Genetic Algorithm for Constrained Molecular Inverse Design
Authors: Yurim Lee
Kyudam Choi
Cheongwon Kim
Venue: ICLR 2022 Submitted (2022)
Keywords: Genetic Algorithm
Constrained Optimization
Molecular Inverse Design
Molecular Generation

Abstract: A genetic algorithm is suitable for exploring large search spaces as it finds an approximate solution. Because of this advantage, genetic algorithm is effective in exploring vast and unknown space such as molecular search space. Though the algorithm is suitable for searching vast chemical space, it is difficult to optimize pharmacological properties while maintaining molecular substructure. To solve this issue, we introduce a genetic algorithm featuring a constrained molecular inverse design. The proposed algorithm successfully produces valid molecules for crossover and mutation. Furthermore, it optimizes specific properties while adhering to structural constraints using a two-phase optimization. Experiments prove that our algorithm effectively finds molecules that satisfy specific properties while maintaining structural constraints.

Under review as a conference paper at ICLR 2022
GENETIC ALGORITHM FOR CONSTRAINED MOLECU- LAR INVERSE DESIGN
Anonymous authors Paper under double-blind review
ABSTRACT
A genetic algorithm is suitable for exploring large search spaces as it finds an ap- proximate solution. Because of this advantage, genetic algorithm is effective exploring vast and unknown space such as molecular search space. Though the algorithm suitable for searching vast chemical space, it is difficult to optimize pharmacological properties while maintaining molecular substructure. To solve this issue, we introduce a genetic algorithm featuring a constrained molecular in- verse design. The proposed algorithm successfully produces valid molecules for crossover and mutation. Furthermore, it optimizes specific properties while adher- ing structural constraints using a two- two-phase optimization. Experiments prove that our algorithm effectively finds molecules that satisfy specific properties while maintaining structural constraints.
INTRODUCTION
When the search space is complex partially known, it is difficult to optimize current solutions using gradient descent (Jin Ha, 1997; Ahmad et al., 2010). Because genetic algorithms find ap- proximate solutions, they are effective in exploring vast unknown space such as molecular search space. (Holland, 1992; Jin & Ha, 1997; Ahmad et al., 2010; Henault al., 2020). drug discovery, optimizing complex pharmacological properties using genetic algorithms have been widely studied and extended (Leardi, 2001).
Deep learning-based molecular design is being actively conducted to improve pharmacological properties and has served as a powerful tool (Gómez-Bombarelli et al., 2018). However, in- ferred drug candidates depending by chemical researchers (George Hautier, 2020). Furthermore, this not only produces non- synthesizable molecules but also causes optimization problems for non-linear structure-activity et al., 2018; Vanhaelen et al., 2020).
the architecture did not satisfy the heuristic guidelines set
lationships
Most molecular design methods first generate molecular structures and then calculate properties of the structures resulting in high computational cost (Duvenaud et al., 2015; Gilmer et al., 2017; Fein- berg et al., 2018 8; Yang al., 2019). Conversely, inverse molecular design specifies target properties in advance and then systematically explores the chemical space to discover molecular structures that have the desired properties (Sanchez-Lengeling & Aspuru-Guzik. 2018).
Recent advances in molecular inverse design of genetic algorithms compete with or even surpass deep learning-based methods (Yoshikawa al., 2018: Jensen, 2019; Nigam et al., 2019; Polishchuk, 2020; Ahn et al., 2020; Nigam et al., (021b;a) This indicates that the computational methods of ge- netic algorithms in a chemical domain are more effective for exploring the vast chemical space (Ahn et al., 2020). However, the genetic operators cause difficulty in lead optimization where the molec- ular structure constrained (Hasançebi Erbatur, 2000).
A common molecular design strategy is to narrow the chemical search space, starting with known potential molecules (Lim et al., 2020). The scaffold, which is the "core" of the molecule is inten- tionally maintained to preserve basic bioactivity (Hu et al., 2016). This is directly involved in the interaction with the target protein (Zhao & Dietrich, 2015: Lim et al., 2020). In lead identification process, scaffolds with biological activity to the target protein are identified (Zhao & Dietrich, 2015). In lead optimization process, it is important to optimize the StR(Structure-AAttvvn Relationship) properties while staying in the chemical space associated with the privileged scaffold (I (Langevin

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et al., 2020). Lead optimization can be described as multiple optimization problem in scaffold constraints (Langevin et al.. 2020). Most studies have focused on the application of generative mod- els to the field of medicinal chemistry, and studies related to structure-constrriined lead optimization have not been extensively explored.
