Efficient Evolutionary Search Over Chemical Space with Large Language Models
Authors: Haorui Wang
Marta Skreta
Cher Tian Ser
Wenhao Gao
Lingkai Kong
Felix Strieth-Kalthoff
Chenru Duan
Yuchen Zhuang
Yue Yu
Yanqiao Zhu
Yuanqi Du
Alan Aspuru-Guzik
Kirill Neklyudov
Chao Zhang
Venue: ICLR 2025 Poster (2025)
Keywords: Large Language Models
Evolutionary Search
Molecule Optimization
AI for Science
Molecular generation
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutionary Algorithms (EAs), often used to optimize black-box objectives in molecular discovery, traverse chemical space by performing random mutations and crossovers, leading to a large number of expensive objective evaluations. In this work, we ameliorate this shortcoming by incorporating chemistry-aware Large Language Models (LLMs) into EAs. Namely, we redesign crossover and mutation operations in EAs using LLMs trained on large corpora of chemical information. We perform extensive empirical studies on both commercial and open-source models on multiple tasks involving property optimization, molecular rediscovery, and structure-based drug design, demonstrating that the joint usage of LLMs with EAs yields superior performance over all baseline models across single- and multi-objective settings. We demonstrate that our algorithm improves both the quality of the final solution and convergence speed, thereby reducing the number of required objective evaluations.

Published as a conference paper at ICLR 2025
EFFICIENT EVOLUTIONARY SEARCH OVER CHEMICAL SPACE WITH LARGE LANGUAGE MODELS
Haorui Wang*', Marta Skreta*2., Cher-Tian Ser?, Wenhao Gao Lingkai Kong', Felix
Chenru Duan Yuchen Zhuang', Yue Yu', Yanqiao Zhu', Yuanqi Du Alán Aspuru-Guzik Kirill Neklyudovi Chao Zhang 'Georgia Institute of Technology, UUniversity of Toronto, Veector Institute. *Massachusetts Institute of Technology, Universitt of Wuppertal, Deep Principle Inc., "University of California, Los Angeles *Cornell University,
"Université de Montréal, Milaa Quebee Al Institute hwang984@gatech.com martaskreta@cs. toronto.commm edu
ABSTRACT
Molecular discovery, when formulated as an optimization problem, presents sig- nificant computational challenges because optimization objectives can be non- differentiable. Evolutionary Algorithms (EAs), often used to optimize black-box objectives in molecular discovery, traverse chemical space by performing random mutations and crossovers, leading to a large number of expensive objective evalua- tions. In this work, we ameliorate this shortcoming by incorporating chemistry- aware Large Language Models (LLMs) into EAs. Namely, we redesign crossover and mutation operations in EAs using LLMs trained on large corpora of chemical information. We perform extensive empirical studies on both commercial and open-source models on multiple tasks involving property optimization, molecular rediscovery, and structure- based drug design, demonstrating that the joint usage LLMs with EAs yields superior performance over all baseline models across single- and multi-objective settings. We demonstrate that our algorithm improves both the quality of the final solution and convergence speed, thereby reducing the number of required objective evaluations.
INTRODUCTION
Molecular discovery is a complex and iterative process involving the design, synthesis, evaluation, and refinement of molecule candidates. This process is often slow and laborious, making it difficult to meet the increasing demand for new molecules in domains such as pharmaceuticals, optoelectronics, and energy storage (Tom et al. 2024). One significant challenge is that evaluating molecular properties often requires expensive evaluations (oracles), such as wet-lab experiments, bioassays, and computational simulations (Gensch et al. 2022 Stokes al. 2020). Even approximate computational evaluations require substantial resources (Gensch et al. (022). Consequently, the development of efficient algorithms for molecular search, prediction, and generation has gained traction in chemistry to accelerate the discovery process. These advancements in computational techniques, particularly machine learning-driven methods, have facilitated the rapid identification and proposal promising
molecular candidates for real-world experiments (Kristiadi et al. 2024 Atz al. 2021 2024 Nigam et al. 2023).
Du et al.
Several current approaches used to generate molecular candidates are based on Evolutionary Al- gorithms (EAs) (Holland) 1992), which do not require the evaluation of gradients and are thus well-suited for black-box objectives in molecular discovery. However, major downside is that they generate proposals randomly without leveraging task-specific information. Consequently, producing reasonable candidates requires numerous evaluations of the objective function. limiting the practical application of these algorithms. Thus, proposals generated by operators that incorporate task-specific information can help reduce the number of evaluations required optimize the objective function.
"Equal first-autorshhpp Equal senhor-authorship

---

Published as a conference paper at ICLR 2025
Matiin :PUTATION MODE
.CROSSOVER MODE
Initial pool ralecules
Kating selection
2I hara moletsles czea. Propona
Final population
TEDMINATOO
3505
Dracle scoring
(ENCSON
V.
molecules to pool
binding
Update: molecule_pool.
Candidate genezation: Attext_pronpt, solecule_podl
y_t]
9.90
Black-box ozacle: 1(L
Figure 1: Overview of MOO.EO. Given an initial pool of molecules, mates selected using default Graph-GA (Jensen, 2019) heuristics and converted to SMILES or SELFIES strings. LLMs then function as mutation or crossover operators, editing the molecule string representations based on text prompts that describe the target objective(s). The offisring molecules are then evaluated using an oracle, and the best-scoring ones are passed to the next generation. This process repeated until the maximum number of allowed molecule evaluations is performed.
Natural language processing (NLP) has increasingly been utilized to represent molecular struc- tures (Chithrananda et al., Schwaller et al. 2019/Oztüür Öztürk et al. 2020) and extract chemical knowledge from literature Tshitoyan an et al. (2019). The connection between NLP and molecular systems is facilitated by molecular representations such as the Simplified Molecular Input Line Entry System (SMILES) and Self Referencing Embedded Strings (SELFIES) (Weininger 1988 Daylight Chemical Information Systems 2007| Krenn et al., 2020). These methods convert 2D molecular graphs into text, allowing molecular structures to be represented in the same modality as their textual descriptions.
Recently, the performance of Large Language Models (LLMs) has been investigated in several chemistry-related tasks, such as predicting molecular properties (Guo al.
2023b; Jablonka et al.)
2024). retrieving optimal molecules (Kristiadi et al., 2024 Ramos et al.)
2023] Ye et al.) (023).
automating chemistry experiments Bran al. (2023); ) Boiko al. (2023) ; Yoshikawa et al. (2023);
Darvish et al. (2024), and generating molecules with target properties FFlam-Shephee & Aspuru Guzik] 2023 Liu al. 2024 Ye al. 2023). Because L.LMs have been trained on large corpora of text that include a wide range of tasks, they demonstrate general-purposs language comprehension as well as knowledge of basic chemistry, making them interesting tools for chemical discovery tasks (White, 2023). However, many LLM-based approaches depend on in-context learning and prompt engineering (Guo et al. 2023b). This can pose issues when designing molecules with strict numerical objectives, as LLMs may struggle to satisfy precise numerical constraints optimize for specific numerical targets (A14Science & Quantum 2023). Furthermore, methods that solely depend on L.MM prompting may produce molecules with lower fitness due a lack of physical grounding, or they may produce invalid SMILEE that cannot be decoded into chemical structures (Skinnider) 2024).
In this work, we propose Molecular Sannuugge-Enaanee Evolutionary Optimization (MOLLEO), which incorporates LLMs into EAs to enhance the quality of generated proposals and accelerate the optimization process (see Figure 1}. MOLLEO leverages LLMs as genetic operators to produce new proposals through crossover or mutation. To our knowledge, this is the first demonstration of how LLMs can be incorporated into EA frameworks for molecular generation. In this work, we consider three LLMs: GPT-4 (Achiam et al.) 2023). BioT5 (Pei et al., (023). and MoleculeSTM (MolSTM) (Liu al. 2023b). We integrate each LLM into separate crossover and mutation procedures, justifying our design choices through ablation studies. We empirically demonstrate the superior performance of MOLLEO across multiple black-box optimization tasks, including single-objective and multi- objective optimization. For all tasks, including more challenging ones like protein-ligand docking, MoLLEO outperforms the baseline EA and other optimization algorithms based on reinforcement learning (RL) and Bayesian Optimization (BO). To further illustrate how our model can be used in novel molecular discovery settings, we show that MOLLEO can improve on the best existing JNK3 inhibitor molecules in ZINC 250K (Sterling & Irwin 2015)
2

---

Published as a conference paper at ICLR 2025
2
RELATED WORK
2.1 MOLECULAR OPTIMIZATION
The molecular design field, encompassing multiple fundamental problems in chemistry, has developed numerous methods. In general, all the existing approaches define the space of possible molecular structures and run combinatorial search to find the molecule with the target properties. Namely, conventional methods include Monte Carlo Tree Search (MCTS) (Yang et al. 2017), Reinforcement Learning (RL) (Olivecrona et al., 201 7a| Guo & Schwaller (023). and Genetic Algorithms (GA)
(Jensen 2019 Fu et al. [021] Nigam et al. 2022 Fu et al. (022).
Due to existing challenges such as searching through exponentially large chemical space and evaluat- ing expensive objectives (Bohacek et al., 1996] [Stumpfe & Bajorath (012), conventional algorithms have recently recoursed to machine learning techniques, especially generative modeling (Du et al. 2024). Generative models learn probability distribution of the observed data which can be later used to propose new molecular structures, thereby concentrating the search space around valid molecular structures. Depending the type of the data and necessary properties for the search algorithms, different generative models have been considered: autoregressive models (ARs) (Popova et al. 20199]
Gao et al. 2021 variational autoencoders (VAEs) (Gómez-Bombarelli al. 2018 Jin et al. 2018).
flow-based models Madhawa et al. 2019); Shi et al. (2020), diffusion models Hoogeboom et al. (2022) Schnnuing et al. (2022)
Despite concentrating the search space around valid molecules by the usage of generative modeling, the optimization of necessary properties can remain infeasible. To narrow down the search space further, one can consider the conditional generative modeling, where the molecular structures are sampled from the conditional distribution having some predefined properties (.ómez-Bombar et al. 2018 Griffiths & Hernandez-Lobatt) 2020; Zang & Wang] [2220] Du et al.] 20022 [Wi et al. 2024). this paper, we demonstrate the use chemistry-aware LLMs as conditional generative models that improve the efficiency of combinatorial search in the molecular space.
2.2 LANGUAGE MODELS IN CHEMISTRY
LLMs have been widely investigated for their applicability in scientific domains (Achiam et al. 2023 A14Science Quantum 2023), as well as their ability to leverage chemistry tools for chemical dis- covery and characterization (Bran al. 2023 Boiko et al. 2023). Several works have benchmarked LLMs such as GPT-4 on chemistry tasks and found that while LLMs can outperform human chemists in some zero-shot guestion-answeringg settings, they still struggle with chemical reasoning (Mirza et al. 2024 Guo et al. 2023b). Several smaller, open-source models have been trained or fine-tuned
specifically chemistry text (Taylor al. 2022 Christofidellis al. 2023
Pei al. 2023).
Recently, language models have also been used to guide a given input molecular structure towards specific objective properties; widely-used term used for this molecular editing (Liu et al. 2023b Ye al. 2023). Modifying structures towards specified properties is important so that they can satisfy potentially many required criteria, requirement in pharmaceutical development where molecules need to be non-toxic and effective against their target (among other things), or in battery design, where molecules need have a large energy capacity and long lifespan. In this paper, we focus on molecular optimization to find molecules with desired properties, rather than editing. For interested readers, we provide additional related works about how LLMS have been combined with EAs for code and text generation, as well as benchmarking LLMs in chemical tasks in Appendiix A.I
3 THE MOLLEO FRAMEWORK
3.1 PROBLEM STATEMENT
Black-box optimization. optimization problem
Molecule discovery with a given property can be formulated as an
m = arg max F(m) MEM
(1)
3

---

Published as a conference paper at ICLR 2025
where is molecular structure and M denotes the set of valid molecules constituting the entire chemical space. The objective F(m)= M - R is a black-box scalar-valued function that measures a certain molecule property m.
The measurement of chemical properties can involve complicated simulations or in vivo experiments, making it impossible evaluate the gradients of the objective function F. Additionally, we assume that the main computational expense the optimization procedure comes from the objective eval- uation (oracle call). Therefore, we design algorithms to minimize the number of oracle calls and compare all the algorithms with the same call budget.
Multi-objective black-box optimization. Oftentimes, molecules need to meet multiple, potentially competing objectives simultaneously. Multi-objective optimization aims to find the Pareto Pareto-optimal solution, where none of the objectives can be improved without deteriorating any of them (Lin et al., (2022). The naive approach to optimize given objectives (Fi(-)) jointly is to consider an aggregate objective, such as the sum of all individual objectives, i.e.
Σ w,Fi(m,
m
arg max MEM
where W/ is the weight of i-th objective, which can be considered hyperparameter. However, determining the weight of each objective function might be nontrivial (Kusanda et al.) (002).
The rigorous approach to multi-objective optimization is the introduction of partial order and consid-
ering the solutions from the Pareto frontier (Geoffrion) 1968 Ekins al. (0100). In this context, the
partial order is defined by comparing all the objectives (Fa(-)) J== for the given molecules, i.e., m'
surpasses m every objective evaluated on m' is greater than the same objective evaluated on m (assuming the maximization of objectives). Formally,
m' ž m
> Vi F((m') 2 F(m)..
For the given set of molecules { (my 1m I the Pareto frontier P(S) is defined as the set of non-dominated solutions. Namely, for every molecule m E P(s) there is no other molecule in S surpassing i.e.
P(S) {m E S:: (m' E S m' m, m' m] 20)
When jointly optimizing several objectives, we use the Pareto frontier to select candidates during the evolutionary search and compare algorithms. Namely, assuming that the objectives are bounded (e.g.. F(-) [0, 1)), one can compare two Pareto frontiers by evaluating their hypervolume
Volume (PSS)) Volume (UmeP(e) H(m)), H(m) far E [0. 1J" < F.(m), (5)
where (m) is the hyperrectangle associated with the objectives evaluated on molecule m, and Volume(-) evaluates the Euclidean volume of the input set.
3.2 EVOLUTIONARY ALGORITHMS
an evolutionary algorithm that operates as follows. An initial pool of molecules is randomly selected, and their fit- nesses are calculated using a black-box oracle, F(-). Two parents are then sampled with a probability proportional to their fitnesses and combined using a CROSSOVER operator to generate an offspring, followed by a random MUTATION with probability Pm- This process is repeated sum_crossover times, and the children are added the pool of offspring. Finally, the fitnesses of the ofispring are measured using F(-) and the offspring are added to the population. For single-objective optimization, the ne fittest members from the population at a given step are selected to pass on to the next generation. For multi-objective optimization, two strategies are investigated: (1) Objective summation, where the summation of individual objectives is used as a single objective, and the ne fittest members are retained; and (2) Pareto set selection, where only the Pareto frontier of the current population is kept. This process is repeated until the maximum allowed oracle calls (oracle budget) have been made. This process is outlined in Algorithm I
We build our MOL.EO framework upon the Graph-GA algorithm (Jensen 2019)
We incorporate chemistry- aware LLMs into the structure of Graph -G by using them as proposal generators at CROSSOVER and MUTATION steps. That is, for the CROSSOVER step, instead of randomly
4

