.14
0.00
0.40
0.00
0.20
0.00
0.20
0.10
CGVAE-M (-1,1)
7,98
11.80
11.20
11.10
16.30
11.40
14.10
10,50
COVAE-M (-5,5)
10.75
4,40
2.90
7.20
5.70
3.00
7.20
3.60
CGVAE-M (-10,10)
13.54
1.30
2.70
3.00
4,90
2.20
9.00
1.70
CGVAE-M (-20,20)
14.25
1.10
1.50
2.00
1.80
1,50
9.70
3.70
MoFlow-R
10.40
4.40
13.60
9.30
20.30
7.30
9.60
9.10
MoFlow-L.
0.35
1.20
0.00
4.10
0.00
0.00
0.00
0.00
MoFlow-M (-1,1)
16.62
39.90
44.10
40.40
21.00
30.90
29.10
37.00
QM9
MoFlow-M (-5.5)
16.26
15,90
26.90
16.20
36.20
31.60
30.40
25.90
MoFlow-M (-10,10)
12.21
3.10
11.60
8.40
25.10
13.80
12.10
7.70
MoFlow-M (-20,20)
8.51
0.60
6.70
6.80
8.50
2.10
1.70
1.40
HierVAE-R
0.19
0.10
0.00
0.10
0.30
0.30
0.40
0.20
HicrVAE-L
0.24
0.10
0.20
0.00
0.10
0.30
0.00
0.10
HierVAE-M (-1,1)
27.30
50.20
48.50
53.80
49.60
$3,40
$5.60
53.50
HierVAE-M (-5,5)
24.96
8.60
19.00
15.40
20.30
18.60
28.20
25.70
HierVAE-M (-10.10)
28.97
6.20
22.20
6.90
14.60
23,80
26.50
28.70
HierVAE-M (-20,20)
30.28
9.90
42.80
8.70
9.70
47.00
37.20
42.60
CGVAE-R
0.72
0.10
0.10
0.10
0.00
0.20
0.10
0.30
COVAE-L
0.43
0.00
0.00
0.00
0.00
0.00
0.10
0.10
CGVAE-M (-1,1)
3.47
1.00
0.40
0.70
1.00
0.80
0.40
0.20
CGVAF-M (-5,5)
5.06
0.30
0.50
0.70
0.70
0.40
0.40
0.60
CGVAE-M (-10,10)
6.46
0.20
0.80
0.90
0,70
0.20
1.20
0.80
CGVAE-M (-20,20)
7.90
0.30
2.00
0.20
0,70
0.20
1.70
1.20
MoFlow-R
2.96
0.90
0.80
1.50
1.90
2.00
0.60
0.60
MoFlow-L
0.03
0.00
0.00
0.00
0.00
0.00
0,00
0.00
MoFlow-M (-1.1)
9.61
21.80
25.00
28.80
16.80
21.70
19.30
19,20
ZINC
MoFlow-M (-5,5)
12.81
24.80
24.30
19.10
24.60
26.20
25.20
24.30
MoFlow-M (-10,10)
9.50
11.70
13.40
8.50
17.00
15,20
15.10
14.30
MoFlow-M (-20,20)
6.10
3.00
3.90
6.20
6,20
7.20
5.70
5.60
HierVAE-R
0.45
0.50
0.80
0.90
0.80
1,20
0.40
1.00
HierVAE-L
0.70
0.90
0.40
0.50
1.10
1.00
0.70
0.70
HierVAF-M (-1,1)
19.40
30.80
32.60
27.00
35.00
27.80
35.30
32.20
HierVAF-M (-5,5)
19.29
1.90
9.60
46.20
18.60
49.00
25.80
9.60
HierVAE-M (-10.10)
14.61
1.20
22.90
31.50
15.50
36.80
11.80
1.10
HierVAE-M (-20,20)
7.68
1.30
0.50
5.80
9.70
10.50
9.30
0.10
HierVAE-R
0.13
0.00
0.10
0.00
0.10
0.20
0.10
0.00
HierVAE-L.
0.36
0.20
0.50
0.10
0.00
0.30
0.00
0.00
HierVAE-M (-1.1)
19.59
17.40
28.30
22.50
15.40
25.00
23.70
24.80
ChEMBL
HierVAE-M (-5.5)
24.89
3.40
37.40
6.30
7.10
30.10
7.90
8.00
HierVAE-M (-10, 10)
38.01
0.10
57.00
17.20
9.60
$3.40
10.90
14.30
VAE-M (-20,20)
39.47
0.40
51.90
25.00
2.10
72.10
11.40
10.60
22

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Under review as a conference paper at ICLR 2022
Table 7: Quantitative Evaluation of Molecule Manipulation over four categorical molecular prop- erties (numbers reported are success rate in %, -R denotes model with random manipulation, -L denotes model with largest range manipulation, -M denotes model with MolSpacE).