In this regard, we introduce genetic algorithm featuring constrained molecular inverse design. We use graph and SELFIES descriptors to generate valid molecules through a two-phase GA-based pipeline. In addition, we introduced a two-phase optimization ensure that molecular generation does not fail to optimize for specific properties while adhering to structural constraints.
2 RELATED WORK
Among the various methods for constrained optimization in a genetic algorithm, the basic one de- signing effective penalty functions (Yeniay, 2005). The functions, which impose a penalty to fitness value, are widely used for constrained optimization (Yeniay, 2005; Fletcher, 2013). The individual fitness value is determined by combining the objective function value with the constraint violation penalty, A dominant relationship exists between the constraint penalty and the objective function value (Coello, 2000).
One of the various methods is focusing on the selection of practicable solutions. To solve the prob- lem of constrained optimization using genetic algorithm, two-phase framework which is Multi- Objective Evolutionary Algorithm (MOEA) was introduced (Venkatraman & Yen, 2005). In the first phase, MoEA confirms the constraint satisfaction of solutions using a penalty function. The algo- rithm ranks solutions based on violations of constraints, completely disregarding objective function. When more feasible solutions are identified, the second phase continues. In the second phase, individual fitness is reassigned according to the objective function-constraint violation space and bi-objective optimization is performed (Venkatraman & Yen, 2005).
Stosttur-coonsrrained molecular optimization work was first presented in the JT-VAE (Jin et al., 20) study. In this study, they create tree-structured scaffold for constraining the structure of molecule and then generate molecular graph using message-passingg network (Jin et al., 2018a). GCPN (You et al., 2018) uses goal-directed reinforcement learning to generate molecules with de- sired properties and similarities. VJTNN (Jin et al., 2018) 8b) treated molecular optimization as graph-to-graph translation problem and solved it through MMPA(Matched Molecular Pair Anal- ysis) (Dalke et al., 2018) dataset. DEFactor (Assouel et al., 2018) generated molecules with op- timized penalized LogP while maintaining molecular similarity through differentiable conditional probability- graph VAE.
Glen & Payne proposed a method for generating molecules through domain-specifie ruse-based crossover and mutation to ensure molecular structure constraints (Glen & Payne, 1995). To produce offspring molecules with the parent's 's substructures, the crossover is used as a strategy to cut the end part of the molecule and link it to the end part of another similar molecule. To produce new molecules, twelve operators of mutation were defined: atomic insertion, atomic deletion, etc (Glen & Payne, 1995).
ChemGE (Yoshikawa et al., 2018) uses the grammatical evolution of SMILES (Simplified Molecular-Input Line-Entry System) (Weininger, 1988) to optimize penalized LogP and KITH pro- tein inhibitors. Their study converts SMILES into integer sequences using a chromosomal mapping process (Yoshikawa al., 2018). Subsequently, operators of mutation are used to optimize gram- matical evolutionary molecular populations. GA-GB (Jensen, 2019) and MolFinder (Kwon & Lee, 2021) defined expert rules for operators of crossover and mutation to ensure the validity of the struc- ture when generating molecules. GEGL. (Ahn et al., 2020) created optimized SMILES strings for penalized LogP by presenting reinforcement learning contained expert policy.
Various studies have proposed SMILES-based approaches since they are convenient to convert complex 3D chemical structure to a simple ID string (Yoshikawa et al., 2018; Kwon & Lee, 2021; Ahn et al., 2020). However, adding atoms or square brackets to string indeed changes the structure globally not locally (Dalke, 2018). It is difficult to generate a completely valid molecule because SMILES are context sensitive (Dalke, 2018; Kwon & Lee, 2021). Recently, the development and application of SELFIES(Self-F Reffeeen-Rerrriiingggggg Embedded Strings), which is a 100% valid string repre- sentation, has been implemented in the molecular inverse design cope with this problem (Krenn
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al., 2020). The GA-D (Nigam al., 2019) generated SELFIES strings using random operator of mutation while maintaining validity. Furthermore, this study maintained the molecular diversity of the population through adaptive penalty of the deep neural discriminator (Nigam et al., 2019). The STONED (Nigam et al., 2021b) study was introduced for performing local chemical space search and molecular interpolation using SELFIES Furthermore, the mutation site limited terminal 10% to maintain the molecular scaffold (Nigam et al., 2021b). SELFIES makes it possible to gen- erate new molecules through the random operation without relying on expert rules operations of mutation (Nigam et al., 2021b).