---

Published as a conference paper at ICLR 2025
Algorithm 1: MOI.EEO Algorithm
Data:
the iittill pool Mlg: the objective F; the population size nei the number of offspring n.-
Result: Optimized molecule population M begin
for E Relo do
L Compute F(m);
for oracle__ _udgeet] do
offspring = []:
for num_crossovers do
sample mo, m. from M, proportionally objective value F(m);
M. + sorted(Mi::;
for i [1, [L,nummmttationss do
offspring + (erccccfsssc(fscnnnn n.]
smallest Tanimoto distance to M,
M, + offsprnnnn for E Ml do
Compute F(m):
Task__ type
then
M, < sorted(Mi.) ne]:
else
M, t Pareto_Frontier((M[))
Return Mr;;
combining two parent molecules, generate molecules that maximize the objective fitness function guided by the objective description. For the MUTATION step, the operator mutates the fittest members of the current population based on the target description. However, we noticed that LLMs do not always generate candidates with higher fitness than the input molecule (demonstrated in Appendix C.I). and so we constructed selection pressure filter edited molecules based on structural similarity to the top molecule (Nigam et al. 2022). That is, we sort the existing population by fitness, apply mutation to the top population members, and then add them the pool of offspring. Then, we prune the pool by selecting the n. most similar offssring to the fittest molecule in the entire pool based on Tanimoto distance. We ablate the impact of this filter in Appendix C.2
For each LLM, we describe below the details of how implement the CROSSOVER and MUTATION operators. We empirically studied different combinations models and hyperparameters (Ap pendix C.2 and in what follows, we describe the operators that resulted in the best performance.
Graph-GA
The baseline algorithm that we build upon and compare against in our experiments.
CROSSOVER; (default Graph-GA GA crossover): Two parent molecules are sampled with a probability proportional to their fitness. Crossover takes place ring non-ring position with equal likelihood. Parents are cut at random positions into fragments, and then fragments from both parents are combined. Invalid molecules are filtered out, and randomly spliced molecule is returned Jensen (2019).
& MUTATION: (default Graph-GA mutation): Random operations such as bond insertion or deletion, atom insertion deletion, bond order swapping, atom identity changes done with predetermined likelihoods Jensen 2019).
MOLLEO (GPT-4) GPT-4 a proprietary LLM trained on a web-scale text corpus.
> CROSSOVER: Two parent molecules are sampled the same way in Graph-GA. GPT-4 is then prompted to generate an offspring with the template tim. "I have two molecules and their prrrgt__objectvve scores: (8in,0, fo), (sin, fi). Propose new molecule wit th higher porrget_bbjectiee] by making crossover and mutations based on the given molecules." where Sin. is input SMILES and fz is its fitness score. This prompt template is similar to those found in Al4Science Quantum (2023); all prompts can be found in An/Appendix E We then obtain edited SMILES molecule output: Sout GPT-4(1; If Sout cannot be
5

---

Published as a conference paper at ICLR 2025
decoded to valid molecule structure, generate an offspring using the default crossover operation from Graph-GA GA. We demonstrate the frequency of invalid LLM edits in Apppendix C.T.
MUTATION: While GPT-4 performs well as MUTATION operator when paired with GPT-4 CROSSOVER (Appendix C.2), we found that the default Graph-GA mutation achieves comparable performance with fewer LLM queries. Therefore, we opt use the default Graph-GA mutation here.
MOLLEO (B1oT5) BioT5 was developed with a two-phase training process using a baseline T5 model (Raffel et al. 2020). Initially. the model was trained on molecule-tex data (339K samples). SELFIES structures, protein sequences, and general scientific text from multiple sources (Pei et al.. 2023) using language masking as training objective. Following this, the model was fine-tuned on specific downstream tasks, including text-based molecular generation, where molecules are generated given an input description (Edwards et al. 2022).
> CROSSOVER: We use the default Graph-GA crossover.
MUTATION: For the top Y molecules in the entire pool, we mutate them by prompting BioT5 with the template in = "Definition: You are given a molecule SELFIES. Your job is to generate SELFIES mol molecule that [target_obs [targgt_objective]]] Now complete the following example Input: <bom> <bo ]<om> Output", where 4ix is the SELFIES representation of a molecule. These prompts have the same format those proposed in Pei al. (2023); exact prompts for all tasks are in[Appendix E] We then obtain an edited SELFIES molecule as an output: lowt = BioT5 (tam). We transform Lout back to the SMILES representation and add it to the pool of offspring. Since SELFIES can always be decoded into molecular structure, there are no issues with BioT5 generating invalid molecules. With X offspring produced from erossover and offspring from the editing procedure, we select the top n. offspring overall. This selection is based on structural similarity determined using Tanimoto distance to the fittest molecule the entire pool Nigam et al. (2022).
MOLLEO (MOLSTM) MoleculeSTM was developed by jointly training molecule and text encoders molecule-text pairs from PubChem using contrastive loss, which maximizes the embedding similarity of each pair (Liu et al.] (023b). To enable molecular editing, they implemented simple adaptor module to align their molecule encoder with the encoder of a pre-trained generative model. This alignment allowed them to utilize the generative model's decoder for structure generation.
CROSSOVER: We the default Graph-GA crossover.
& MUTATION: For the top Y molecules in the entire pool, we edited them by following a single text- conditioned editing step from (Liu et al. 2023b). Given the MoleculeSTM molecule and text encoders (E and Ere, respectively). ) pre-treined generative model consisting of an encoder EMg and decoder Day (Irwin et al., 2022), and an adaptor module (Age) to align embeddings from Ese and EMg. an input molecule SMILES (smn) is edited towards a text prompt describing the objective by updating the embedding from Exg. First, the molecule embedding 2o is obtained from Emg (s Then, Zn is updated using gradient descent for T iterations:
I++1 = Iz a C(Tr),
(6)
where a is the learning rate and [(xx) defined as:
C(xx) ----coosnnesste (rr
+ Al|z, zoll2-
(7)
controls how much the embedding at iteration can deviate from the input embedding. Finally, Fr is passed to the decoder DMg generate molecule SMILES Sout- The text prompts follow the format "This molecule (has/is/other exact prompts for all tasks are [Appendix E We ablate MolSTM hyperparameter selection in Appendix C.4 If Sout cannot be decoded into valid molecule (see Appendix C.T, we edit the next best molecule (so that we have Y offspring after the editing has finished). Similarly MOLLEO (BIOT5), we combine the X crossover and Y mutated offspring and select the n. most similar molecules to the top molecule overall to keep.
verb property)", which follows Liu et al. (2023b);

---

Published as a conference paper at ICLR 2025
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
We evaluate MOLLEO on 26 tasks from two molecular generation benchmarks, Practical Molecular Optimization (PMO) (Gao et al. 2022) and Therapeutics Data Commons (TDC) (Huang et al. 2021).
Exact task definitions can be found in TDC We organize the tasks into the following categories:
1. Structure-based optimization, which optimizes for molecules based on target structures. It includes isomer generation based on target molecular formula disomers_c7h8n22 isomers_c9hn @n2o2pf2c1) 10mmerrs_ch100222222211 and two tasks based on matching or avoiding scaffolds and sub- structure motifs (deco_hop, scaffold_hop valsartan_smartss
Name-based optimization. These tasks involve finding compounds similar to known drugs (Mes- tranol, Albuterol, Thiothixene, Celecoxib, troglitazone) and seven tasks (MPO) that aim to rediscover drugs (Perindopril, Ranolazine, Sitagliptin, Amlodipine, Fex- ofenadine, Osimertinib, Zaleplon) while optimizing for other properties such hydrophobicity (LogP) and permeability (TPSA). Two tasks, median1 and medi aim to generate molecules with properties similar to several known drugs simultaneously. Successfully completing these tasks means that LLMs can make perturbations toward desired molecules when given a chemical optimization goal.
optimization
Property optimization. We first consider the trivial property optimization task QED (Bickerton let al. 2012), which measures the drug- likenesss of a molecule based on a set of simple heuristics. We then focus on the three tasks that measure a molecule's s activity against the following proteins: DRD2 (Dopamine receptor D2), GSK38 (Glycogen synthase kinase-3 beta), and JNK3 (c-Jun N-terminal kinase-3). For these tasks, molecular inhibition is determined by pre-trained classifiers that take in a SMILES string and output value p E [0. 1], where p 2 0.5 predicts that molecule inhibits protein activity. Finally, we include three protein- ligand docking tasks from TDC (Graff) iet al.) (02T) (also referred to structure- based drug design (Kuntz) (1922), ), which are more difficult tasks closer to real- world drug design compared to simple physicochemical properties (Cieplinski et al. 2020). The proteins we consider are DRD3 (dopamine receptor D3, PDB ID:: 3PBL), EGFR (epidermal growth factor receptor, PDB ID: 2RGP), and Adenosine A2A receptor (PDB ID: 3EML). Molecules are docked against the protein using AutoDock Vina (Eberhardt et al. 2021), with the output being the docking score of the binding process.
To evaluate method, we follow (Gao et al.) (022) and report the area under the curve of top-k average property values versus the number of oracle calls (AUC top-k), which takes into account both the objective values and the computational budget spent. For this study, we set k 10 in order to identify a small, distinct set of top molecular candidates. For the multi-objective optimization, we consider two metries: top-10 AUC for summing all optimized objectives and the hypervolume of the Pareto frontier (see (seelEquatin) Equation (5)).
For baselines, we use the highest three ranking models from the PMO benchmark (Gao et al. 2022 including REINVENT (Olivecrona et al. (0017)), an RNN that utilizes a reinforcement learning based policy to guide generation; Graph-GA: and Gaussian process Bayesian optimization (GP BO) (Trpp et al. 2021), where a GP acquisition function is optimized with methods from Graph-GA. We also include Augmented Memory (Guo Schwaller 2024), which combines data augmentation with experience replay to enhance the reinforcement learning-boo based policy for guiding generation, as well as Differentiable Scaffolding Tree for Molecule Optimization (DST, (Fu et al., 202T)), which optimizes molecule structure using gradient ascent in the latent space of graph neural network trained to predict a target property.
For the initial population of molecules, randomly sample 120 molecules from ZINC 250K (Sterling & Irwin 2015). In all runs, we restrict the budget of oracle calls to 10, 000 but terminate the algorithm early if the average fitness of the top-100 molecules does not increase by 10-3 within epochs, as was done in (Gao et al. 2022). For the docking experiments, we restrict the budget to 1000 calls due to higher evaluation costs. Additional experimental details and the choice of hyperparameters are provided in Appendix B