Datasets
Models
Avg.
acetylene
tetrazole
thiazole
thiocyan
CGVAB-R
16.64
25.40
29.60
29.60
29.60
CGVAB-L
8.21
12.80
15.90
13.90
13.40
CGVAB-M (-1,1)
57.59
85.10
95.60
95.40
93.10
CGVAE-M (-5,5)
57.52
85.80
99.80
100.00
99.90
CGVAB-M (-10,10)
55.71
78.10
100.00
100.00
99.90
CGVAE-M (-20,20)
53.29
65.90
99.90
100.00
100.00
MoFlow-R
47.97
73.80
75.90
76.20
76.90
MoFlow-L
0.41
0.00
0.00
0.00
0.00
MoFlow-M (-1,1)
41.38
33.50
45.70
31.60
36.10
QM9
MoFlow-M (-5,5)
56.84
71.70
78.30
74.60
73.90
MoFlow-M (-10.10)
48.66
72.90
74.30
77.50
74.70
MoFlow-M (-20,20)
37.25
61.20
58.70
68.90
64.50
HierVAE-R
1.16
1.70
2.00
2.00
2.00
HierVAE-L
0.87
1.70
2.00
0.00
0.00
HierVAE-M (-1,1)
68.84
77.00
83.00
74.00
78.10
HierVAE-M (-5,5)
64.47
91.10
99.50
99.90
100.00
HierVAE-M (-10,10)
61.61
88.30
98.50
100.00
100.00
HierVAE-M (-20,20)
60.68
83.70
95.50
100.00
100.00
CGVAE-R
6.18
14.20
14.20
13.60
14.20
CGVAE-L
2.94
7.10
7.10
6.60
7.00
CGVAE-M (-1,1)
46.91
97.30
99.00
93.90
99.60
CGVAE-M (-5,5)
44.65
97.60
99.30
95.50
99.70
CGVAB-M (-10,10)
43.63
97.40
99.60
95.70
99.70
CGVAE-M (-20,20)
44.10
97.90
99.50
95.10
99.80
MoFlow-R
18.00
31.80
31.20
31.60
34.50
MoFlow-L
0.11
0.30
0.00
0.50
0.00
MoFlow-M (-1,1)
24.43
23.20
23.10
22.20
22.60
ZINC
MoFlow-M (-5,5)
40.66
49.30
50.60
48.50
47.50
MoFlow-M (-10.10)
36.05
51.50
53.60
54.30
52.50
MoFlow-M (-20.20)
27.12
46.90
46.30
46.30
45.40
HierVAE-R
1.26
1.90
1.70
1.40
1.50
HierVAE-L
0.91
0.00
2.00
1.60
0.00
HierVAE-M (-1,1)
42.67
19.90
57.20
35.60
50.40
HierVAE-M (-5,5)
55.41
66.40
97.50
84.50
93.10
HierVAE-M (-10,10)
52.68
95.60
99.60
98.70
99.90
HierVAF-M (-20,20)
46.99
100.00
96.70
100.00
100.00
HierVAE-R
0.13
0.00
0.00
0.00
0.00
HierVAE-L
0.36
0.10
0.00
0.00
0.00
HierVAE-M (-1,1)
62.38
93,20
92.60
94.30
92.80
ChEMBL
HierVAE-M (-5,5)
56.33
98.80
95.30
90.30
100.00
HierVAE-M (-10,10)
61.38
95.20
87.30
81.20
99.90
HierVAE-M (-20,20)
59.83
87.50
74.70
74.00
99.90
23

---

Under review as a conference paper at ICLR 2022
Table 8: Quantitative Evaluation of Molecule Manipulation over four categorical molecular proper- ties (numbers reported are strict success rate in %. RR denotes model with random manipulation. -L denotes model with largest range manipulation, -M denotes model with MolSpacE).