PROPOSED METHOD
In this section, we introduce a novel genetic algorithm for constrained optimization in molecular inverse design. Our algorithm generates molecules suitable for the target properties while constrain- ing the structural similarity conditions. First, our algorithm constructs a population that always satisfies the structural similarity condition the first phase. Second, the algorithm selects the appropriate molecular descriptors ac- cording to the genetic operators to ensure the validity of the molecule. The detailed whole process shown figure
target molecule. We use two strategies satisfy the constraint
Phase 1)
Constraint Satisfaction for Population
Initial Population
Compute Tanimoto Similarity
Does at lenst feasible solution exist?
No
END
Evaluation Fineess Score
F(m) Stmttarityeenaltymm)
Yes
Filtering
Phase 2)
Bi-optimization for property and constrainl conditions
Seleztoon
Reproduce
Termination Reuched??
Yes
Muration)
END
Computc Propertics
(penalized similarity)
No
Evaluation Fimesss Score
F(m) = PlagP(m) SomtlarityPeenaltymm
New Population
Figure 1: Illustration of genetic algorithm process for constrained optimization
The algorithm starts by constructing a population depending on whether the target molecule or similar molecules exist in the dataset. In the case of the target molecule its similar molecule existing in the dataset, the population consists of molecules above the similarity calculated by the similarity between the molecules in the dataset and the target molecule. On the contrary, target molecular SMILES is randomly arranged to construct population according to the population size.
Next, algorithm confirms constraint satisfaction from the population and proceeds with bi- optimization for property and constraint conditions. The detailed process explained the sub- sections below.

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3.1 TWO-PHASE MOLECULAR OPTIMIZATION
In the work of constrained molecular optimization, the optimization often failed when a large num- ber of molecules were subject penalty included in the population. Therefore, focused on en- suring that the population always contains only feasible solutions. Inspired by MOEA (Venkatraman & Yen, 2005), we divided the process into constraint satisfaction and bi-optimization, each of which is further explained in the following.
Constraint Satisfaction for Population In this phase, the algorithm searches for feasible solu- tions considering only the structural similarity. The objective function is not considered in this process. The algorithm measures the Tanimoto coefficient of how similar is to the reference molccule(Nigam et al., 2019). The similarity between the target molecule m and the product molecule m' is expressed as sim m'). Similarity Penallyyy) is given to each molecule ac- cording follows:
0, sim (m, m') 2 1 sim(m, m') < 5
SimilarityPennattymnn
(I)
SimilarityPPenalllil is set to 0 if sim(m, m') 2 8 (Nigam et al., 2019). Otherwise, death penalty of 100 is given. Next, the algorithm assigns fitness value to each individual by equation 2 and selects only feasible solutions. A fitness function expressed as follows:
F F(m) = Simalority/Penattyrr
(2)
Bi-Optimization for Property and Constraint Condition In this phase, the algorithm switches to bi-optimization for the fitness function value and the constraint condition when one more feasible solutions are found. Our work simultaneously is to maximize the penalized Log value while retaining the molecular structure. The penalized LogP P (J(m) of each molecule is expressed as follows:
((m LopP(m) SAScore(m) Ringleenalty(m)
The higher score for J (m) has more suitable structural profile as a drug (Yoshikawa al., 2018). LogP(m) is the octanol-water partition coefficient for the molecule m. SAScore(m) is the Syn- thetic Accessibility score, which quantitative score for whether molecular structure can be synthesized (Ertl & Schuffenhauer, 2009). The higher the score, the more difficult it is to synthesize molecules. By giving a penalty according to the score, it is possible to prevent the generation of molecules that cannot be synthesized. RingPenaltyl (m) is used to give penalty to prevent the cre- ation of molecules with unrealistically many carbon rings. This function is a penalty for molecules with seven more rings of carbon (Yoshikawa et al., 2018).