---

Published as a conference paper at ICLR 2025
MoILEOXGPT-4)
MoILEO(BIOTS)
MoILEOXMoISTM;
Graph-GA
0.0
0.2
0.4
0.6
0.8
1.0
Fitness
pop
2000 oracle calls Random crossaver/mutation, one round
LLM editing round
1000 orade
4000 oracle calls
Figure 2: Population fitness over increasing number of iterations for JNK3 inhibition. In the lightest blue, we plot the fitness distribution of the initial molecule pool. We then pass the molecules through a single round of LLM edits (pink curve), or a single round of random crossover/mutaaion operations (yellow curve). We then show the fitnesses of the top-10 10 molecules after 1000-4000 oracle calls.
Table 1: Top- 10 AUC of single-objective tasks. The best model for each task is bolded and the top three are underlined. We also report the sum of all tasks (total) and the rank of each model overall.
Task type
objective (t)
Methad
REINVENT
Augonented Memory
Graph GA
GP
MoLLEO (MolSTM)
MoLLEO (BioT5)
(GPT-4)
QED
0.941
0.000
0,941 0.000
0,937 0,000
0.937 = 0.002
0.37 0.002
0.948 0,000
Property oplinization
INK3
0.781
0
0.153
0.155
0.643 0.236
0.728 0.079
0.027
DRD2
0.945 + 0.007
0.962 a 0.005
0.964 = 0.012
0.923 0.011
0.975 0.000
0 + 0.002
0.968
1068+0..00 0.012
GSK33
0.865 0.013
0.027
0,788 0.070
0.001
0.889 0.015
0.087
mestranol similarity
1.61810.008
0,26: 0.035
0.579
0.627 a 0.088
0.596 a 0.018
0.111
0.972 0,009
albuterol_ similarit
0.896=0.00
0 0.026
0.874 0.020
0,902 0,019
0.929 e 0.005
0.908 + 0.003
0,965 0.024
optimization
Name-hased
thiothisene rediscovery
0.534.0000
0.562 0.002
0.179 1: 0.025
0.559 a 0.027
0.508 : 0.035
0 0.081
0.727 0.052
velecuxib rediscuvety
0.716 0.084
0.784.00001
0,582 0.057
0,728 0.048
0.594 0.105
0. 0.508 0.017
0,864 0,034
trogitazznee rediscovery
0.452 n008
0.377 0.010
0.405 0.007
0.381 0.025
0.044
perindopril_ mpo
0.537 + 0.016
0.598 + 0.008
0.538 0.009
0.493 = 0.011
0.554 0.057
0.738 = 0.016
0.000 0.031
ronoazinee mpo
0.760 0.000
018012
0.728 0.012
0.725
0.760 0.022
sitaglipci__mp mpo
0.021 + 0.003
0.479 + 0.09
0.433 = 0.075
0.186 0.055
0.548 = 0.065
0.506 0.100
0.584 0.067
amlodipine_mpo
0.642 0.044
0.686 0.046
0.625 0.040
0.552 0.025
0.674 0.018
0,776 0.038
0 0.032
fexofunadine_ mpo
0,769 1: 0.009
0.686 1 0.010
0,779 0.025
0.745 1 0.009
0.789 a 0.016
0.773 1 0.017
0.847 0,018
osimettinih mpo
B.34 e 0.06
0.856 e 0.013
0.808 0.012
0,762 : 0.029
0.823 0.007
0.817 a 0.016
0.355 010074
zaleplon mpo
0.347 0.01
0.02
0.156 0.007
0,272
0:475
0.026
0,510:
medianl
0.372 0.015
0,335
1 0.012
0,287 0.008
0,325 a 0.012
0.298 a 0.019
0.358 1 0.033
0.252 0.024
median2
0.294 0.06
0.290
0.229 0.017
0.034
0.251 0.851
0.259 0.019
0.045
isomen, c7hh82202
0.842 + 0.029
0.949 + 0.016
0.662 + 0.071
0.948 = 0..16
0.928 + 0.038
0.984 0.00N
bomers, (ithhhhh0a 0n2222p/2el
0.642 0.054
0.30
1.016
0.719 0.017
0,169 0.188
0.871 0.039
0.873 1 0.019
0.574 a 0,053
bosed optimization
deen hop
0.666
0.04
0.688 0.000
0.619 0.004
0.629 0.018
0663 0.016
0.827 0.093
0.942 11:113
scafiald_hr hop
0.560 + 0.019
0.366 + 0.008
0,517 = 0.007
0.548 = 0.019
0.527 = 0.019
0.559 0.102
0.971 0.004
aabartan_smart
0.000 0.000
0.000
0.000
0.000
0.000
0.000
Total (f)
14.036
15.356
13.823
13.182
14.557
15.424
17,862
Rank (1)
4.2 EMPIRICAL STUDY
First, we motivate the idea of why incorporating chemistry-aware LLMS in GA pipelines is effective. In Figure 2] we show the fitness distribution of an initial pool of random molecules inhibiting JNK3. We then perform a single round of edits to all molecules in the pool using each LLM and plot the resulting fitness distribution of the edited molecules. We find that the distribution for each LLM shifts to slightly higher fitness values, indicating that LLMs do provide useful modifications. However, the overall objective scores are still low, so single-step editing not sufficient. We then show the fitness distributions of the populations as the genetic optimization progresses and find that the fitness in- creases to higher values on average, given the same number of oracle calls. We show the performance of direct LLM querying versus the optimization procedure for additional tasks in Appendix C.I
The results of single-objective optimization across 23 tasks in PMO are shown in Table reporting the AUC t0p-10 for each task and the overall rank of each model. We show the performance of additional baselines in AAppendix D.I The results indicate that employing any of the three LLMs we tested as genetic operators improves performance over the default Graph-GA. Notably, MOLLEO (GPT-4) outperforms all models in 15 out of 23 tasks and ranks first overall, demonstrating its utility in molecular generation tasks. MOL. MOO (BIOT5) achieves the second-hest results out of all the
8

---

Published as a conference paper at ICLR 2025
DRD3
EGFR
Adenosine
-7.0
8.0
-50
Docking score
9.0
-90
10.0
9.0
-10.0
12.0
-1.0
11.0
120
-1200
120
13.0
-300
14.0
11.00
-14.
14.0
-16.0
200
400
6:00
800
1000
0
200
400
600
800
1000
0
200
400
600
800
1000
Number of orade calls
Number of oracle calls
Number oracle calls
Graph-GA
MoILEO(BIOTS)
MoILEOIGPT-4)
MOILEO(MOISTM)
Convergence reached
Figure 3: Average docking score of top-10 molecules when docked against DRD3, EGFR, or Adenosine A2A receptor proteins. Lower docking scores are better. For each model, we show the convergence point (the moment of stabilization of the population scores) with a star, if the model converges before 1000 oracle calls have been made. Here, the model is considered to have converged if the mean score of the top 100 molecules does not increase by at least le-3 within 5 epochs.
models tested, obtaining a total score close to that of MOLLEO (GPT-4), and has the benefit of being free to use. We observe that MOLLEO (B1oT5) generally performs better than MOLLEO (MOLSTM), producing a higher percentage of molecules with improved fitness after editing, as shown in Appendix C.1 For the tasks deco_hop and scaffold_ hop, there is only small gain for the open-source MOLLEO models. We speculate that this is because these models have not been trained on molecular descriptions containing SMARTS patterns. Also, it is unelear how well these models perform with negative matching (c.g., This molecule does not contain the scaffold [#7]-cIn[c;h1]]] h1; [c; (C;
We were also interested in knowing whether the open-source models were generating molecules that could have been seen during training. We took ZINC20 (Irwin et al. 2020), a database of 1.4 billion compounds that were used to generate the training set for T5 and PubChem (Kim et al., (~250K molecules), which was used to generate the training set for MoleculeSTM, and checked if the final molecules for the JNK3 task from each model appeared in the respective datasets. We found that this was not the case; there was no overlap between the generated molecules and the datasets.
We demonstrate empirically that MOLLEO algorithms consistently converge faster than all the considered baselines, i.e., for any given budget oracle calls, MOLLEO achieves better objective values (see [Appendix C.3). This is important when considering how these models can translate to real-world experiments to reduce the number of experiments needed to find ideal candidates. We also study the computational cost of MOL.EE in Appendix D.4
In Figure 3, we present results for more challenging protein-ligand docking tasks, which better approximate real-world molecular generation scenarios compared to those in Table I We plot the average docking scores of the top-10 10 best molecules for MOLLEO and Graph-GA against the number of oracle calls. We observe that nearly all LLMs in MOLLEO generate molecules with lower (better) docking scores than the baseline model for all three proteins, and they converge faster to the optimal set. Among the three LLMs, MOLLEO (B10T5) achieves the best performance. Surprisingly, MoI.LFO (GPT-4) performs worse than Graph-GA in the Adenosine A2A receptor docking task. In practice, better docking scores and faster convergence rates could result in requiring fewer bioassays to screen molecules, making the process both more cost- and time-effective. We visualize the top-10 molecules found by MOLLEO in EGFR docking and deco_hop tasks in Appendix D.8]
In Table 2. we show the results of our multi-objective optimization for three tasks. Tasks and 2 are inspired by goals in drug discovery and aim for simultaneous optimization of three objectives: maximizing molecule's 's QED, minimizing its synthetic accessibility (SA) score (meaning that it is easier to synthesize), and maximizing its binding score to either JNK3 (Task 1) or GSK3,3 (Task 2). Task 3 is more challenging as it targets five objectives simultaneously: maximizing QED and JNK3 binding, as well as minimizing GSK33 binding, DRD2 binding, and SAScore. We find that MOLLEO (GPT-4) consistently outperforms the bascline Graph-GA in all three tasks in terms of hypervolume and summation. In Table 2 we see that the performance of open-source LLMS degrades when introducing multiple objectives into the prompt. We speculate that this performance drop may come from their inability to capture large, information- -ense contexts. We further assess both the

---

Published as a conference paper at ICLR 2025
Table 2:
Summation and hypervolume scores of multi-objective tasks. We report the results for two aggregation methods: Summation (Sum) and Pareto optimality (PO). Sum(AUC) refers to the summation of top-10 AUC for all optimized objectives. The best results for each task are bolded.
Task 3: QED (1), JNK3 (f), SAscore (JJ,SSK33 (!). DRD2 (L)
Task 1: QED (1), JNK3 (1).
Task 2: QED (t). GSK30 (t). SAscore ())
SAscore
Aggregate
Model
Sum(AUC)
Hypervolume
Sum(AUC)
1lypervolume
Sum(AUC)
Ilypervolume
objective
Graph-GA
19677 0.088
0.713 + 0.083
2.186 0.069
0.719 0.055
3.856 0.075
0.162 0.048
Sum
MOLLEO (MLLSTM)
2.177 + 0.178
0.625 + 0.162
2.349 0.132
0.303 0.024
4,040 + 0.097
0.474 + 0.193
MOLLEO (BioTS)
1.966 0.222
0.592 0.19
2.306 0.120
0.693 0.093
3.904 0.092
0.266 0.201
MoLLEO (GPT-4)
2.367 + 0.044
0.752 # 0.085
2.543 = 0.014
0.832 + 0.024
4.017 + 0.048
0.606 + 0.086
Graph-GA
2.150
0.603 0.082
2.339 0.139
0.640 0.034
4,051
0.606 0.052
MoLLED (MoLSTM)
2:234 a 0.246
0.472 a: 0.248
2.340 =: 0.254
0.202 a 0.054
3.989 a: 0.145
0.381 +: 0. 0.204
PO
MOLLEO (B10T5)
2.325 0.164
0.630
2.299 0.203
0.645 0.127
3.946
0.177
MoLLEO (GPT-4)
2,482 a 0.057
0.727 + 0.038
2.631 = 0.023
0.820 a 0.024
4,212 a 0.034
0.696 + 0.029
Table 3: Initializing MOLLEO with the best molecules from ZINC 250K (Sterling & Irwin 2015). The results of three different I.I.Ms in MOLLEO and Graph-GA are compared. For all molecules in ZINC 250K, we run the JNK3 oracle and select the top 120 molecule pool. We run MOLLEO initializing from this pool of molecules and optimizing JNK3. We report the top- 10 AUC on the output of MOLLEO.
Model
JNK3 Top- 10 AUC
Initial fitness
0.373=0.079
Graph-GA
0.787=0.035
MOLLEO (MOLSTM)
0.815=0.048
MOLLEO (BIOTS)
0.79910.036
MOLLEO (GPT-4)
0.844=0.052
structural and objective diversity of the Pareto optimal sets across all tasks, and we visualize these sets in objective space for MOLLEO and Graph-GA on Tasks and (see (see Apppeddix D.7).
Given that the goal of EAs is to improve upon the properties of an initial pool of molecules and discover new molccules, we showcase these abilitics by generating a set of molecules with higher objective values than the best-known molecules from ZINC 250K (Sterling & Irwin 2015). That is, we initialize the molecular pool with the best molccules from ZINC 250K and run the optimization with MoLLEO and Graph-GA. We report the t0p-10 AUC on the JNK3 task in Table 3 and find that MOLLEO algorithms are consistently able to outperform the baseline model and improve upon the best values found in the existing dataset. We briefly investigate the use of retrieval augmented search in Apppeddix C.5)and and find that incorporating information from existing databases is helpful. To further validate the effectiveness of the LLM-base genetic operators, we compare the molecules before and after LLMs' editing in Appendix D.5 and check whether the optimization objectives are in LLMS training data in Appendix D.6) We also incorporate MOLLEO into other GAs and generative models to validate its generalization capability in Appendix D.2 and Appendix D.3
the
5 CONCLUSION
Herein, we propose MOLLEO: the first demonstration of incorporating LLMs into evolutionary algo- rithms for molecular discovery. We show that chemistry- aware LLMs can serve as informed proposal generators, resulting in superior optimization performance across multiple molecular optimization benchmarks, including pigtein-ligand docking. Furthermore, we show that both open-source and com- mercial versions of MOLLEO can be used in scenarios that involve numerous objective evaluations and can generate higher-ranked candidates with fewer evaluation calls compared to bascline models. Because the structural perturbations of MOLLEO are more effective than random perturbations in a genetic algorithm, it will become more feasible to deploy oracles that are computationally more ex- pensive but more accurate in representing the target property, generating candidates that show greater promise for real-life applications. This is an important consideration due to the high experimental costs of testing candidates. As LLMs continue advance, we anticipate that the performance of the MOLLEO framework will also continue to improve, making MoLLEO promising tool for applications in generative chemistry. We introduce the future work in Appendix A.2
10

---

Published as a conference paper at ICLR 2025
REPRODUCIBILITY STATEMENT
Our code is available at https::ts/// htttt:://ggthhie thub w M20LL/ We provide the experi- mental details and the choice hyperparameters' Section 4.1 and Appendix The pseudocode of MOLLEO algorithm is in Algorithm
7 ACKNOWLEDGEMENT
M.S. thanks Ella Rajaonson for feedback protein-ligand docking and Austin Cheng for discussions on molecule generation. Y.D. thanks Delia Qu for helpful discussions on evolutionary algorithms. A. A.-G. thanks Dr. Anders G. Frøseth for his generous support. A. A.-G. also acknowledges the generous support of Natural Resources Canada and the Canada 150 Research Chairs program.
This work was supported in part by NSF IIS-2008334 IIS-210961,, CAREER IIS-2144338, ONR MURI N00014-17-1-2656, and computing resources from Microsoft Azure. Resources used in preparing this research were also provided, in part, by the Vector Institute and the Acceleration Consortium.
REFERENCES
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, 1lge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et Gpt-4 technical report. arXiv preprint SSIV::2303.877744 2023.
Microsoft Research AI4Science and Microsoft Azure Quantum. The impact of large language models on scientific discovery: a preliminary study using gpt-4. arXiv preprint arXiv:231 1.07661, 2023.
Kenneth Atz, Francesca Grisoni, and Gisbert Schneider. Geometric deep learning on molecular representations. Nature Machine Intelligence 3(12): 1023-102, 2021.
Nikhil Behari, Edwin Zhang, Yunfan Zhao, Aparna Taneja, Dheeraj Nagaraj, and Milind Tambe. A decision-language model (dlm) for dynamic restless multi-armed bandit tasks in public health. arXiv preprini arXiv:2402.1 (2.00..48001 /4807, 2024.
G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins. Quantifying the chemical beauty of drugs. Nature chemistry, 4(2):90-98, 2012.
Regine S Bohacek, Colin McMartin, and Wayne C Guida. The art and practice of structure-based drug design: molecular modeling perspective. Med. Res. Rev. 16(1):3-50, 1996.
Daniil Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes. Autonomous chemical research with large language models. Nature, 624(7992):57--57) 2023.
Andres M Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew White, and Philippe Schwaller. Chemcrow: Augmenting large-language models with chemistry tools. arXiv preprint arXiv:2304.053766 2023.
Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar. Chemberta: Large-scale self- supervised pretraining for molecular property prediction.
Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, and Matteo Manica. Unifying molecular and textual representations via multi-task language modelling. In International Conference on Machine Learning, pp. 6140-6157. PMLR, 2023.
Tobiasz Cieplinski, Tomasz Danel, Sabina Podlewska, and Stanislaw Jastrzebski. We should at least be able to design molecules that dock well. arXiv preprint arXiv:2006. 16955, 2020.
Kourosh Darvish, Marta Skreta, Yuchi Zhao, Naruki Yoshikawa, Sagnik Som, Miroslav Bogdanovic, Yang Cao, Han Hao, Haoping Xu, Alán Aspuru-Guzik, et al. Organa: A robotic assistant for automated chemistry experimentation and characterization. arXiv preprint 2024.
11