Datasets
Models
Avg.
acetylene
tetrazole
thiazole
thiocyan
CGVAB-R
1.75
2.30
0.10
0.00
0.00
CGVAB-L
1.14
1.20
0.10
0.00
0.00
CGVAB-M (-1,1)
7.98
5.00
0.00
0.00
0.00
CGVAE-M (-5,5)
10.75
17.00
0.00
0.00
0.00
CGVAB-M (-10,10)
13.54
20.00
0.00
0.00
0.00
CGVAE-M (-20,20)
14.25
13.70
0.20
0.00
0.00
MoFlow-R
10.40
7.20
0.70
0.00
0.00
MoFlow-L
0.35
0.00
0.00
0.00
0.00
MoFlow-M (-1,1)
16.62
2.40
0.20
0.00
0.00
QM9
MoFlow-M (-5,5)
16.26
7.70
0.30
0.00
0.00
MoFlow-M (-10.10)
12.21
9.20
0.50
0.00
0.00
MoFlow-M (-20,20)
8.51
4.40
0.30
0.00
0.00
HierVAE-R
0.19
0.10
0.00
0.00
0.00
HierVAE-L
0.24
0.70
0.10
0.00
0.00
HierVAE-M (-1,1)
27.30
12.80
0.20
0.00
0.00
HierVAE-M (-5,5)
24.96
54.70
1.50
0.00
0.00
HierVAE-M (-10,10)
28.97
82.30
4.40
0.00
0.00
HierVAE-M (-20,20)
30.28
82.70
2.90
0.00
0.00
CGVAE-R
0.72
0.20
0.00
0.10
0.00
CGVAE-L
0.43
1.50
0.10
0.40
0.00
CGVAE-M (-1,1)
3,47
0.80
0.10
1.80
0.00
CGVAE-M (-5,5)
5.06
0.60
0.20
2.50
0.00
CGVAB-M (-10,10)
6.46
1.00
0.10
2.60
0.10
CGVAE-M (-20,20)
7.90
1.70
0.40
4.90
0.100
MoFlow-R
2.96
0.10
0.00
0.00
0.00
MoFlow-L
0.03
0.10
0.00
0.00
0.00
MoFlow-M (-1,1)
9.61
0.00
0.00
0.00
0.00
ZINC
MoFlow-M (-5,5)
12.81
0.10
0.00
0.00
0.00
MoFlow-M (-10.10)
9.50
0.00
0.00
0.00
0.00
MoFlow-M (-20.20)
6.10
0.10
0.00
0.00
0.00
HierVAE-R
0.45
0.00
0.00
0.00
0.00
HierVAE-L
0.70
0.00
0.20
0.00
0.00
HierVAE-M (-1,1)
19.40
0.00
0.00
0.00
0.00
HierVAE-M (-5,5)
19.29
0.00
0.80
0.10
0.00
HierVAE-M (-10,10)
14.61
0.00
2.90
0.10
0.00
HierVAF-M (-20,20)
7.68
0.00
1.60
0.00
0.00
HierVAE-R
0.13
0.00
0.00
0.00
0.00
HierVAE-L
0.36
0.10
0.00
0.00
0.00
HierVAE-M (-1,1)
19.59
1.20
2.20
2.90
0.10
ChEMBL
HierVAE-M (-5,5)
24.89
7.30
11.00
16.30
0.00
HierVAE-M (-10,10)
38.01
21.10
21.50
28.50
0.00
HierVAE-M (-20,20)
39.47
16.90
9.00
42.40
0.00
24


Reviews:
[{"review_id": "tdbzqxKELBr", "reviewer": "Reviewer_bacV", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "## Overview\n\nThis paper addresses an important problem which few prior works have addressed before in the literature. Their rough approach is reasonable. However, I had a lot of questions and doubts, some major and some minor, which I will discuss below. I will use **(major+/-)** and **(minor+/-)** to denote issues which are extremely and moderately important to me, respectively.\n\n## Problem definition\n\nI am sympathetic to your problem setting of \"continuously\" tuning the properties of molecules **(minor+)**, as this could be used to make a lot of practical tools (such as the interesting demo you provide in the end). Roughly, given a starting molecule, you should be able to produce another similar molecule but with slightly higher or lower property values. I applaud your efforts to try to define this more precisely in section 4, but I think your definition has several significant problems **(major-)**:\n\n- there seem to be no constraints on the molecules outputted (e.g. a similarity constraint to the starting molecule). Without this, your metrics could be easily exploited by having a sorted list of ~1000 molecules and associated property values, and given a starting molecule, output in order all molecules above/below the starting molecule's property value. A similarity constraint would remove this exploit, for example (although there are certainly other ways to do this).\n- I think the monotonicity conditions in your SR and SSR are too strict, given the high degree of randomness in generative models. By your metrics, a property sequence of [1, 1, 1, 1.00001, 1.001, 1.001] would satisfy both SR and SSR, while [-3, -2, -1, 0.01, 0, 1, 2, 3] would not, even though the second covers a much broader property range and to me seems more in line with what practitioners would want. I acknowledge that this criticism is subjective.\n- I object to the use of the word \"continuous\" in your definitions, which you use several times. All molecular outputs are discrete, and many of the properties you manipulate are integer valued. In the strict technical sense of the word, nothing you do has continuity, although it does match the non-technical usage of the word. Perhaps you could say \"step-wise\" or \"monotonically\" instead?\n\nI acknowledge that creating a good definition of this is hard and I don't claim to have a great definition myself. The best rough suggestion I can think of would be to define a manipulation in $k$ steps as a sequence of molecules $m_1, \\ldots, m_k$ such that $|f_P(m_i) - f_P(m_{i+1})| < \\epsilon \\forall i$ for some reasonable $\\epsilon$, and that $\\text{sim}(m_i, m_{i+1}) > \\delta \\forall i$ for some similarity metric. Given this, I would produce a  long sequence of molecules using your method, until the first instance where the conditions are violated and it ceases to be a valid manipulation. You could then score each manipulation based on its length (longer is better) and the magnitude of the property difference between the starting and ending molecules (larger is better). Which exact scoring function to use is unclear, as well as the appropriate value of $\\epsilon, \\delta$, but hopefully you get what I mean from this sketch.