The algorithm reassigns the fitness value to each individual according to equation 4 and sorted by rank. A fitness function can be represented follows:
F(m) = [(m) - -SimilarttyPennatyymm)
(4)
We wanted not only to preserve the superior individuals but also to replace the inferior individuals through reproduction. A portion of the generated molecules are replaced while the rest of the are kept through the selection module. The probability of replacing a molecule is determined using a smooth logistic function based on ranking of fitness (Nigam et al., 2019).
3.2 GENETIC OPERATORS
In chemistry, expert rules are required to create valid molecules through the operation of crossover and mutation. We used graph molecule deseriptor for crossover and SELFIES molecule descriptor for mutation. The detailed process is shown in figure 2.
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Crossover Performing one-point crossover string is faster than the operation of graph. However, arbitrarily cutting strings does not take the molecular structure into account, We used an operator of graph-based crossover presented in GB-GA (Jensen, 2019) Considering the structure, our algorithm distinguishes between cutting and not cutting rings. There are two types of cutring methods considering the molecular structure: non-ring cut and ring cut. The probabilities all the same in both cases.
An operation of the crossover used to maintain diversity of individuals. This operation first selects two-parent molecules from the population randomly. Next, it performs ring crossover or non-ring crossover according to set probability. The bonding of fragmented molecules proceeds according to the rules of reaction. It generates minimum of 0 to maximum of new offspring molecules through an operation of crossover, To maintain the population, only one is randomly selected from the produced offspring molecules.
Mutation operation mutation uses the SELFIES (Krenn al., 2020) strings, which can pro- duce 100% valid molecules with string conversion without special rules. The SELFIES strings can maintain the validity of the molecule even when a random point operation of mutation is performed in STONED (Nigam et al., 2021b). Therefore, performed one-point mutation using SELFIES which lighter and more efficient than graph-based. There are three types of one-point mutation maintain diversity of individuals: atom replacement, atom insert, and atom deletion. The prob- abilities are all the same in all three kinds of cases. Although the SELFIES strings produce valid bioisosteric replacements. We restricted the range of
molecules, we wanted the mutation to the operation of mutation to the terminal 10% (Nigam et al., 2021b). When mutation is performed, the specified probability determines which operator of mutation perform. The position of the point is randomly determined within the specified range, and then the corresponding operation is performed.
A-2) [Parent A]
[Fragement
Fragement A-1
Fragement
point [Parent]
[Parent B]
[Fragement 2]
Non-ring Cut
Fragement
Fragement B-2
cut point
[Offspring]
[Frugement I]
Frageeent- FragmentD))
FrgementA-1 FragmentB-2
[Paren]]
Fragemment 2]
< Ring Cut
FregementA-2 + Fragment-1
Pmigmemm--2 Prigmentl-22
B-1)
B-2)
[SMILES]
[[C[[C@Heep]][aaao [[[[mmch2_2_ppp 2[C]IN)...
[Atom Insert]
[SELFIES]
mutation site
[Atom Replacement)
[[@@lessp][Branch Branchh2 [[C
CC[C@Hexl|[Branch [[[rancch2 [[[C[[[
[[C]]]][C Ring [[Brnchl [[N][S] (Branch [[[]aC[[Bancht [[[[0]0][-0]
[O@Hesp [Branchl _ |BBanch2_ [[C]]N]
[Atom Deletion]
mutation site
Figure 2: Illustration of genetic operations. A-1) Operators crossover A-2) Operation of crossover B-1) SMILES SELFIES transformation and mutation restriction sites(terminal 10%) B-2) Opera- tors mutation
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4 EXPERIMENTS
4.1 DATA SET
The experiments used 250,000 commercially available molecules extracted from ZINC (Irwin al., 2012). LogH, SAScore, and RingPenalty constituting ((m) for the molecule m are normalized based on the corresponding data set. The optimization target 800 molecules with the lowest pe- nalized log? value in the data set.
4.2 SIMULATION RESULTS
In this section, we compared our model with various baseline models using penalized LogP. All generated molecules satisfy the constraint that the structural similarity between the original molecule and the generated molecule higher than the fixed threshold.