---

Published as a conference paper at ICLR 2025
Inc. Daylight Chemical Information Systems. Smarts-a language for describing molecular patterns, 2007.
Kalyanmoy Deb and Himanshu Jain. An evolutionary many-objective optimization algorithm using reference--pint-basse nondominated sorting approach, part i: solving problems with box constraints. IEEE transactions on evolutionary computation, 18(4):577-601, 2013.
Yuanqi Du, Xian Liu, Nilay Mahesh Shah, Shengchao Liu, Jieyu Zhang, and Bolei Zhou. Chemspace: Interpretable and interactive chemical space exploration. Transactions on Machine Learning Research, 2022.
Yuanqi Du, Arian Jamasb, Jeff Guo. Tianfan Fu. Charles Harris, Yingheng Wang. Chenru Duan, Pietro Liò, Philippe Schwaller, and Tom I.. Blundell. Machine learning-aided generative molecular design. Nature Machine Intelligence, June 2024. ISSN 2522-5839. doi: 10.
$42256-024-00843-3 URL/https://doi..rrrr
Jerome Eberhardt, Diogo Martins, Andreas F Tillack, and Stefano Forli. Autodock vina 1.2. O: New docking methods, expanded force field, and python bindings. Journal of chemical information and modeling, (1()))891-3899) 2021.
Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji. Translation between molecules and natural language. arXiv preprint arXiv:2204. 1117, 2022.
Sean Ekins, Dana Honeycutt, and James T Metz. Evolving molecules using multi-objective optimization: applying to adme/tox. Drug discovery today, 15(11-12):51-440, 2010.
Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, and Tim Rock- täschel. Promptbreeder: Self-referential self-improvement via prompt evolution. arXiv preprint arXiv:2309. 2023.
Daniel Flam-Shepherd and Alán Eapuru-Guzie Language models can generate molecules, materials, and protein binding sites directly in three dimensions xyz, cif, and pdb files. arXiv preprint arXiv:2305.05700. 2023.
Tianfan Fu, Wenhao Gao, Cao Xiao, Jacob Yasonik, Connor W Coley, and Jimeng Sun. Differentiable scaffolding tree for molecular optimization. arXiv preprint arXiv:2 122./09914400 10469, 2021.
Tianfan Fu, Wenhao Gao, Connor Coley, and Jimeng Sun. Reinforced genetic algorithm for structure- based drug design. Advances in Neural Information Processing Systems, 15-12325-12335 2022.
Wenhao Gao, Roeío Mercado, and Connor Coley. Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design. arXiv preprint arXiv:2/10006389, 2021.
Wenhao Gao, Tianfan Fu, Jimeng Sun, and Connor Coley. Sample efficiency matters: benchmark for practical molecular optimization. Advances in neural information processing systems, 35: 11342-21357, 2022.
Tobias Gensch. Gabriel dos Passos Gomes, Pascal Friederich, Ellyn Peters, Théophile Gaudin, Robert Pollice. Kjell Jomeer, AkshatKumar Nigam, Michael Lindner-D' Addario, Matthew S Sigman, et al. A comprehensive discovery platform for organophosphorus ligands for catalysis. Journal of the American Chemical Society, ((13)):2205112)) 2022.
AM Geoffrion. Proper efficiencyand the theory of vector optimization. J. Math. Anal. Appl. 22, 1968.
Rafael Gómezz-Bobbrrllli Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobate Benjamín Sänchez-Lengelinng Dennis Sheberla. Jorge aguilera-Iparraguine Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik, Automatic chemical design using data-driven continuous representation of molecules. ACS central science, 4(2):268- 276, 2018.
DE Graff, El Shakhnovich, and CW Coley. Accelerating high-throughpur virtual screening through molecular pose-based active learning, chem. Sci, 12:7866-7881, 2021.
Ryan-Rhy: Griffiths and José Miguel Hermandez-Lobato. Constrained bayesian optimization for automatic chemical design using variational autoencoders. Chemical science, 11(2):577-586. 2020.
12

---

Published as a conference paper at ICLR 2025
Jeff Guo and Philippe Schwaller. Augmented memory: Capitalizing experience replay to accelerate de novo molecular design. arXiv preprint arXiv:2305.16160, 2023.
Jeff Guo and Philippe Schwaller. Augmented memory: Sample-efficient generative molecular design
with reinforcement learning. Jacs Au, 2024.
Qingyan Guo, Rui Wang. Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, and Yujuu Yang. Connecting large language models with evolutionary algorithms yields powerful prompt optimizers. In The Twelfth International Conference Learning Representations, 2023a.
Taicheng Guo, Bozhao Nan, Zhenwen Liang, Zhichun Guo, Nitesh Chawla, Olaf Wiest, Xiangliang Zhang, al. What can large language models do in chemistry? comprehensive benchmark eight tasks. Advances in Neural Information Processing Systems, 36:59662-59688, 2023b.
John H Holland. Genetic algorithms. Scientific american, (6:611) 1992.
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, and Max Welling. Equivariant diffusion for molecule generation in 3d. In International conference on machine learning, pp. 8867-8887. PMLR, 2022.
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf H Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik. Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2021.
John Irwin, Khanh G Tang, Jennifer Young, Chinzorig Dandarchuluun, Benjamin R Wong. Munkhzul Khurelbaatar, Yuri S Moroz, John Mayfield, and Roger A Sayle. Zinc20-a free ultralarge-scale chemical database for ligand discovery. Journal of chemical information and modeling, 60(12)x6065-600 2020.
Ross Irwin, Spyridon Dimitriadis, Jiazhen He, and Esben Jannik Bjerrum. Chemformer: a pre-trained transformer for computational chemistry. Machine Learning: Science and Technology, 3(1): 015022, 2022.
Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, and Berend Smit. Leveraging large language models for predictive chemistry. Nature Machine Intelligence, pp. 1-9, 2024.
Jan H Jensen. A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration chemical space. Chemical science, 10(12):3567-3577 2019.
Wengong Jin, Regina Barzilay, and Tommi Jaakkola. Junction tree variational autoencoder for molecular graph generation. International conference on machine learning, pp. 2323-2332. PMLR, 2018.
Hyeonah Kim, Minsu Kim, Sanghyeok Choi, and Jinkyoo Park. Genetic-guided gilownets for sample efficient molecular optimization. In The Thirth-eighth Annual Conference on Neural Information Processing Systems, 2024.
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, et al. Pubchem 2023 update. Nucleic acids research, 51(D1): D0177-DD3300 2023.
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik, Self- referencing embedded strings (selfies): A 100% robust molecular string representation. Machine Learning: Science and Technology, 1(4):045024, 2020.
Agustinus Kristiadi, Felix Kalthhf,, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik and Geoff Pleiss. A sober look at llms for material discovery: Are they actually good for bayesian optimization molecules? arXiv preprint arXiv:2402.050015 2024.
Irwin D Kuntz. Structure-based strategies for drug design and discovery. Science, 257(3)73): 1078-1082, 1992.
13

---

Published as a conference paper at ICLR 2025
Nathanael Kusanda, Gary Tom, Riley Hickman, AkshatKumar Nigam, Kjell Jorner, and Alan Aspuru- Guzik. Assessing objtti-ojeective optimization of molecules with genetic algorithms against relevant baselines. In for Accelerated Materials Design NeurIPS 2022 Workshop, 2022.
Joel Lehman, Jonathan Gordon, Shawn Jain, Kamal Ndousse, Cathy Yeh, and Kenneth O Stanley. Evolution through large models. In Handbook of Evolutionary Machine Learning, pp- 331-366. Springer, 2023.
Xi Lin, Zhiyuan Yang, and Qingfu Zhang. Pareto set learning for neural multi-objective combinatorial optimization. In International Conference on Learning Representations, 2022. URL https:
Shengcai Liu, Caishun Chen, Xinghua Qu, Ke Tang, and Yew-Soon Ong. Large language models as evolutionary optimizers. arXiv preprint arXiv:23 19046, 2023a.
Shengchao Liu, Weili Nie, Chengpeng Wang. Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, and Animashree Anandkumar. Multi-modal molecule structure-text model for text-based retrieval and editing. Nature Machine Intelligence, 5(12): 1447-1457, 2023b.
Shengchao Liu, Jiongxiao Wang. Yijin Yang. Chengpeng Wang, Ling Liu, Hongyu Guo, and Chaowei Xiao. Conversational drug editing using retrieval and domain feedback. In The Twelfth International Conference on Learning Representations, 2024. URL pettps://opees /openreve/ppeeeview. wtw.sew.wweooooommmiim..
yRrPfKyJQ2
Yecheng Jason Ma. William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Eureka: Human-level reward design via coding large language models. In The Twelfth International Conference on Learning Representations, 2024. /ttttttts////ereeeee http//ppnneviw....m.. wttp:ntt//oommmmdd-earraut
Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, and Motoki Abe. Graphnvp: An invertible flow model for generating molecular graphs. arXiv preprint arXiv: /905. / 2019.
Adrian Mirza, Nawaf Alampara. Sreekanth Kunchapu. Benedict Emockabu, Aswanth Krishnan, Mara Wilhelmi, Macjonathan Okereke, Juliane Eberhardt, Amir Mohammad Elahi, Maximilian Greiner, et al. Are large language models superhuman chemists? arXiv preprint arXiv:2404. 01475, 2024.
AkshatKumar Nigam, Robert Pollice, and Alan Aspuru-Guzik. Janus: parallel tempered ge- netic algorithm guided by deep neural networks for inverse molecular design. arXiv preprint arXiv:2/06.0m 00000.0401.....1. 2021.
AkshatKumar Nigam, Robert Pollice, and Alán Aspuru-Guzik, Guzik. Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design. Digital Discovery, 1(4):390 404, 2022.
AkshatKumar Nigam, Robert Pollice, Gary Tom, Kjell Jorner, John Willes, Luca Thiede, Anshul Kundaje, and Alán Aspuru-Guzik. Tartarus: A benchmarking platform for realistic and practical inverse molecular design. Advances in Neural Information Processing Systems, 36:3263-3306, 2023.
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen. Molecular de-novo design through deep reinforcement learning. Journal of cheminformatics, 9:1-14, 2017a
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen. Molecular de novo design through deep reinforcement learning, CoRR, abs/1704.07555, 2017b. hthhhtt:://axxivorrg
abs//170..072 07555
Hakime Öztürk, Arzucan Özgür, Philippe Schwaller, Teodoro Laino, and Elif Ozkirimli. Exploring chemical space using natural language processing methodologies for drug discovery. Drug Discovery Today, 25(4) 2020.
Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingee Xia, and Rui Yan. BioT5: Enriching cross cross-modal integration in biology with chemical knowledge and natural language associations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 1102-1123, 2023.
14

---

Published as a conference paper at ICLR 2025
Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev. Molecularmn: Generating realistic molecular graphs with optimized properties. arXiv preprint arXiv:190S.13372 2019.
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21((40):1-67, 2020.
Mayk Caldas Ramos, Shane S Michtavy, Mare D Porosoff, and Andrew D White. Bayesian optimiza- tion of catalysts with in-context learning. arXiv preprini arXiv:2304.0534) 2023.
Bernardino Nomera-Paredes. Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco JR Ruiz, Jordan S Ellenberg, Pengming Wang, Omar Fawzi, et al. Mathematical discoveries from program search with large language models. Nature, 625(7995):468 475, 2024.
Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamash, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, al. Structure- drug design with equivariant diffusion models. arXiv preprint arXiv:2210.1369. 2022.
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher Hunter, Costas Bekas, and Alpha A Lee. Molecular transformer: a model for uncertainty-calibrate chemical reaction prediction. ACS central science, 5(9):1572-1583, 2019.
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang, Graphaf: a flow-based autoregressive model for molecular graph generation. 2020.
Dong-Hee Shin, Young-Har Son, Ji- Ji-Nung Han, Tae-Eui Kam, et al. Dymol: Dynamic many-objective molecular optimization with objective decomposition and progressive optimization. In ICLR 2024 Workshop Generative and Experimental Perspectives for Biomolecular Design.
Michael Skinnider. Invalid smiles are beneficial rather than detrimental to chemical language models. Nature Machine Intelligence, pp. 1-12, 2024.
Teague Sterling and John J Irwin. Zine 15 1igaid discovery for everyone. Journal of chemical information and modeling, 25512) 2237,,2377740 2015.
Jonathan M. Stokes, Kevin Yang. Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M Donghia, Craig R MacNair, Shawn French, Lindsey A Carfrae, Zohar Bloom-Ackermann, et al. A deep learning approach to antibiotic discovery. Cell, 180(4))688-702, 2020.
Dagmar Stumpfe and Jürgen Bajorath. Exploring activity cliffs in medicinal chemistry: miniperspec- tive. Journal of medicinal chemistry, 55(7):2932-2942, 2012.
Mengying Sun, Jing Xing. Han Meng, Huijun Wang, Bin Chen, and Jiayu Zhou. Molsearch: search- based multi-objective molecular generation and property optimization. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp. 4724 2022.
Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic. Galactica: A large language model for science. arXiv preprint arXiv:22 1.09085, 2022.
Gary Tom, Stefan P. Schmid, Sterling G. Baird, Yang Cao, Kourosh Darvish, Han Hao. Stanley Lo, Sergio Pablo-García, Ella M. Rajaonson, Marta Skreta, and al. Self-driving laboratories for chemistry and materials science. ChemRxiv, 2024. doi: 10.26434/chemxxx-22244--p
Austin Tripp, Gregor N. C. Simm, and José Miguel Hernández-Lobato A fresh look at de novo molecular design benchmarks. In NeurIPS 2021 Al for Science Workshop, 2021. URL https:
Vahe Tshitoyan, John Dagdelen, Leigh Weston, Alexander Dunn, Ziqin Rong, Olga Kononova, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature, (717763)95555 2019.
15