\n\n## Related work\n\nYour paper is closely related to a variety of existing works on molecule generation. While you have many important citations, I think that you are missing many others **(minor-)**:\n\n- Bombarelli et al., 2018 should really be credited with the invention of BO in the latent space of generative models, not Jin et al\n- a variety of other works which do molecule optimization [1-7]\n- work on graph to graph translation, e.g. [8] which is very similar to spirit to yours\n- work on conditional generative modelling for molecules (e.g [9]) which could also be used for your problem setting\n\nThere are also many other relevant works, especially in computer vision, which aim to modify images by manipulating latent vectors. As far as I could tell, no such analogous methods are mentioned in your paper. [10-13] are some starting references for you. Many of these papers propose methods similar enough to yours that you should discuss how the method you propose is different **(minor-)**.\n\n## Your proposed method (section 5)\n\n### A questionable argument in section 5.1\n\nI was not sympathetic to your argument in section 5.1 **(minor-)**. While yes, molecules with similar structures tend to cluster together in latent space, and structures determine properties, it _does not follow_ that molecules with similar properties will cluster together.\nThis is because of _activity cliffs_, a common and well-understood phenomenon where molecules which are very similar can have very different properties [14]. They are caused by many things in biology having cut-offs or other non-linear effects (e.g. if a molecule is a little bit too big it can't fit into a receptor and will have 0 activity). Reading this section it felt like the authors were not aware of this.\n\nAlthough you don't state it explicitly, the success of your method depends on molecules with similar property values clustering together. I think that you should verify this more explicitly, although I do appreciate that in Figure 2 you validated that structurally similar molecules cluster together, which is an important start to producing similar molecules given a target molecule.\n\nOne nitpick about Figure 2: you don't mention how these similarity values are calculated.\n\n### Latent directions: regression becomes classification??\n\nIn section 5.2-5.3 your motivation seems to significantly switch from regression based (manipulating real-valued properties) to classification based (finding a hyperplane between positive and negative classes).\nI find this awkward and not well explained **(major-)**.\nIn general, the existence of a separating hyperplane can only be guaranteed when the positive and negative examples form disjoint convex sets.\nThis seems unlikely to occur with generative models with a Gaussian prior.\nIt seems plausible to me that a generative model could have multiple latent directions which cause the property values to increase.\nFor example, consider molecular weight, which is modified by adding/removing/substituting atoms (effectively anything). Since there are many ways to add/remove atoms from a molecule, I imagine that such a property could be modified by several directions in latent space, and that there would not be a $(n-1)$ dimensional hyperplane separating \"positive\" and \"negative\" classes.\n\nEven if there is just 1 direction of increase which would make the separating hyperplane assumption sensible, I find the separation into positive and negative classes quite artificial **(minor-)**. I could not find an explanation of this in your paper, but thankfully was able to find it in the code (thanks for making your code accessible and quite readable **(minor+)**). It looks like you choose the 2nd percentile of your dataset as the cutoff between positive and negative (so you will have very unbalanced classes). I am a unsure of why this choice was made: was it driven by performance or just an arbitrary choice? An explanation of these choices would make the paper stronger.\n\n## Experiments\n\nI was very happy to see the authors evaluate their method on 3 dataset and over 100 properties **(major+)**. Often I think papers of this sort test too few properties / datasets and I am glad that the authors did not do this. I think that the choice of experiments was generally good, but I have concerns about some choices that the authors made.\n\n### Datasets\n\nI was very happy with the authors' choice of datasets **(minor+)**.\n\n### Baselines\n\nI think the authors were too narrow in their choice of baselines **(major-)**: they compared their method against 2 simple methods which are identical in every way except for the choice of hyperplane. There are many other methods which I think would make competitive and sensible baselines, notably conditional VAEs and graph to graph translation methods like [8]. I acknowledge that these methods don't work with pretrained models, but as a reader I would want to know whether the SR/SSR scores are competitive in an absolute sense, or whether they are just competitive relative to other methods which use pre-trained models. _Having at least 1 competitive baseline would make the paper much stronger.\n\n### Molecular properties\n\nAlthough the authors chose a large number of properties to test, I felt that these properties were all superficial molecular descriptors from `rdkit.Descriptors.descList` which are intrinsically not as difficult to manipulate as real-world experimental properties such as drug activity **(major-)**. The properties mostly fall into just a few categories which can be considered separately.