This work is realistic scenario where drug discovery usually starts from known molecules, such as existing drugs (Besnard et al., 2012; Lim et al., 2020). For comparison with other baseline models, the molecular generation size was limited 81 SMILES lengths. This is because the value of Log varies depending on the number and type of atoms, and good score can be obtained by increasing the molecular size make it arbitrarily large (Jensen, 2019). 0.4 or 0.6 which is the Tani- moto similarity threshold. For each molecule, the maximization was performed for 20 generations measure the greatest. The improvement measurement results are shown Table 1.
Ó 20.4
ó 20.6
Improvement
Sucess
Improvement
Sucess
JT-VAE
0.84
84%
0.21
46.4%
GCPN
2.49#1.30
100%
0.79#0.63
100%
MMPA
3.29#1.12
1.65+1.44
DEFactor
3.41+1.67
85.9%
1.55
72.6%
VJTNN
3.55
2.33
GA-DNN
5.93#1.41
100%
3.44+1.09
99.8%
Constrained GA(Ours)
5,53+1.29
100%
3.67*1.29
100%
Table I: Comparison on constrained improvement of penalized LogP specific molecules
Penalized LogP Distribution
0.40
origin
0.35
improvement(s 0.4)
improvement66 x 0.6)
0.30
0.25
Density
0.20
0.15
0.10
0.05
0.00
-10
-5
panelized LogP
Figure 3: Distribution of penalized LogP
Figure 3 shows the distribution of how much penalized LogP has improved compared to the original molecules. The penalized LogP of molecules was maximized, and the distribution shifted from right to left. In the case of the 5 2 0.4, it can be seen that the distribution of penalized LogP is more

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skewed to the right than 8 2 0.6 because the structural restriction is lower. In Figure 4, molecules with maximized penalized LogP were visualized while maintaining structural constraints.
Original
Improvement
Original
Improvement
Similarity = 0.619 Improvement +6.97
Similarity 00.407
Imprevement +10.833
PlogP -3,456
PlagP 3.523
PlogP -7.374
Plog? 5.00
Similarity 0.63
Similarity
Improvement +6.59
Improvement 19.788
PlogP -3.066
PlogP 1.524
PlogP -8,371
PlogP = 1885
Similarity
Similanity
Improvement +6.529
Improvement 19.651
Plogl- -5.664
PlogP 0.865
PlogP - -5.066
PlogP 4.585
Figure 4: Compounds generated at the twentieth iteration (left 2 0.4 right: : 0.6)
We focused cannabidiol molecules which expected to have promise for targeting various disease proteins. Cannabidiol is natural product extracted from cannabis and is non-psychoactive ingredient. This attracting attention a promising molecule that improve Alzheimer's disease symptoms by reducing beta-amyloid accumulation and amyloid plaque prodcution, which affects the pathogenesis of Alzheimer's disease (Cooray al., 2020; Jiang al., 2021). We experimented with penalized LogP molecular optimization of cannabidiol. The Tanimoto similarity coefficient is set to 0.6, and optimization carried out up to the 19th generation. The Murcko Scaffold (Bemis & Murcko, 1996) of the cannabidiol molecule is compared with the generated molecules. Duplicate molecules are removed and total 11 molecules are compared. The visualization result is shown in figure 5. In this figure, the yellow site is molecular scaffold and the red sites are the altered points in the target molecule. Although some generations do not retain the scaffold, it can be seen that most of the generated molecules do retain the scaffold. Moreover, the scaffolds of all the generating molecules contain cannabidiol scaffolds as substructures. Furthermore, Hydrophobic carbon-valent functional groups mainly added which can improve the penalized LogP value.
[Sart]
[Gennrationn
[Generation 2]
[Generation.]] 3]
RogP 2.38
PogP 5.24 Cure Siailuuty - 0,53
Plag? Similarit
PkypP Siniladt
[Gemeration 4]
(Generation 5)
[Gensnation 7]
[Gieneratio 9]
Mugfh=477 4476 Core Simbbeit 1.0
MigP-5005 5505 Core Simibrit - dá1
PlogP - 5 Core Simitrity [Generation 17]
5,06 Ches Sinilaity
[Generation
[Genention
[Generation 19)
PlogP 5.561 Siniurty 1.