---

Published as a conference paper at ICLR 2025
Xiaoxuan Wang, Ziniu Hu, Pan Lu, Yanqiao Zhu, Jieyu Zhang, Satyen Subramaniam, Arjun R Loomba, Shichang Zhang. Yizhou Sun, and Wei Wang. Scibench: Evaluating college-level scientific problem-solving abilities of large language models. arXiv preprint arXiv:2307.10635, 2023.
Guanghao Wei, Yining Huang, Chenru Duan, Yue Song, and Yuanqi Du. Navigating chemical space with latent flows. arXiv preprint arXhv:2405.03987, 2024.
David Weininger. Smiles, a chemical language and information system. 1. introduction to methodol- ogy and encoding rules. Journal of chemical information and computer sciences, 28(1):31-36, 1988.
Andrew D White. The future of chemistry is language. Nature Reviews Chemistry, 7(7):457-458, 2023.
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc Le, Denny Zhou, and Xinyun Chen. Large language models as optimizers. In The Twelfth International Conference on Learning Representations, 2024.
Xiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe, Kei Terayama, and Koji Tsuda. Chemts: an efficient python library for de novo molecular generation. Science and technology of advanced materials, 18(1):972-976, 2017.
Geyan Ye, Xibao Cai, Houtim Lai, Xing Wang, Junhong Huang, Longyue Wang, Wei Liu, and Xiangxiang Zeng. Drugassist: A large language model for molecule optimization. arXiv preprint arXiv:2401.10334, 2023.
Naruki Yoshikawa, Kei Terayama, Masato Sumita, Teruki Homma, Kenta Oono, and Koji Tsuda. Population- novo molecule generation, using grammatical evolution. Chemistry Letters, 47(11):1431-1434 2018
Naruki Yoshikawa, Marta Skreta, Kourosh Darvish, Sebastian Bvellao-RRubach Zhi Ji, Lasse Bjøm Kristensen, Andrew Zou Li, Yuchi Zhao, Haoping Xu, Artur Kuramshin, et al. Large language models for chemistry robotics. Autonomous Robots, 47(8):1057-1085 2023.
Chengxi Zang and Fei Wang. Moflow: invertible flow model for generating molecular graphs. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 617 626, 2020.
Qingfu Zhang and Hui Li. Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions evolutionary computation, 11(6):12-731, 2007.
Yiheng Zhu, Jialu Wu, Chaowen Hu, Jiahuan Yan, Tingjun Hou, Jian Wu, et al. Sample-effccient mujti-objective molecular optimization with gflownets. Advances Neural Information Processing Systems, 36, 2024.
16

---

Published as a conference paper at ICLR 2025
Appendix
A EXTENDED DESCRIPTIONS
A.1 EXTENDED RELATED WORK
Benchmarking LLMs on Chemistry Tasks ChemLLMBench benchmarked several widely-used LLMs set of eight chemistry tasks, such as property prediction, reaction prediction, and molecule captioning (Guo et al. 2023b). The results showed that while LLMs can perform well in selection tasks, they struggle with tasks requiring more in-depth chemical reasoning, such as property- conditioned generation. This motivates the need to improve how LLMs are used in generative tasks. Similarly, SciBench evaluated LLMs on free-response college-level exam questions across various science disciplines, including chemistry, which required complex, multi-step solutions (Wang et al., 2023). Their results indicated that LLMS were unable to generate correct solutions for the majority of questions (Wang et al.) (023). However, progress of LLMs has been noted in general question- answering capabilities: a recent work introduced ChemBench, a dataset of over 7,000 question-anse -answerr pairs aimed at providing systematic understanding of LLM capabilities across different subdomains in chemistry (Mirza et al. 2024). It was concluded that state-of of-the-art LLMs such as GPT-4 and Claude 3 were able to beat human chemists these questions on average, although they still struggle with physical and commonsense chemical reasoning.
LLMs and Evolutionary Algorithms Previous research has demonstrated that language models can be incorporated as operators in evolutionary algorithms in applications such as code and prompt generation (Lehman al. 2023). For example, OPRO and LMEA use LLMs optimize solutions
for different mathematical optimization problems (Yang et al. 2024: Liu et al. 2023a). Other
works have shown that LLMs can be used as crossover and mutation operators to directly optimize
prompts using a training set, outperforming human-engineered prompts (Fernando et al. [2023] Guo al. 2023a). Other applications of LLMs in evolutionary frameworks have been code synthesis (FunSearch (Romera-Panee Paredes et al. 2024)), generation of reward functions in RL for robot control (Eureka (Ma et 2022)). and resource allocation public health settings (Behari et al. 2024).
Multi-objective optimization frameworks In our work, we study the effectiveness of LLM-based mutations multi-objective molecular optimization setting. To ensure simplicity and clarity in our evaluation, we adopt straightforward approaches, such as the sum of objectives and Pareto set selection as selection criteria. Classic methods like MOEA/D (Zhang & Li 2007) and NSGA-III (Deb & Jain (003)) are designed to handle scenarios where the Pareto set exceeds the population capacity. MOEA/D uses decomposition to assign each solution to a specific subproblem defined by weight vector. If the size of the Pareto set exceeds the population size, MOEA/D will select solutions based on their contribution to specific subproblems, so that it can ensure a balance between diversity and convergence. NSGA-III uses reference points in the objective space maintain diversity. When the Pareto front size exceeds the population, a clustering mechanism based on the reference points is applied to select solutions that best represent different regions of the Pareto front. Additionally, there are also some recent works focusing on this topic. For example, [Sun et al. (2022) developed a Monte Carlo tree search algorithm that evaluates rewards by comparing new molecular structures against maintained global Pareto set. Shin et al. introduced a method to decompose the optimization process into progressive sequence based on the order of objectives. Zhu et al. (2024) integrated GFlowNets with a preference-conndtionee sum of objective functions, further advancing the optimization landscape.
A.2 FUTURE WORK
Molecular discovery and design is a rich field with numerous practical applications, many of which extend beyond the current study's scope but remain relevant to the proposed framework. Integrating LLMs into evolutionary algorithms offers versatility through plain text specifications, suggesting that the MOLLEO framework can be applied to scenarios such as drug discovery, expensive in silico simulations, and the design of materials large biomolecules. Future work will aim to further improve the quality of proposed candidates, both in terms of their objective values and the speed with which they are found.
17

---

Published as a conference paper at ICLR 2025
A.3 COMPUTATIONAL RESOURCES
Our experiments computed on NVIDIA A100-SXM4-80GB and T4V2 GPUs. Some of our experiments utilized the GPT-4 model; this refers to the gDt turbo checkpoint from 2023-07-01 All GPT-4 checkpoints were hosted Microsoft Azure
A.4 LIMITATIONS
All benchmarks and tasks evaluated in this study proxies for real chemical properties and may not correctly capture the true chemical performance of molecules in the real world. Thus, the effectiveness of our model in real-world applications remains to be thoroughly validated.
A.5 BROADER IMPACT
The methods proposed in this paper aim to find compounds with desired properties more efficiently, which can benefit many areas, including drug discovery and materials design. While we do not foresee negative societal impacts from methods, acknowledge the potential of their dual use for nefarious purposes. We encourage discussions around these issues and strongly support the development and deployment of safeguards prevent them.
B HYPERPARAMETERS AND ADDITIONAL EXPERIMENTAL DETAILS
For the choice of hyperparameters, baseline genetic algorithm that we build our method upon. We kept the best hyperparameters that were determined in (Gao al.] 2022). In each iteration, Graph-GA samples two molecules with a probability proportional to their fitnesses for crossover and mutation and then randomly mutates the offspring with probability Pm 0.067. This process is repeated to generate 70 offspring. The fitnesses of the offspring are measured, and the top 120 most fit molecules in the entire pool are kept for the next generation. For docking experiments, we reduce the number generated offspring to 7 and the population size to 12 due to long experiment runtimes. We set the maximum number of oracle calls to 10,000 for all experiments except docking, where we set default ctoly-stopping criterion the same in PMO (Gao et al. 2022), which is that terminate the algorithm if the mean score of the top 100 molecules does not increase by at least 1c-3 within five epochs.
the best practices from Graph-GA (Jensen 2019). the
1,000. We kept the
In the multi-objective optimization tasks, we applied a simple transformation by using score for the objectives involving minimization. Also, ensure all objectives remain within the range of 0 to 1 by using normalization. This approach allows for consistent scalarization and comparability across objectives.
MOLLEO (MOLSTM) involves additional hyperparameters when doing gradient descent; we investigate their selection in Appendix C.4 Additionally, we investigate design choices for MOLLEO (GPT-4) in Apppendix C.5 We use three tasks for model development: JNK3, per perindoprii_mme isomers_c9ht 2c2o2pf2c1 130mers__ch100222222221: the rest are only evaluated during test-time. For each model, we show the prompts we used in Anpppeddix El We created prompts similar to those demonstrated in the original source code of each model. replacing each template with a task description. We briefly investigate the impact of prompt selection in Appendix C.6
and
All experiments are conducted with five random seeds. The computational resources we utilized are described in Appendix A.3]
openai apenai.azzre.cco
18

---

Published as a conference paper at ICLR 2025
Table Al: Viability of LLM edits. We prompt different LLMs with descriptions of JNK3 and perindopril mpo target objectives on an initial random pool of molecules drawn from 5 random seeds. We report the percentage of valid molecules (number of valid molecules/number of total molecules), the percentage of molecules with higher fitness after editing, and the mean fitness increase of those molecules.
Metric
MoleculeSTM
BioT5
GPT-4
peridopril mpo: 0.938
peridopril mpp:
peridopril mpo:
:ipp::prl_mpo:
1.000
0.862
Percent valid molecules
JNK3:
JNK3:
JNK3:
0.928
1,000
0.835
peridopril_mpe 0,456
peridopril_mphl
peridopril_mper
Percent molecules with higher fitness after editting
0,568
0.240
JNK3:
JNK3:
JNK3:
0.206
0.513
0.263
peridopri_ +0.033
peridopril_ Impo:
peridopril_mgn Lmpo
+0.208
+0.032
Mean fitness increase
JNK3:
JNK3:
JNK3:
+0.022
+0.0320
+0.0262
C ABLATION STUDIES
C.I PERFORMANCE OF SINGLE-STEP MOLECULE EDITING
To motivate the incorporation of LLMs into GA framework, directly query the LLMs consider to edit a molecule towards a certain property and calculate: (1) the percentage of valid molecules that are output (given that not all SMILES are valid molecules) and (2) which of the output molecules have higher fitness. We show these results on the JNK3 inhibition task in Table A and find that MolSTM and GPT-4 are not always able to produce valid molecules, whereas BioT5 always is due to its use of SELFIES. We also found that BioT5 produced more molecules with higher fitness values compared to the other L.MMs.
In Table A3 we show the performance of directly querying LLMs with an initial pool of molecules on additional tasks. We find that while LLMs are able to edit the molecule pool to improve fitness marginally, using them in optimization framework results in much better fitness values.
C.2 INCORPORATING LLM-BASED BASED GENETIC OPERATORS INTO GRAPH-GA
There are many ways to incorporate LLMs as genetic operators in a GA framework. We investigate several options. First, we investigate using LLMs crossover operator. For GPT-4 and BioT5, we gave each model two parent molecules as input and a description of the objective, and asked the model produce molecule as an output. Because MolSTM aligns molecule embeddings with text embeddings, our crossover operation was to either take a linear or spherical interpolation of the parent molecule embeddings and maximize the similarity of the resulting embedding to the text objective. For the mutation operator, we prompted each LLM with molecule and description of the objective. Finally, we investigated the impact of applying selection pressure in the form of a filter, where we only mutated the top Y molecules and pruned the resulting offspring by distance to the hest molecule overall. We show the results for operator settings we tried in Table A2 and show which operators we ended up using for each LLM in the final framework.
C.3 OPTIMIZATION TRENDS OVER SINGLE-OBJECTIVE TASKS.
In Figure AI we show the optimization curves for three tasks: JNK3, perindopril_mme _mpo, and iso- mers 1op22222ppf
C.4 MOLECULESTM HYPERPARAMETE SELECTION
MolSTM has several hyperparameters: in this section, we motivate our choices for the final model. The first is the number of population members that are selected undergo L.Le-- mutations
19

---

Published as a conference paper at ICLR 2025
Table A2: Top-10 AUC on 5 random seeds for the JNK3 and perindopril mpo tasks using different combinations genetic operators. The operators used for each model to compute the final results in the main paper are indicated with a symbol.
Graph-GA (Baseline)
Operators
MoLLEO (MoLSTM)
MoLLEO (BioT5)
MoILEO (GPT-4)
(Default Graph-GA settings)
peridopril mpo: 0.538 0.009
CROSSOVER:
Random
JNK3:
N/A
N/A
N/A
MUTATION:
0.553 + 0.136
Random, 0.067
peridopril_mpp mpo: 0.499 0.012 [linca]] 0.505 0.018 [spherical] JNK3: 0.722 [lincar] 0.744 0.055 [spherical]
peridopril mpo: 0.600 0,031 JNK3: 0.790 = 0.027
CROSSOVER:
peridopril impo 0.727 0.013
L.LM
N/A
MUTATION:
JNK3: 0.436 0.052
Random, Pmu 0.067
CROSSOVER:
peridopril papp:ppil_mpo mpo: 0.532 0.034 JNK3: 0.631 0.327
peridopril peppppillmppo::: mppo 0.676 0.034 JNK3: 0.650 0.096
peridopril Lmpp:: mmpo 0.552 = 0,024 JNK3: 0.673 0.047
Random
N/A
MUTATION:
LLM, P.u. 0.067
CROSSOVER:
peridopril mpo: 0.513 0.040 JNK3: 0.553 0.193
peridopril mpp:
peridopril mpo:
Random
0.686 0.343 JNK3: 0.708 0.030
0.615 0,058 JNK3: 0.762 0.044
N/A
MUTATION:
LLM,
CROSSOVER:
Random
peridopril_ mpo:
MUTATION:
0.579 0.044 JNK3: 0.571 # 0.109
Selected top molecules, randomly mutated. pruned
N/A
N/A
N/A
offspring distance top-1 molecule
CROSSOVER:
Random
peridopril mpo: 0.554 0.034 JNK3:
peridopril _mpo: 0.740 0.032 JNK3: 0.728 0.079
peridopril mpo:
MUTATION:
0.575 0.074
Selected top Y molecules, mutated with LLM, prumed offspring by distance lop-1 molecule
N/A
0.730 0.188
JNK3:
0.758 0.031
CROSSOVER:
LLM
peridopril_mpe mppo: 0.490 0.016 [lincar] 0.517 = 0.006 [spherical] JNK3: 0.692 0.110 [lincar]
peridopril Lmppriilpppoo-o-- mpp: 0.736 + 0.014 JNK3:
peridopril mpo: 0,592 + 0.035 JNK3:
MUTATION:
Selected top Y molecules, mutated with LLM. pruned offspring by distance to top-1 molecule
N/A
0.429 0..110
0.794 0.026
20