\n\n- MolWt, logP, HeavyAtomCount, and similar properties are essentially a weighted sum over the atoms in a molecule. They can be trivially manipulated by adding and removing specific atoms from anywhere in the molecule.\n- all properties after \"NHOHCount\" are fragment properties which count instances of particular substructures. They can be trivially manipulated by simply adding or removing instances of this substructure.\n- QED is more complex, but is essentially a measure of whether a molecule fulfills a specific set of conditions for MolWt, logP, and counts of certain fragments. It is somewhat more complex to manipulate but still not too difficult in my opinion.\n- FpDensityMorgan* are essentially a normalized count of the number of unique atomic environments in a molecule, and can be increased/decreased by adding/removing unique substructures (admittedly I'm <90% sure about this one)\n- EState, Chi*, Kappa*, BalabanJ are some sort of graph theoretic properties which I don't have a good intuition for, so I don't really know how difficult they are to manipulate.\n\nBecause of the simplicity of these properties, I did not find the manipulations shown to be impressive. I think it would make the paper much stronger to consider more complex properties. My first suggestion is the goal-directed objectives from Guacamol [15] (which are mostly based on fingerprints and therefore still not super realistic). Preferably I would like to see results on at least 1 objective which is not \"similarity\" or \"rediscovery\" since those are too easy. My second suggestion would be molecular docking scores, since these are more realistic. You could consider using the \"dockstring\" package [16] for this. It would not surprise me if MolSpacE performed poorly on these more complex properties.\n\n### Results\n\nIn general I liked the presentation of the results **(minor+)**, but have a few questions **(minor-)**.\n\n1. In Table 1, the averages for SR are often a lot higher than all the 7 property values you show in the table (e.g. ~60 for ChEMBL HierVAE-M while all the other values are < 30). This implies that there are either outliers, or that the properties shown in the table are somehow atypical. Can you provide clarification? One suggestion might be to not show the average, but instead show the average rank of all the baselines. The average can be highly influenced by some tasks being easier than others.\n2. Figure 3 is very unclear. Are the plots for a single property, or for the 7 properties you study? I am not sure what the figure is showing or how to interpret it.\n3. In section 6.4, did you achieve similar results with the fragment properties, or any more complex properties?\n\n## References\n\n[1] Grammar Variational Autoencoder http://arxiv.org/abs/1703.01925\n\n[2] Molecular de-novo design through deep reinforcement learning https://doi.org/10.1186/s13321-017-0235-x\n\n[3] Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks 10.1021/acscentsci.7b00512\n\n[4] Optimization of Molecules via Deep Reinforcement Learning 10.1038/s41598-019-47148-x\n\n[5] Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining https://proceedings.neurips.cc//paper/2020/hash/81e3225c6ad49623167a4309eb4b2e75-Abstract.html\n\n[6] Constrained Bayesian optimization for automatic chemical design using variational autoencoders https://pubs.rsc.org/en/content/articlelanding/2020/sc/c9sc04026a\n\n[7] Optimizing Molecules using Efficient Queries from Property Evaluations http://arxiv.org/abs/2011.01921\n\n[8] Learning Multimodal Graph-to-Graph Translation for Molecular Optimization http://arxiv.org/abs/1812.01070\n\n[9] Conditional Molecular Design with Deep Generative Models https://doi.org/10.1021/acs.jcim.8b00263\n\n[10] GAN Dissection: Visualizing and Understanding Generative Adversarial Networks http://arxiv.org/abs/1811.10597\n\n[11] Interpreting the Latent Space of GANs for Semantic Face Editing https://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Interpreting_the_Latent_Space_of_GANs_for_Semantic_Face_Editing_CVPR_2020_paper.html\n\n[12] On the \"steerability\" of generative adversarial networks http://arxiv.org/abs/1907.07171\n\n[13] Controlling generative models with continuous factors of variations http://arxiv.org/abs/2001.10238\n\n[14] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3869489/\n\n[15] GuacaMol: Benchmarking Models for de Novo Molecular Design 10.1021/acs.jcim.8b00839\n\n[16] https://arxiv.org/abs/2110.15486", "summary_of_the_paper": "This paper proposes Molecular Space Explorer (MolSpacE), a method to generate molecules with continuously varying properties using a pre-trained latent variable generative model. It essentially involves 3 steps (although these steps were not clearly spelled out in the paper):\n\n1. Sample many points from the model and evaluate their properties\n2. For each property, train a SVM to predict the property given the the latent vector\n3. Use the normal to this hyperplane as a \"property manipulation\" direction in latent space, which can be added a latent vector to change the property value of the decoded molecule.", "summary_of_the_review": "Overall I am happy to see a paper addressing this worthwhile problem, but feel that there are too many issues to recommend acceptance, and therefore I must recommend rejection. In order to change my mind, I think the following would need to be addressed:\n\n1. Clarify differences between your