Hegh - 5.9 Com Simbrity -
Cues Similariy
Sinikeit
Figure Optimization of Penalized LogP for Synthetic Cannabidiol 2 0.6)

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5 CONCLUSION
In molecular inverse design, the computational method of genetic algorithms is effective in exploring vast chemical spaces, A common molecular design strategy is to narrow the chemical search space, starting with known potential molecules. In lead optimization process, the scaffold, the "core" of the molecule, is intentionally maintained to preserve basic bioactivity. This process of optimizing SAR(SStrctureeAAct Activvtty Relationship) properties is multi-objective problem that maintains chem- ical space associated with the privileged scaffold.
In this work, demonstrated molecular inverse design that maximizes penalized LogP while constraining the molecular structure. The proposed method constructs a solution set in which the molecular population always satisfies the structural similarity through two-phase optimization. To generate valid molecules, we used to graph and SELFIES molecular descriptors. Our algorithm ef- fectively generated optimized molecules while maintaining structural similarity target molecules. Experiment shows that excels over the state of arts model. In cannabidiol molecular optimization, our model able to maintain the molecular core and optimize target properties over generations.
It is very important to design new molecules and materials with specific structures and functiion. In light of our experimental results, optimizing only penalized LogP may not take the diversity of molecular functional groups into account when binding to protein targets. In this regard, we need an extension method that can consider various properties at the same time. Our algorithm uses docking module to enable inverse molecular design with enhanced binding affinity for biological targets. Moreover, expected to be used design peptides or proteins with the desired function.
6 REPRODUCIBILITY STATEMENT
The genetic algorithm parameters generates 1000 offspring molecules for each generation. The probability of crossover and mutation 100% and 50% respectively. The extent of mutations restricts to the terminal 10%. The code for reproducibility is posted in the url anonymous repository; https: :/ //annyyoonusss.a 4open. seiiencce///eeproouuee
follows; The population size was set 100. The algorithm
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10


Reviews:
[{"review_id": "WIzbjU6G6XO", "reviewer": "Reviewer_WQyJ", "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": "1.\tCompared to (Nigam et al., 2019), the objective function is almost the same, i.e., Eqn.(3) in this paper and Eqn.(2) in (Nigam et al., 2019). The crossover and mutation operations are also used in (Nigam et al., 2019).\n2.\tThe authors only conduct experiments on improving LogP, which cannot show that this is a general method for molecule generation.\n3.\tThe improvement over GA-DNN is limited.\n", "summary_of_the_paper": "The authors proposed a genetic algorithm for molecule generation. The reward is a combination of several objectives like Eqn.(3). The genetic operations include crossover and mutation. The authors conduct experiments to optimize the LogP of molecules.", "summary_of_the_review": "The method lacks novelty and experiments are not convincing.", "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": "Ek4HbkzKh5", "reviewer": "Reviewer_YxXB", "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. On a high level, molecular optimization under structural constraints is an important research direction, and it has relevance to real-world tasks. \n\n2. Results on the core optimization task look promising. \n \n\n### ===== Detailed cons: ===== \n\n \n\n1. The experiments in the paper are limited and not very relevant to real-world drug discovery. Moreover, as the algorithmic novelty is rather mild, I'd expect a more thorough experimental evaluation. \n\n(a) The only explored objective is penalized logp optimization from the JT-VAE work. While this is slightly more realistic than optimizing for logp alone, it's still a rather toy-ish optimization objective, which can be seen from Figure 5 (most improvements are based on attaching long carbon chains). It would be more interesting to explore a variety of more complex tasks, like optimizing against learned property predictors [1], Guacamol benchmarks [2], docking score optimization [3] or scaffold-constrained optimization [4,5]. \n\n(b) While the paper talks a lot about structural constraints, the way this is realized is through a hard constrained on similarity with a seed molecule. In real-world usecases, it is more common to have a subgraph (scaffold) constraint as a hard constraint, and similarity to seed as a soft constraint. Setting up the problem like this would also showcase some of the unique abilities of genetic algorithms: while it's hard to \"bake in\" the similarity constraint into *any* method (i.e. perform optimization without ever breaking the constraint), it is possible to define mutation/crossover