---

Published as a conference paper at ICLR 2025
C9H1002002120
0.5
Sc.te5 RI01550
0.5
06
0.2
0.4
04
12x
12x
10c
Augmented Memory
Graph-GA
McILECIB 075))
MOILED/SPT
MTLEOMM
REINENT
SONOE reached
Figure AI: Average top-10 10 molecules generated by MOLLEO and Graph-Gr GA models for three tasks over an increasing number of oracle calls. For each model, we show the convergence point with a star. The model is considered to have converged the mean score of the top 100 molecules does not increase by at least le-3 within five epochs.
Table A3: Ablation studies of LLM editing based on direct user queries. Top-10 average objective scores reported.
JNK3
isomers_c9h (0tmmers__c110222222211
perindopril
Initial population
0.085 0.010
0.101 0.025
0.281 + 0.026
MolSTM direct query
0.084 0.008
0.201 0.040
0.390 + 0.008
MoLLEO (MoLSTM)
0.716 0.240
0.905 = 0.0372
0.572 + 0.041
BioT5 direct query
0.109 0.012
0.260 0.076
0.648 + 0.019
MoLLEO (B10T5)
0.883 0.040
0.909 0.015
0.759 = 0.019
GPT-4 direet query
0.164 0.076
0.686 0.127
0.388 + 0.075
MOLLEE (GPT-4)
0.926 0.052
0.935 0.048
0.643 = 0.094
(Algorithm 1p. In Table A4 we show the Top-10 AUC after choosing different numbers of top-scoring candidates for editing by MoleculeSTM. We found that 30 candidates resulted in the best performance. Note that we used a different prompt for this experiment than the one used to obtain results in Table 1 (see (Apeendp C.6p. We use 30 candidates anytime the filter is employed for all models, although this hyperparameter can be ablated independently for each model.
MoleculeSTM has several hyperparameters related molecule generation since it involves gradient descent to optimize an input molecule embedding based on a text prompt. We look at two hyper- parameters, the number of gradient descent steps (epochs) and learning rate, and plot the results in Figure A2 We find that if the learning rate is too large (Ir=1), the mean fitness changes unpredictably, but if is too small (Ir=le-2), there are minimal changes to the mean fitness. Setting the learning rate to le-1 results in more consistent improvements in mean fitness. We also set the number of epochs to 30 since more epochs are too coneucinu and fewer do not result in noticeable fitness changes.
C.5 GPT-4 ABLATIONS
We conduct experiments to understand the performance of MOLLEO (GPT-4) in the following settings: different numbers of offspring in each generation, different underlying GPT models, incorporating retrieval augmentation methods, and different rules from Graph-GA and SMILES-GA Table A5 and Table A6, and describe the results following sections.
Number of top-scoring
candidates selected for mutation
Top-10 AUC
Table A4: Top- 10 AUC JNK3 bind- ing task with varying numbers of top- scoring candidates selected to undergo LLMeb mutations.
20
0.680=0.213
30
0.730=0.188
50
0.627+0.250
21

---

Published as a conference paper at ICLR 2025
Prompt: This molecule looks like Penicilin.
033
10
025
I=
% valid moleculss
64
Mean fitnass
0.2
60
0.0
Epadd
(a)
Promet: This melecule hydrophobic.
1.
6.
20.0
volld molecules
84
Maan fitnes
40.0
2010
bools
Figure A2: Mean fitness and percent valid molecules with varying number of gradient descent epochs (plotted on log-scale) and learning rates MoleculeST'M on two tasks: (a) molecular similarity to Penicillin (based on Tanimoto distance) and (b) molecule hydrophobicity (logP).
22

---

Published as a conference paper at ICLR 2025
Table A5: Ablation study on MOLLEO (GPT-4). Impact of the number of offspring in each round and search (RAG).
Number of offispring
RAG Search
20
70
200
w. RAG
w/o. RAG
jnk3 chomer_c9hi 20
0.731+0.012
0.790=0.027
0.785+0.02 0.2
0.830=0.047
0.790=0.02)
0.967+0.010
0.87410.053
0.960
0.982=0.018
0.874+0.053
perindopril mpo
0.573+0.042
0.600#0.031
0.580+0.028
0.71740.024
0.600#0.031
Table A6:
Ablation study on MOLLEO (GPT-4). Impact of different versions of LLMs and rules from different sources.
Different Versions of LLMs
Rules
GPT-3.5
GPT-4
rules
Graph rules
SMILES-GA rules
jnk3 isomer_ www.hhhhhhhh. On222pfeel perindopril mpo
0.669#0. 104 0.902+0.021
0.790s0.027
0.765=t0.047
0.790+0.027
0.774x0.088
0.874+0.053 0.56440.022
0.871#0.085
0.874+0.053 0.60010.031
0.872+0.029
0.60040.031
0.56240.042
0.58340.031
Number of offspring We vary the number of offspring generated in each iteration of MOLLEO (GPT-4) on three tasks and find that 70 offspring produces, on average, the best results, which also the same number determined in (Gao et al. 2022
Retrieval-augmeentd search To explore how retrieval can enhance L.MSs the optimization process, we incorporate retrieval-augmenter search module into MOI.EEO (GPT-4). Specifically, after offssring are proposed, 1,000 molecules are randomly sampled from ZINC 250K. From these, 20 molecules selected based their Tanimoto similarity to the top 20 molecules in the current population. These retrieved molecules then replace the 20 worst molecules in the population. In Table A5 the results show that this approach is effective in improving the optimization results of MoLLEO (GPT-4) for each task.
GPT-3.5 GPT-4 We tested MOLLEO using both GPT-4 and GPT-3.5, an older version of the model. In Table A6, we show that GPT-4 outperforms GPT-3.5 on two tasks, although GPT-3.5 still beats the baseline Graph-GA algorithm TTbll I). Interestingly, GPT-3.5 beats GPT-4 on task based on structure-based optimization.
Different rules In Graph-GA, the default crossover and mutation operators are pre-defined by domain experts based chemical knowledge. These pre-defined operators can be considered as rules guiding the generation process. Here we also consider rules from another source, SMILES-GA (Yoshikawa al. 2018), which defines rules that operate SMILES strings instead of graphs. To evaluate the impact of rules from different sources, we perform an ablation study on MOLLEO and also conduct experiments without any rules, where LLMs repeatedly queried to propose molecules until the offspring size reaches the target number in each round. The results shown in Table A6 indicate that both Graph-GA and SMILES-GA rules are better than not using results at all, and Graph-bau baspd rules are better than SMILES- based rules.
C.5.1 GPT-4 IN AN ACTIVE LEARNING FRAMEWORK
We investigate the performance of GPT-4 when the EA framework is replaced with active learning setting. This can be thought of as testing the impact of the genetic operators in the underlying genetic framework. In this setting, we initialize population pool and randomly sample & molecules from the pool. We then pass the molecules to GPT-4 and query for a new molecule with better objective values. After generating batch of molecules, we integrate the batch back into the population without selection, allowing the population to grow until it reaches the budget of oracle calls, In our experiment, set the budget to 10,000 oracle calls, the batch size to 100, and k to 2.
23

---

Published as a conference paper at ICLR 2025
The results, shown in Table A7 indicate that the active learning setting achieves subpar performance compared to MoLLEO (GPT-4). This demonstrates that while LLMs like GPT-4 can modify existing molecules, they struggle to independently propose high-quality molecules, underscoring the necessity of the evolutionary process. Interestingly, we observe that the active learning setting performs relatively well the isomer task compared to the other two; this can maybe be attributed to the isomer task being simple.
Table A7: Ablation studies of active learning (AL) on GPT-4. We report the Top- 10 AUC of single objective results.
GPT4-AL
MoiLEO (GPT-4)
JNK3
0.583#0.042
0.790=0.027
isomer caterr_chhh00 191109922222ed
0.873+0.048
0.874+0.053
perindopril mpo
0.539#0.046
0.600=0.031
C.6 IMPACT OF PROMPT SELECTION
The choice of prompt for a given task is an important consideration, as some prompts can be better aligned with information the model knows. For example, the prompt we used MOLLEO (MOLSTM) for the JNK3 inhibition task was "This molecule inhibits JNK3." However, there are multiple ways of describing inhibition and multiple ways of identifying the enzyme (JNK3, c-Jun N-terminal kinase 3). To that end, we investigate the impact of prompt selection downstream performance.
To generate a set of prompts, prompted GPT-4 to generate ten synonymous phrases for input prompt. We then computed the Spearman rank-order correlation coefficient (Spearman's 's p) of each phrase on an initial molecule pool between the cosine similarity generated by MoleculeSTM and the ground truth fitness values. Finally, we ran the genetic optimization using MOLLEO (MOLSTM) with the input prompt and the prompt with the highest Spearman rank-order correlation coefficient.
On the INK3 task, the default prompt we wrote was "This molecule inhibits JNK3.", ", which had a Spearman's p of -0.0161. The prompt with the largest Spearman's 's p (0.1202) was "This molecule acts as an antagonist to JNK3." When we ran MOLLEO (MOLSTM) with the default input prompt, the top-10 AUC was 0.643 # 0.226. When we ran MOLLEO (MOLSTM) using the prompt with the largest Spearman's 's the top- 10 AUC was 0.730 + 0.188 This demonstrates that prompt selection can influence downstream results, especially for smaller models, and opens the door for future work in this area.
Table A8: Top-10 10 AUC of single-objective tasks additional baseline models. The best model for each task is bolded and the top three are underlined.
type
Method depoctivn
RUINVENT
Angmented Memory
Gmiph GA
MocLBB (MolSTM)
(RinTS)
MOLLBO
Property opeiiestionn
INK3
QED
0.939
0.677
0157
U.941
0
0.941 0,000
0 0.073
0:400
0.557
0.564
0.000
0.643 0.326
0.738
0.97
0.079
0 0.000
0,790 0.027
GSK33
0.767
x 0.103
0.665 = 0.043
0.880 a 0.002
0.788 a 0.070
0351
x 0.041
0,808 = (1.4]
0.889 = 0.015
0.863 + 0.847
mestranl vimilarity
0.435 à 0.015
0.618 00248
0.764 0.815
0.579 0.022
0.627 x (1.89
0.596 0.018
0.777 0.100
0.972 0.199
albutenel similarity
06144 0.021
0.595 0.008
0.918200000
0,874 0.020
0.902 0.019
0.929 2 0.005
0,968 0,005
0.965 + 0.004
optimization
Name-tasch
thiothisem redisznvery
perindopril mpa
04400x0001
0.353
0:534 0.013
0.537 0.016
0.502
0.000
0.493 0.011
0.037
0.738 0.016
0 0.031
6.052
maolazine mmjo
0.665 x: 0.000
81009
00,02
0.728
a 0.012
0.735 x
01725 = (1.04)
0.749
0.012
0.769
0.122
odimerinn
0.794 0.007
11,04
0.856 0.013
0,808 0.012
0,762 0.02
0.823 11007
0.017
0,835
& 0.122
someess e7hön2o2
0.706 0.033
0.442 0.009
0.954
0.003
0,949
0.036
0.662 0.07
09668 0.006
0.928 0,018
0,984 a 0.008
hasual
hop
11560=0:00
0.565
0.548
0.19
0.951
qptimizaioon
valaatta_xnsstt
0.000 0.000
8.000 = 0.000
0.000 0.000
0.000 0.800
0.000 e: 0.000
0.000 0.000
0.001
0.867 : 0.192
24

---

Published as a conference paper at ICLR 2025
Table A9: Performance of Augmented Memory framework with and without LLM-based genetic operators. We report the Top-10 10 AUC of single objective results.
Augmented Memory Objective(t)
w/o LLM
w/ BioTS
w/ GPT-4
JNK3 albuterol_st Sobuterol_simillritty
0.773 0.073
0.781 0.094
0.794 + 0.087
0.918 0.026
0.925 0.076
0.941 + 0.033
Table A10:
Performance Genetic GFN with different genetic operators. AUC of single objective results.
We report the Top-10
Genetic GFN Objective(t)
default GA
MolLEO(BioTS)
MoILEO(GPP-4)
JNK3 albuterol similarity
0.766 + 0.077
0.775 0.056
0.783 + 0.034
0.946 0.013
0.962 0.017
0.971 0.020
D EXTENDED EXPERIMENT RESULTS
D.1 ADDITIONAL, BASELINE MODELS
We report the performance of an additional baseline, DST (uu et al. 2021), on randomly selected
tasks in Table A8
D.2 INCOPORATING LLMS INTO AUGMENTED MEMORY
To further evaluate the effectiveness of L.MM-based genetic operators, integrate them into framework with augmented memory mechanisms (Guo & Schwaller (022)) to refine the molecules stored in replay buffer. The results presented debeoosnjemoottttttc that incorporating both BioT5 and GPT-4 into this framework improves performance, indicating the capability of LLM-based genetic operators effectively augment molecules in the replay buffer. The performance improvement not large, which is likely due to the application of only single round of edits.
D.3 INCOPORATING LLMS INTO GENETIC GEN AND JANUS
We experiment with extending the MOLLEO framework to other genetic algorithms other than Graph GA including Genetic GFN (Kim et al., 2024) and JANUS (Nigam et al., 202I). Genetic GFN combines GAs with GFlowNet by first sampling molecules using the current policy during the GFlowNet training stage. These sampled molecules are then refined into higher-reward ones using GAs, after which the policy is fine-tuned using the refined samples. We replace the default GA in Genetic GFN with MOLLEO and the results are shown in Table TT0 Both MOLLEO (BIOTT5) and MOLLEO (GPT-4) outperform the default GA, which relies on predefined rules crafted by chemical experts.
JANUS maintains distinct populations of molecules that exchange members, each governed by specialized genetic operators- one set focused on exploration and the other on exploitation. The exploitative genetic operators apply molecular similarity as an additional selection pressure, while the explorative operators leverage guidance from a deep neural network (DNN) trained on molecules across all previous generations. We replace these genetic operators with MOLLEO (MOLSTM) and MOLLEO (B10T5). The results of this approach are presented in Table A1I The results show that MOLLEO can edit molecules effectively both Genetic GFN and JANUS, indicating the utility of LLM-bbsed genetic operators in several settings.
D.4 COMPUTATIONAL COST MOLLEO
The most expensive part of chemistry experiments is often the oracle call. In table A12 show the computational costs of MoLLEO vs Graph GA for two experiments: JNK3, which uses lightweight oracle, and docking against human dopamine D3 receptor (PDB ID 3pbl), which is more expensive.
25