method and similar methods for discovering directions in latent space proposed in [10-13]\n2. Further clarification or justification of finding a hyperplane using a classification method\n3. Comparing against a set of non-trivial baselines and having superior, or at least reasonably similar performance\n\nI also think the paper could be made much stronger by:\n\n1. Using a more difficult set of molecular properties\n2. Improving the definition of molecular manipulation and the metrics proposed", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "na9lVna6IKs", "reviewer": "Reviewer_MABC", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "The paper uses linear models trained on latent dimensions of an existing deep generative model for molecular property prediction. The normal directions of the learned decision boundaries are then used to manipulate molecules. \n\nStrengths:\n(1) The paper compares proposed approach with random manipulation and Largest range manipulation on three different datasets, there different deep generative models, and different molecular properties. \n\nWeaknesses:\n(1) The idea of using linear models trained on latent dimensions of existing deep generative models for steerable molecule generation or for interpreting/interacting with generative models are not new. For example, see Das, et al. Nature Biomedical Engineering volume 5, pages 613–623 (2021).\n(2) Steerable molecule generation using predictors trained on latent dimensions has been investigated before, e.g. Chenthamarakshan, et a. NeurIPS 2020, which has been shown to tackle multi-property manipulation well as well. \n(3) The paper does not provide a comprehensive review of the existing works on steerable molecule generation, nor does it consider existing optimization/sampling based methods as baselines (Chenthamarakshan, et al, NeurIPS 2020; Fu, et al, AAAI 2018; Yang, et al, ICML 2020; arXiv:2011.01921). \n(4) Current works consider many different complex molecular properties that are relevant for real-world applications, e.g. . Therefore, this statement does not hold \"For example, existing work only discover two molecular properties, penalized logP (octanol-water partition coefficient) and QED (Drug-likeness) (Jin et al., 2018; Shi et al., 2020; Liu et al., 2018). \" For example, https://arxiv.org/abs/1912.05910 considers DRD2 activity prediction. \n(5) It is not clear what extent of structural diversity is considered in SSR estimation.", "summary_of_the_paper": "This work investigates molecule manipulation towards desirable properties by leveraging existing deep generative models.", "summary_of_the_review": "The idea of leveraging linear property predictors trained on latent dimensions of deep generative models for molecule manipulation/controlled molecule generation is not novel. The work does not consider comparison with. existing baselines, nor does it report performance on real-world property manipulation tasks (such as protein activity manipulation). It is not clear what is the real-world significance of continuous property change. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "VJZyg09SKD", "reviewer": "Reviewer_5bTH", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "### ===== Detailed pros: ===== \n\n \n\n1. Interpreting and dissecting generative models of molecules is an interesting and important direction, and one that so far has not been sufficiently explored. Moreover, the authors have built an interactive system which makes their work much more accessible. \n\n \n\n2. The paper is mostly clear, and also includes helpful figures (Figure 1 gives an especially good overview; Figure 2 is useful to get some qualitative intuition). \n \n\n### ===== Detailed cons: ===== \n\n \n\n1. The work makes several very strong assumptions about what a good latent space-based generative models is, without sufficient evidence or ablations that this makes sense. Overall, it's not clear what the takeaways should be, or how we should interpret the comparison between the models. \n\n(a) Section 5 gives qualitative results showing that nearby latent points decode to similar molecules, and then goes on to assume that properties are linear functions of the latent space, which is a very strong assumption. It's not clear if here \"property is linearly separable\" is a good approximation of \"property can be reliably predicted with a non-linear model\" or \"property is easy to control using optimization\". One way to tackle this would be to show ablations, for example proving that if a property is hard to predict with a linear model, then it's also hard to predict it generally (given only the latent code), or that it's hard to optimize for it. \n\n(b) The steerability metric assumes monotonic changes to the property, which is rather strict, and it's unclear if results would look different if one used a soft monotonicity requirement. \n\n(c) To add to this, it's not clear if the streerability or separability metrics correlate with something useful down-stream, e.g. with optimization performance, or with the ability of the optimization algorithm to exploit (overfit) to property predictors during optimization [7]. \n\n(d) While the authors propose to do optimization by following the normal vector of the separation plane, that does not sound like a very powerful or practical optimization method (it is not compared to more general methods [1-6], and it may not be worthwhile to do this comparison, as I wouldn't expect this to do very well). One argument the authors make is efficiency, which is fair, although it's important to keep in mind that black-box optimization methods are sometimes also very efficient, e.g. when the property predictor is learned directly from the latent space, or it's learned from the molecule space with a very simple model (e.g. random forest on molecular fingerprints). Thus, I think it's hard for the optimization method proposed in this work to really compete with more general ones, and the paper should focus more on explanability/interpretability/predicting which models will be good to optimize in without running optimization.