operations for genetic algorithms that maintain the subgraph (scaffold) constraint. It would be interesting to see how a scaffold-constrained genetic algorithm does on scaffold-based optimization tasks [4,5]. \n\n(c) A well-known pitfall of genetic algorithms when applied to drug discovery is producing molecules that are unreasonable (toxic, unstable, etc). This should be assessed through post-filtering checks like quality filters (e.g. [6]). \n\n \n\n2. Several parts of the paper are unclear. \n\n(a) When the algorithm cannot find molecules that satisfy the constraint to form the initial population, the authors say the initial SMILES are \"randomly arranged\". Does this mean random SMILES strings are made up (e.g. by randomly adding atoms), or that random (entire) SMILES strings are randomly selected from the dataset? \n\n(b) When describing the different evolutionary operators and the choices there, the paper says \"probabilities are the same\", and it's unclear what that exactly means: for example, when you have to add a new atom, is the probability of choosing each of the atom types uniform? \n\n(c) Unclear what \"we restricted the range of the operation of mutation to the terminal 10%\" means. \n\n(d) Unclear what \"optimization target is 800 molecules with the lower penalized logP value in the data set\" means. \n\n(e) The conclusion says \"Our algorithm uses a docking module\"; I'm not sure what this means, since this is the first time docking is even brought up. I'm assuming this was meant to describe future work. \n\n \n\n3. Prior work is somewhat misrepresented. \n\n(a) At the top of page 2, the authors say that structure-constrained optimization has not been extensively explored, while there are many scaffold-constraints models out there [4,5,7]. \n\n(b) The authors claim JT-VAE is a *structure constrained* model due to the junction tree representation, which I don't believe is fully accurate: the junction tree is an intermediate representation, which isn't meant to constrain the *final* molecules in a specific way (as any molecule can be decomposed as a junction tree). Instead, the junction tree allows the model to keep the *intermediate* states of generation reasonable (and avoid e.g. unfinished aromatic rings). While the JT-VAE work indeed evaluates on similarity-constrained optimization, the model itself isn't specially geared towards this task, and it doesn't introduce special constraints during the generation procedure that would help to meet the similarity threshold. \n\n \n\n### =====  Nits (didn't influence my score, just here to help): ===== \n\n \n\n- appendix: \"algorithms is effective\" -> \"algorithms are effective\" \n\n- \"The proposed algorithm successfully produces valid molecules for crossover and mutation\" -> I guess replace \"for\" with \"through\" \n\n- first paragraph of intro, \"have been studied\" -> \"has been studied\" \n\n- second paragraph of intro, unclear what \"depending on the architecture\" means there \n\n- top of page 2: \"multiple optimization\" -> \"multi-objective optimization\", \"in scaffold constraints\" -> \"under scaffold constraints\" \n\n- bottom of page 3: \"on the contrary\" -> \"otherwise\" \n\n- typos in figures: \"fragement\", \"panelized\" \n\n- caption of Figure 3 says the distribution has shifted from right to left, whereas I think it's the other way around \n\n \n\n### ===== References ===== \n\n \n\n[1] Hierarchical Generation of Molecular Graphs using Structural Motifs \n\n[2] GuacaMol: Benchmarking Models for de Novo Molecular Design \n\n[3] We Should at Least Be Able to Design Molecules That Dock Well \n\n[4] Scaffold-based molecular design with a graph generative model \n\n[5] Learning to Extend Molecular Scaffolds with Structural Motifs \n\n[6] https://github.com/PatWalters/rd_filters \n\n[7] Scaffold-constrained molecular generation ", "summary_of_the_paper": "This paper proposes to use a two-stage genetic algorithm for similarity-constrained molecular optimization. The authors define chemically-relevant strategies for crossover/mutation, and show good results on constrained optimization of penalized logp.", "summary_of_the_review": "The paper explores reasonable techniques, but the experimental evaluation is limited, and the overall setting is not very relevant to real-world tasks. Moreover, many things are left unclear. I believe the paper needs more work to be made ready for publication. ", "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": "6ZCdYnOz6fy", "reviewer": "Reviewer_g92F", "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": "\nStrength:\n1. The whole framework well adapts existing methods with good motivations.\n2. The proposed model outperforms other baselines in the PlogP optimization benchmark. \n\n\nWeakness:\n1. The novelty is limited. They apply an existing genetic algorithm framework and the fitness functions are straightforward. The operations are the most novel point in this paper, however, its novelty is very limited because its modification is not significant compared to the existing one. In addition, the graph-based crossover is an adaptation of GB-GA. \n2. Experiments only show one property optimization, but they claim it as a multi-objective problem. There are many molecular properties that need to be optimized at the same time, which should have been considered in this paper in order to prove its superiority. Please refer to the multi-objective optimization benchmark in CMG[1].