---

Published as a conference paper at ICLR 2025
Table All:
Performance JANUS with different genetic operators. We report the Top-10 AUC of single objective results.
JANUS Objective(t)
default GA
MoILEO (MOLSTM)
MOLLEO (B1OT5)
JNK3 albuterol_ albuterol_similarrty
0.678 0.031
0.680 0.024
0.685 = 0.044
0.712 0.049
0.779 0.038
0.904 + 0.051
Table A12: Average Running time of five seeds of MOLLEO framework
Task
Model
Avg time
Graph GA
8h 23m 37s Ih 46m
Docking 3pbl
MoLLEO (B10T5)
6h 57m 14s + 1h 24m
MOLLEO (GPT-4)
12h 14m 25s + 2h 33m
Graph GA
12m 17s + 5m 1h 23m 6s 20m
JNK3
MOLLEO (BIOT5)
MOLLEO (GPT-4)
4h 06m 12s + 1h 15min
In this table, we show that for lightweight oracles, incorporating LLM edits indeed results in high runtimes since calling the LLM is more expensive than random edits. However, as experiments become more expensive, such as with docking, the cost of the LLM call becomes insignificant in comparison to the docking time, hence the runtime is governed by the oracle call. For MOLLEO (GPT-4), the runtime is further constrained by OpenAI API rate limits, which impose restrictions on the number of input/output tokens processed per minute.
D.5 SIMILARITY ANALYSIS FOR MOLECULES BEFORE AND AFTER LLM EDITING
To investigate the types of LLM edits occurring to a molecule, we analyzed the Tanimoto similarity between molecules before and after editing, comparing these values to the similarity with a random molecule. This approach allows us to evaluate whether the edited molecules are as distant from their original versions as they from random molecules.
We present these similarities in Table A13 for all the LLMs included in our study. For GPT-4, which employs crossover instead of mutations, report both the maximum and minimum Tanimoto similarities, where the maximum similarity corresponds to the closer parent and the minimum similarity to the further parent. In all cases, the Tanimoto similarity between molecules before and after editing is consistently higher than the similarity between edited molecules and random ones, indicating that the edits effectively preserve molecular substructures.
Interestingly, molecules edited by BioT5 exhibit lower similarity to their pre-edit versions compared to other LLMs, suggesting that BioT5 may reconstruct more of the sequences.
Table A13: Tanimoto similarity between molecules after LLM editing and molecules before LLM editing/random sampled molecules
Tanimoto similarity
Tanimoto similarity
to molecules before editing
to random sampled molecules
MoleculeSTM
0.761 + 0.235
0.120 + 0.044
BioT5
0.173 + 0.082
0.111 + 0.038
0.433 # 0.207 (max)
GPT-4
0.123 + 0.045
0.165 + 0.089 (min)
26

---

Published as a conference paper at ICLR 2025
Table A4: Task keyword frequency in open-source LLM training data. We that BioT5 has two training phases (pre-training and fine-tuning), and so we include the frequency of keywords in both datasets.
Bio15 hits Seeettaininn++Fin-tuum
Prompt
Keyword
MoISTM hits
JNK3
0
0+0
"This molecule inhibits JNK3"
kinase
1698
1698-271
Jun N-terminal kinase
17
17+0
DRD3
0
0+0
"This molecule inhibits DRD3"
Dopamine receptor
73
73+3
Dopamine receptor
7+0
Dopamine receptor
"This molecule inhibits EGFR"
EGFR
82
82+0
adenosine receptor A2a
0
0+0
"This molecule hinds adenosine receptor A2a"
adenosine receptor
25
2543
Table A15: Multi objective results. The best model for each task is bolded.
Task 1: maximize QED (t).
Summation
minimize SA (1), maximize JNK3 (1)
(Top 10 AUC) (*)
Hypervolume (1)
Structural diversity (1)
Objective divensity (t)
Graph GA
1.967
0,713 0.083
0.115
0.351
MoILEO STM)
2.177 + 0.178
0.625 + 0.162
0,803 0.011
0.362 = 0.074
Summation
MoLLEO (B1oTS)
1,946 0,222
0.199
0.196
0.341 = 0.292 = 0.076
MOLLEO (GPT-4)
2.367 = 0.044
0.752 : 0,085
0,726 0.063
Graph-GA
2.120 0.159
0.603 + 0.082
0,761 + 0.034
0.219
Pareto optimulity
MoLLEO (MoLSTM)
2.234 0.246
0.248
0.015
0.306
MolLEO (BioT5)
2.325 + 0.164
0,630 +: 0.120
0,724 0.020
0.339 = 0.062
Mo.L.EO (GPT:4)
0.057
1.104
Task maximize QED (t), minimize SA (1), maximize GSKB3 (*)
Gmph-GA
0.055
MoLLEO (MoLSTM)
0.132
0,303 + 0.024
0.820 0.010
0,440 = 0.037
Summation
MoLLEO (B1oT5)
2.306 = 0.120
0.693 : 0.093
0,803 0.013
0.384 = 0.045
MoLLEO (GPT-4)
2.543 0.014
0.832 0.024
0,715 4 0.052
0.391 0.021
Graph-GA
2.339 a 0.139
0.640 1: 0.034
0,816 a: 0.028
0.381 = 0.071
Pareto optimality
MoLLEO (MoLSTM)
0.054
0.017
MOLLEO (BioT5)
2.299 + 0.203
0.645 + 0.1
0.759 0.022
0.371 = 0.047
MoLLEO (GPT-4)
2.631 = 0.023
0,820 + 0.024
0.646 0.017
0.191 = 0.026
Task maximize QED JNK3
minimize SA (J). GSKB3 (J). DRD2 ())
Graph GA
3.856 0.075
0,162 + 0.048
0.024
= 0.057
MoLLEO (MoLSTM)
4,040 : 0.097
0.474 1 0.193
0,783 0.027
0,413 = 0.064
Summatioo
MoLLEO (B1oTS)
3.904 +: 0.092
0,266 +: 0.201
0.828 0.005
0.243 = 0.081
MoLLEEO (GPT-4)
4.017 0.048
0.606 0.086
0.726 0.064
0.289 0.050
Graph GA
4.051 + 0.155
0.606 + 0.052
0.688 0.047
0.294 = 0.074
Pareto optimality
(MoLSTM)
3,989 = 0.145
0,381 0.204
0.030
=
MOLLEO (B10T5)
3.946 = 0.115
0.367 : 0.177
0,784 0.020
0.367 = 0.177
MoLLEO (GPT-4)
4.212 + 0.034
0.696 + 0.029
0.641 a 0.037
0.266 = 0.062
D.6 CAPTION ANALYSIS FOR OPEN- SOURCE LLMS
To understand how well objective tasks are represented in the training data of the underlying open- source L.LSs used in this study, we examine the frequency of keywords related to those tasks in Table A14 Based on our findings, the models have seen small number of data points related to molecules on objective tasks such as EGFR and Jun N-terminal kinase (although the latter uses a different spelling than we indicated with our prompt). To our knowledge, the LLMs do not contain information explicitly on tasks such as DRD3 and Adenosine receptor A2a, but related concepts do exist in the training data. Despite this, MOLLEO (B1OT5) achieves the best performance these docking experiments (see Figure 3). These results highlight the strong generalization capabilities of I.LMs.
27

---

Published as a conference paper at ICLR 2025
(a)
(b)
McILEOI
McILEO(GPT-4)
Graph-GA
Graph-GA
MoILEOIBIOTS)
MoILEO(BIOTS)
MoILEOIMOLSTM)
MoILEO(MOLSTM)
Utopian Point
Utopian Point
A.1
0.13
01
0.03
0.
0 o of 0. 15
NNK
0.3
5.5
GSK3B
QED
QED
0.
1.
Figure A3: Pareto frontier visualizations for Graph-GA and MOLLEO on the following multi- objective tasks: (a) Task (min SAscore, max JNK3 binding, max QED) and (b) Task 2 (min SAscore, max GSK33 binding, max QED). The utopian point corresponds to the maximum (best) possible values across all objectives. SA scores are rescaled to [0, 1].
D.7 DIVERSITY ANALYSIS IN MCLTTI-BBJETIVVE OPTIMIZATION
We show the structural diversity and objective diversity for multi-objective optimization in Table A15 Structural diversity reflects the chemical diversity of the Pareto set and is computed by taking the average pairwise Tanimoto distance between Morgan fingerprints of molecules in the set. Objective diversity illustrates the objective value coverage of the Pareto frontier and is computed by taking the pairwise Fuclidean distance between objective values of molecules in the Pareto set. In Figure A3) also visualize the Pareto optimal (in objective space) for MOLLEO and Graph-GA for Tasks and? 2.
GSK38
QED VS 5A
QED VS GSK38
104
HOLCOIBET TO!
POLCOIDOTS
Grash-kk
Uheda Next
CSK39
MOLOOIOTHI Me PORETTO
GSK3B
QED
QED
(a) GSK33 vs. SAscore in task 2
(b) QED vs. SAscore in task 2
(c) QED vs. GSK39 in task 2
JNK3 vs
QED
QED vs JNK3
032
Me PeLDozaOTO
115
PaLSOPOLLINg
Melaoorri)) Port
0333
3.7-
5.0
OED
(d) JNK3 vs. SAscore in task
QED vs. SAscore in task
(f) QED vs. JNK3 in task
Figure A4: 2D plots for multi-objective optimization in task and task 2
D.8 CASE STUDY: SAMPLE MOLECULES FROM FINAL POOL
Below, we show the top ten molecules across all runs from the MOLLEO and Graph-GA for two tasks: deco_hop and EGFR docking.
28

---

Published as a conference paper at ICLR 2025
D.8.1 TASK 1: deco_hop
The goal of the deco_hop task generate molecules that contain specific substructures while containing others; these substructures are shown in Figure AS The final deco_hop o__ score is calculated as the mean of substructure presence/absence (binary score) and Tanimoto distance the target molecule. We showcase our best-generated molecules from the deco hop task in Figure A6
s substzucture(s) (s) to avoid
substructuze (s) to include
target molecule s imilarity
N
city ;;, 00
O0
C
HN
c c;ha
Figure A5: Substructures be included or avoided in the hop task.
D.8.2 TASK 2: EGFR docking
The goal of the EGFR docking task is to generate molecules that have a low binding affinity to epidermal growth factor receptors in humans (EGFR, PBD ID: 2RGP. Molecules docked against EGER using AutoDock Vina (Eberhardt et al., (021), and the output is the docking score of the binding process. We showcase best-generated molecules from this task in Figure
E PROMPTS
For each model, we show the prompts used for each task. When creating the prompts, we followed the format of examples in the original source code as closely as possible.
MOLLEO (MOLSTM) prompts
ORD
This molecule like drug.
This molecule inhibits JNK3.
GSX33
This molecule inhibits GSX3S.
DRD2
This malecule inhibita DR02,
mestranol mestranol_eimilaricn similarity This molecule looks like Mestranol.
albuterol similarity This molecule looks like albuterol.
thiothixene shiothiwene_rediscowe rediscovery This molecule looks like Thiothixene.
celecoxib redissovedy This looks like Calecoxib.
perindopri1 mpo
This molecule looks like Perindopril and has 2 arematic rings.
zanolazine mpo This molecule looks like Ranolazine, is highly permeable, is hydrophobic, and has 1 F atom.
sitagliptin mpo This molecule has the formuls C16H15PEN50, looks like Sitagliptin, is highly pesmeable, and is hydrophcbic.
29

---

Published as a conference paper at ICLR 2025
0.328
0.50
0.574
0.376
0.583
6.586
0.570
Graph-GA
0.661
0.651
0.646
0.652
OC
0.651
(b) MOLLEO (MOLSTM)
8.983
0.981
0.985
8.964
NN
0.961
0.981
6.999
0.974
0.961
0.959
(c) MOLLEO (B10T5)
0.982
8.979
99
9.978
(d) MOLLEO (GPT-4)
Molecules with best deco_hop scores generated by Graph-GA and each MOLLEO model. The deco hop score each molecule is written beside it. Higher deco deco_hop scores are better.
Figure A6:
Iscmers_ c9w1___9_10000 ON202PF2C1
This molecula the atoms
deco sep
This molecule does contain the substructure (#7)-cleec2nsssecl. which is aminobenzothiszzle,, does not contain the aubstructuze ([46) (-0)=0, which is dimethyl sulfone, conzeins scaffold, is similar to cccoclecinene (esssccctnescdc)cc2eeee..
4-amino-7 -yyroooygiinzzelise (#0)2(C) (2ic.
scaffold_hog
This molecule does not centein the scaffold [#T)
[cphi]c12, contains the substructura [660--6k0--te)--ttee---eii-- (6) [47] :lcce2ncsc2cl, and is similer to 00CD=1ce2nene (Wcieco c2ce18 (-0) (-01 (1C) roic.
maxjnk3 maxqed minsa
This molecule aynthesizable, locka Like drug, and inhibits JM33.
30