\n\n(e) Finally, it's not clear what getting a bad result on the metrics proposed by the authors means. For example, the authors mention that CGVAE has a large gap between SSR and SR, meaning that if often produces molecules that are different but have the same property value. It's unclear to me if that's a bad thing.\n\n \n\n2. Some statements about prior work are just not true. \n\n(a) \"(…) current methods are confined to a limited number of molecular properties, which hinders real-world applications in drug discovery and material science. For example, existing work only discover two molecular properties, penalized logP (…) and QED\" - false, as many existing methods can optimize towards an arbitrary (in-vivo-computable) objective function [1-5]. For most of these models, I know that they are used in real-life drug-discovery projects in the pharma industry. While many other works do work on toy properties such as ones mentioned by the authors, it's important to note this is not always the case. Even objectives in the Guacamol benchmark [6] are designed to be more realistic that optimizing towards logP/QED by combining several properties (and occasionally other constraints). I think a more realistic discussion is needed here, referring to [1-6] and other papers as well. I would also suggest to pivot this work more strongly on the interpretability aspect (and its quantification), instead of claiming that it actually improves molecular optimization in itself.\n\n(b) To add to the above, even the abstract says: \"it is difficult to (…) customize the output molecule with desired properties\", again suggesting that molecular optimization is hard in itself. As mentioned above, molecular optimization is well-studied, and just getting high values of the optimization objective is typically not hard, and often can be achieved with even relatively simple genetic algorithms. On the other hand, I agree with low interpretability of such techniques, and that they are not very well-suited for interactive design, so there is certainly room for methods similar to the one presented in this work if framed correctly. \n\n \n\n \n\n### =====  Other comments: ===== \n\n \n\n- The paper keeps referring to \"drug-like\" and \"drug-unlike\" molecules, while actually talking about \"high QED\" and \"low QED\". It would be useful to make this explicit (drug-likeness is a vague and hard to define notion, while QED is a simple handcrafted approximation of that notion). \n\n- Bottom of page 5 is confusing, as it shuffles may very strong assumptions without calling them out explicitly. First, it talks about manipulating multiple properties, but equations suggest it's talking about something more specific, which is manipulating *a linear combination* of several properties. It then states that property values follow a multivariate normal distribution, which is a very unexpected conclusion, and is only true because of the strong linearity assumption. \n\n- Figure 4 is very hard to read, maybe use a differrent form to present this result. \n\n- Section 6.3 mentions that MoFlow performed better than CGVAE on the metric defined by the authors due to its reversibility. I think given the vast amount of differences between the models, attributing the difference to reversibility is just speculation, so should be marked as such. Overall, that whole paragraph (2) is highly speculative and in my opinion doesn't really bring much. \n\n- Table 1 is hard to read, because it compares both different models and different ways of choosing the latent direction at the same time. Maybe it would be more readable to first compare the ways of choosing latent directions, establish that the proposed way works the best, and then compare the models. \n\n- It is very odd that the largest manipulation direction performs worse than random. It would be useful to get a bit more intuition into why that happens. \n\n- Bottom of page 8 defines several ranges; what are these ranges for? Distance in the latent space? \n\n \n\n### =====  Nits (didn't influence my score, just here to help): ===== \n\n \n\n- abstract: \"generative models can synthesize new molecules\" -> I guess you mean \"propose\" or \"design\", as \"synthesize\" would imply the molecules are actually made \n\n- page 2: \"the steering the\" -> I guess replace the second \"the\" with \"of\" \n\n- page 3: \"latent space, which is usually modelled as a Gaussian distribution\"; \"latent space, which is commonly assumed to be Gaussian distribution\" -> technically it's the prior that is gaussian, while the latent space is R^l (which is not a distribution) \n\n- page 3: \"and capable\" -> \"and is capable\" \n\n- page 3: \"there exists property functions fP which defines\" -> I'd drop the \"s\" in both cases \n\n- page 5: \"scaling the changes\" -> \"scales the changes\" \n\n- Equation 10: I'm assuming this \\mathcal{E} is supposed to mean expectation; if so, then it's more common to use \\mathbf{E} for that \n\n- Many places use \\mathcal{R} to denote the set of real numbers, while again it's common to use \\mathbf{R} \n\n- In Equation 8 the scaling factor alpha seems to turn into k in Equation 13 (and then alpha means something different) \n\n- Page 8: \"obverse\" -> \"observe\" \n\n \n\n### ===== References ===== \n\n \n\n[1] Efficient multi-objective molecular optimization in a continuous latent space \n\n[2] Learning to Extend Molecular Scaffolds with Structural Motifs \n\n[3] REINVENT 2.0 – an AI Tool for De Novo Drug Design \n\n[4] Reinforced Molecular Optimization with Neighborhood-Controlled Grammars \n\n[5] Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation \n\n[6] GuacaMol: Benchmarking Models for de Novo Molecular Design \n\n[7] On failure modes in molecule generation and optimization", "summary_of_the_paper": "This paper proposes to analyse latent space-based generative models of molecules by measuring how separable the latent space is with respect to several molecular properties. If a property is sufficiently separable, then it can be affected by moving along the normal vector of the separation plane. Experiments compare the streerability of several established generative models. ", "summary_of_the_review": "While the direction pursued by the paper is interesting, there are many strong assumptions made with little justification, and overall it's not clear how one should interpret the results in this work, or what the impact on realistic optimization tasks is. Finally, some things are also unclear, and the relation to prior work is somewhat misrepresented. Thus, I believe this paper falls below the acceptance threshold.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "75Yjwaxt9dF", "reviewer": "Reviewer_Pgt6", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "My first concern about this study is that there are many methods that have simlar concepts (model-agnostic and does not require re-training of the molecule generative model). Some examples are list below. They commonly perform optimization on the latent space toward desired properties without modifying the molecular generative model. Also, they are model-agnostic, applicable to any molecule generative model. I'm wondering if the proposed method is superior to or is advantages in any aspect over existing methods. \n\nGómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., ... & Aspuru-Guzik, A. (2018). Automatic chemical design using a data-driven continuous representation of molecules. ACS central science, 4(2), 268-276.\nGriffiths, R. R., & Hernández-Lobato, J. M. (2020). Constrained Bayesian optimization for automatic chemical design using variational autoencoders. Chemical science, 11(2), 577-586.\nNotin, P., Hernández-Lobato, J. M., & Gal, Y. (2021). Improving black-box optimization in VAE latent space using decoder uncertainty. arXiv preprint arXiv:2107.00096.\n--> bayesian optimization on the latent space to manipulate molecule.\nKwon, Y., Kang, S., Choi, Y. S., & Kim, I. (2021). Evolutionary design of molecules based on deep learning and a genetic algorithm. Scientific reports, 11(1), 1-11.\n--> genetic algorithm on the latent space to manipulate molecule.\nWinter, R., Montanari, F., Steffen, A., Briem, H., Noé, F., & Clevert, D. A. (2019). Efficient multi-objective molecular optimization in a continuous latent space. Chemical science, 10(34), 8016-8024.\n--> particle swarm optimization on the latent space to manipulate molecule.\n\nMy second concern is whether the proposed method provides \"interpretability\". While the authors metioned theyir method is effective on interpreting molecular generative models, the results the authors presented are gradual modifications of a query molecule and their respective properties, just like other related studies. I don't see any particually \"interpretable\" stuffs. ", "summary_of_the_paper": "This paper presents a method named \"MolSpaceExplorer\" that explores the latent space of a molecule generative model to continuously optimize molecules toward despred properties.They identify latent directions by constructing a serapation boundary (hyperplane) on the latent space and use the directions to improve latent vectors, which are then fed into the generatove model to produce new molecules with desired properties. The authors also developed an interface for interactive molecular discovery. The authors mentioned that the main contributions of this study are 1) this method is model-agnostic thus applicable to any molecule generative model 2) it does not require any retraining of the molecule generative model.\n\n\n", "summary_of_the_review": "I think this paper does not demonstrate any significant contributions compared to existing studies.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The reviewers find the work to address an interesting and important problem but have several critical concerns about its insufficient treatment of prior work in this area,  lack of novelty in relation to the body of existing literature.
Decision: Reject