\n3. Only PlogP optimization was shown in the experiment. There are other properties for the single objective optimization such as QED or DRD2. \n\n[1] Shin, Bonggun, et al. \"Controlled molecule generator for optimizing multiple chemical properties.\" Proceedings of the Conference on Health, Inference, and Learning. 2021.", "summary_of_the_paper": "This paper presents a genetic algorithm framework for molecular optimization. It produces valid molecules through the crossover and mutation operations along with appropriate fitness functions. The evaluation was done in one of the property optimization tasks. \n", "summary_of_the_review": "The reason why I can't recommend this as one of the ICLR papers is\n1. The technical novelty is almost none.\n2. Many other experiment results should be provided in order to assess various aspects of the proposed model.", "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": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "3o7QaXDQOUB", "reviewer": "Reviewer_QMQj", "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": "Strength:\n * Related work section is comprehensive.\n\nWeakness:\n * Weak and incomplete results. In Table 1, the model performance is quite similar to GA-DNN baseline. For $\\delta>0.4$, constrained GA (proposed method) is 5.53 while GA-DNN achieves higher logP improvement 5.93. The original benchmark proposed by Jin et al. 2019 also did molecular optimization for QED and DRD2, but authors did not report any results on these two properties.\n * Lack of novelty. The proposed method is a direct application of standard genetic algorithm. The only difference is that this paper applies a two-stage optimization procedure that enforces the similarity constraint first and then optimize the property. The cross-over operator is the same as GB-GA (Jensen 2019), and the mutation operation is based on SELFIES string (Krenn et al. 2020).\n* There is no ablation study of modeling choices. What's the benefit of two-stage procedure? Does that make a huge difference in model performance? The main claim of the paper (benefit of two-stage procedure) is not supported by empirical results.", "summary_of_the_paper": "This paper proposes a genetic algorithm for constrained molecular optimization. To generate molecules with better properties while staying similar to the starting molecule (lead), this paper proposes a two phase procedure. The first stage (constraint satisfaction) searches for feasible molecules that are similar to the lead compound. The second stage optimizes the objective function (property). The crossover operation is the same as GB-GA (Jensen 2019) and the mutation operation introduces random modifications on SELFIES string representation. The method is evaluated on logP optimization task, with minor improvement over previous work GA-DNN (Table 1).", "summary_of_the_review": "I vote for rejection. The method itself is not novel because it is a standard application of genetic algorithm. The results show that the proposed method did not outperform existing baselines. The main claim of the paper (benefit of two-stage procedure) is not supported by ablation study.", "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": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "Not applicable"}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper describes a genetic algorithm for molecular optimization under constraints. The aim is to generate molecules with better properties while close to an initial lead molecule. The proposed approach is a two-stage one. The first stage aims to satisfy constraints and searches for feasible molecules that are similar to the lead. The second stage optimizes the molecular property. The method is evaluated on logP optimization task, with minor improvement over previous work.

The reviewers point out the following strengths and weaknesses:

Strengths:

- Molecular optimization under structural constraints is an important research direction.
- Comprehensive related work section.

Weaknesses:

- Lack of novelty because it is a standard application of genetic algorithm.
- The results show that the proposed method did not outperform existing baselines.
- The main claim of the paper (benefit of two-stage procedure) is not supported by ablation study.
- The authors only conduct experiments on improving LogP, which is a benchmark that is too easy and not challenging.
- The objective function and cross-over operation are the same or very similar to previous work.
- The experimental evaluation is limited, and the overall setting is not very relevant to real-world tasks.

Overall, all reviewers vote for rejection. It is clear that the paper needs more work before it can be published.
Decision: Reject