---

Published as a conference paper at ICLR 2025
13.5
11.5
13.99
Graph-GA
$44.9
F14.8
-1..2
:14.6
(b) MOLLEO (MOLSTM)
15.4
$15.4
F15
15.1
25.
15.2
$15.0
(c) MoLLEO (B10T5)
144.4
144.2
.4.5
14.66
-34.4
(d) MOLLEO (GPT-4)
Figure A7: Molecules with best EGFR docking scores generated by Graph- GA and each MOLLEO model. The docking score of each molecule written beside Lower docking scores are better.
maxgak3b mtxpmmaaxaeaeeeeeeaeee minsa This molecule is syathesizable, locks Like drug, and inhibits GSK3B.
maxjnk3 maxqed minsa mindrd2 mingak3b This molecule aynthesi like a drug, and inhibits UNK2.
inhibit GSRR3, inhibit DRD2,
3pbl_docking
This molecule inhibits DR03,
2rgp docking
This molecile inhibits
3am1 docking
This molecule binds Lo adenosine receptes 82a.
31

---

Published as a conference paper at ICLR 2025
MoLLEO (B10T5) prompts
Template:
Definition: You given molecule SELPIES. Your job generate SELFIES molecula that (CBJECrIVE]. Now completa the following example Input: (ww_liee__eeeeeleieeeeeeee Output:
OBJECTIVE: looks more like drug
JNK3
ORJRCCTIV: inhibita JNK3 more
68X3
OBUECTIVE:
more
DRD2
OBJECTIVE: inhibits DRO2
mestranol_ eimilarity
OBJECTIVE: looks more like Mestrancl
albuterol similarity
OBJECTIVE: looks more like Acbuterol.
thiothixane ceciskene_edddccverry like Thiothixens
OBJECTIVE:
rediscovery
OBJECTIVE: looks more like Celecoxib.
perindopril mpo
ORJECTIVE: looks more like Parindopril and has 2 arematic rings
sitagliptin
ORJRCTTUR: the formula oT 6R15F6650, looks more sisagliptin, highly gezmeable, and la hydrophobic
ranolazine_ mpo
ORJRCCTTV:: looks more kik Ranolazine, is highly permeabla, is hydrophable, and has
Isomers ON202PF2C1
OBJECTIVE: has the Formuls C9H10N2022F2C1
deco hop
ORJECCTIV: does not contain the ructuructure [#7]-c1 ooc2ncac2e1, does contain asbstructure cs ([+6]) [=0) contains
scaffold [12]-otn[cph) [1chh]nee2 nc2 ( (-(48)) [o,h0] [o;h1]e12, and 1a similar to
[C] [G] [C] [+C] [0] [=[]] [C] [-8] [C] [Branch]] [#0) [N][[]]]] [+0] (-O] [N) [8] [C] [Ring]]
[Breachi] [=C] [Ringl] [-Branch2] [=C] [Ringl] [S] [+C] [Ring2] [Ringl] [Ring2] [9] [-3ranchi]
[C] [=0] [=granchi] [C) [=0] [c] [Branchi] [e; [c] [Branchi] [2) [c] [c]
scaffold_hog hop
ORJECTIVE: does not contain
scaffald [B6]]]())
[12
contains the rueutre
[47] slccc2ncse2e1, and is similar to the SELPIES [C]] [C] (0](0]) [-X] [0] [Brenchi] [42] [8] [C] [+C] [C] [=C] [=]] [[]][s] [C] [Ringl] [Branchi] [=2] [Kingl] [=aranch2] [=C]
(Kingi) [8] (C) (=C) [Ring2] [Ringi] [Ring2] [s] [=Branchi] [c) [=0] [=Branchi]
[=O] [C] [Rranch1]
(Branch1] [C] (C)
maxjnk3 maxqed minsa OBJECTIVE: is greates inhibito: of JNK3 is more syathesizable and is more like drug.
maxgsk3b maxqud minsa OBJECTIVE: inhibits 68X35 more,
synthesisable
drug.
maxjnk3 maxqed minsa mindrd2 mingek3b
оBJECTIVE; is greates inhibito: of JNK3, is more like drug, inhibits GSK3D less, inhibits DRD2 less and is more synthesizeble.
3pb1_ docking
OBJECTIVE: inhibits
2rgp docking OBJECTIVE: inhibits EGTR
32

---

Published as a conference paper at ICLR 2025
donk_do
to adenosime na receptor
MOILEO (GPT-4) prompts
Template:
have two molesules and their (TASK).
(Smiles of Parent A, objective score of Parent X) (Smi of Parent B, objective acore
propese molecule (OBJECTIVE). can
crossover
mutations based the given molecules just propose molecule based your knowledge.
Your output should fol
format: [ectxplanationss SEXPIANATTON, esMolecu
poxt $Molecule)]. I. Hlere are the requirements:
SEXPLANATION
your analysis.
2. the Stolecule should be the smiles of your proposed molecule.
moleculs
gED:
ORJECTIVR:
TASK: QED scores
QBURCTIVE
score measures drug-likeness of molezule.
JNK3
OBJECTIVE: has higher JNK3
TASK: 3833 scores
QBURCTIVE_DETINITTOON The JNK3.
molecular's biological activity againat
G3X3
OBJECTIVE: has nigher GSX33 score TASK: G3К3Ø scores
OR.TRCTTVR_ DEFENITTON: The GSK38 score mesaures a molecular's blologica" vityy against 63K3Ø.
ORJECTTVR: has higher DRD2 500"8 TASK: DRD2 scores DOURCTIVR_DEPINITICC The DRD2
molesule's biologicsl activity against biological target namec. the dopamine type receptor (DRO2).
OBJECTIVE: higher mestranol similarity score TASK: mestranol larity sccres The mestrancl similarity score measures à melecule's Tanimoto similarity with Mestranol.
thiothixene rediecovery
OBJECTIVE: has higher thiothixene rediscovery score TASK: thiothi redt seevary scores
MOAECTIV_-DEPINTTION The thlothixene rediscovery measurea molecule's Tanimoto similarity with shicthixene's SMILES to check whether it could be rediscovered.
perindopril _mpo
QBJECTIVE:
higher perindopsil mittiobjeective score TASK: periadspril milti-objective scores
OBJECTIVR, ORJRCTIVV_DDPPNNTTII DEPENITINN The perindopril multi-objective score reasures the geometric of several and the number of aromstic zings.
including the molecile's Tanimoto similarity perindopril
sitagliptin mpo
овUECтIVE:
higher sisagliptin milti-objectàve score TASK: sitagl iptin molti-objective acores
OBJECTIVE DeCCIIVV_DEPINNTIIN: The aitagliptin mettiobjeettve score ressures the geometric of several
including the molecule's Tanimoto similarity sitagliptin, TPSA sccre, Logs score and iscmer score with c16H15P6N30.
ranolazine mpo
OBJECTIVE:
higher ranolazine multi-oajective score
TASK: varolazine my mi jact scores
The ranolazine multi objective acore measures the geometzic means of seversl including the nolesule's Tanimoto similarity ranclesine, TPSA score Loge scora and nurber of fluorine stoms.
COJECTIVE_DEPINITTI
33

---

Published as a conference paper at ICLR 2025
Isomera 1 :N202PP22:;::
OBJECTIVE: nigher score
TASK: isomer scores
ORJECTTVR_
DEPENTTION: ORUFCTTIORLDEPINTTT..
isomer acore measures a molncula's similaritty terms
atom counter to C9810N2CZPE2C1,
hop
OBJECTIVE: has highe: deco hop score TASK: deco hop scores
The deco hop score is the arithmetic means of several scores, including binary score about whether cortain certain SMARTS structuees (maximize the similerity to the SKILE ' [47] [elle:hl]nn2[erhal] [48]) [esh0] [erhi]e12', while excluding specific SMARTS patterns [f7]-cccce2nnsc2cl and (c56( =0)))")) and (2) the molecule's Tonimato similarity PR20 "CCCOclcc2ncne (#c3e:chncactt))ccecc (=0) (-0)0 (C)
PRAECTIVE_DEEPNIITII
scaffold hop
OBJECTIVE: has nighe: scaffold hop score TASK: scaffold
DEFFIIIE.EEE The scaffeld hop score is the srithmetic means including binary
of several
about whethar contains
certain SEARTS siructures (maximize the similarity to the SMILE (+()))((t)(((()((())))))))-)))])
(f)t))/(((((((1))cccc2eeeee specific SMARTS patterns [[77)-cin[eph Jnc2 (ei(()))-((())) [ophö] Je1211 and (2) the molecule's Tanimoto similarity to PICO ccccoclec2aene (No3coofncscte3) c2ce11 (-0) (C)) (c)er.
excluding
maxjak3 maxqed minsa
objECтIVE: has higher QED score, higher JNK3 score, and lover åA score TASK: ORD, (Synthetic Accescibility) and JNK3 scores.
CANBCTIVR-DEFINITIOO None
maxgsk3b, max.adddiisds
ORJECTIVE: has higher QED score, higher CSX39 score, and a lower SA score ORD, (Synthetic Acceasibillty)) and GSK3/ PRJECTIVE_DEPINITTE
Noon
maxjnk3 maxqed mineeed_miiaaa_eee mindd_ 3b
OBJECTIVE: has higher QED score, higher JNK3 scoze, a lower GSK39 score, lover 2802 and lower SA TASK: ORN, SA (Synthetia acoessibiltty), JNK3, GSx38 and DRn2 scores
PRIRCTIVN_DEPINTTTON Rone
2rgp docking
OBJECTIVE: binds bettez DGFR TASK: decking scores to EGFR
OBJECTIVE_DeR DECCIIV_DEFINNTION The docking
hox well molecula hinds EEFR. lower docking score generally catea a stronger or more favorable birding affinity,
3pbl_docking
OBJECTIVE: binds bette: to DRD3 TASK: decking
DRD3
OEUECrIwE_DEEINTTTESS The docking score measures how well molecule binds to DRD3, A dooking
generally indicates stronger favorabla hirding affinity,
Seml docking
овJECтIVE: binds better to adenosine seceptor A2a TASK: docking scores to adenosine zeceptor A2a BBJECTIVE_DEFLLITIIN The docking receptor A2a. lower docking score generally indicates a stronger or more fovorshla o'rding artinity.
how well molecule binds adenosine
34


Reviews:
[{"review_id": "QcHSzH4exf", "reviewer": "Reviewer_wNKW", "summary": "This paper introduces MOLLEO (Molecular Language-Enhanced Evolutionary Optimization), which is a framework that combines Large Language Models (LLMs) with evolutionary algorithms for molecular discovery. The study integrates three LLMs - GPT-4, BioT5, and MoleculeSTM - into genetic operators such as mutations and crossover operations for molecular generation. The experimental results span multiple tasks, including property optimization, molecular rediscovery, and structure-based drug design, where MOLLEO demonstrates superior performance over its baseline models. Among the tested models, MOLLEO with GPT-4 achieves the best results in 15 out of 23 tasks. Furthermore, this study tackles more challenging scenarios such as protein-ligand docking tasks, which better represent real-world molecular generation conditions.", "questions": "1) The authors propose using LLMs for genetic operators (mutation and crossover), but how does this approach fundamentally differ from existing methods that use deep neural networks for learning genetic operators? What unique capabilities do LLMs bring to molecular generation that cannot be achieved through existing deep learning-based mutation and crossover operations? The authors may consider that LLMs do not require additional training for genetic operator learning, as they can be adapted simply by modifying prompts. However, I believe that the inference process for LLMs could introduce a computational cost comparable to training deep neural networks for genetic operators. Furthermore, while the authors may suggest that LLMs can facilitate the generation of molecules aligned with objective fitness functions through descriptive prompts, deep neural networks trained specifically for genetic operations can offer distinct advantages. By learning from a dataset of molecules, they can capture meaningful molecular representations and generate novel combinations that are data-driven rather than randomly assembled.\n\n2) The authors state that in Graph-GA, the default crossover and mutation operators are pre-defined by domain experts based on chemical knowledge. How can we be confident that LLMs naturally incorporate this domain-specific knowledge and execute these crossover and mutation operations accurately, rather than merely presenting empirical results? What evidence supports the notion that LLMs inherently understand and replicate these chemically-informed operations?\n\n3) The authors state that they examined whether the open-source models generated molecules previously seen during training and found no overlap between generated molecules and the datasets. However, given that LLMs are trained on extensive chemical literature, including popular molecular objectives like JNK3, GSK, QED, and DRD2, could there still be a risk of data leakage? Specifically, if LLMs have learned about these well-known objectives, they may inherently recognize and prioritize molecular structures that are known to perform well for these targets. If the LLM’s prior knowledge is being leveraged intentionally, how does this approach handle genuinely novel molecular objectives that the LLM has never encountered? In cases where the LLM lacks specific training for unfamiliar objectives, how can it be expected to perform effective genetic operations without relying on prior knowledge, especially when such knowledge may be absent or limited?\n\n4) Building on the previous question, methods like REINVENT and Augmented Memory can optimize molecules from scratch, allowing them to adapt effectively to entirely new and unseen objectives. Given that MOLLEO relies on LLMs with pre-existing chemical knowledge, can it perform better when optimizing for novel molecular targets with limited or no prior information? How does MOLLEO address this challenge to ensure effective adaptation and optimization for objectives that the model has never encountered?\n\n5) The authors employ GraphGA as the backbone model, but why was this specific choice made? Other genetic algorithm-based methods, such as GPBO, or even other generative models like Augmented Memory, could also be suitable. For instance, in molecular generation, LLM-based genetic operations could be implemented and integrated into a replay buffer to iteratively refine results. This approach seems feasible, so why did the authors choose to rely solely on GraphGA as the backbone model?\n\n6) In their multi-objective experiments (shown in Table 2), where mixed objectives are used (e.g., maximizing QED while minimizing SAscore), the authors do not explicitly explain how they handle the differences between minimization and maximization objectives. Do they simply flip the sign of the minimization objectives to treat them as maximization objectives? If so, is this approach sufficient for effectively balancing objectives with inherently different goals?\n\n7) In Figure 2, there appears to be a typo where \"LLM editting\" is used. Should this perhaps be corrected to \"LLM editing\"?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The primary strength of this paper is its straightforward implementation of integrating LLMs into evolutionary algorithms for molecular optimization. This makes it easy to follow and understand the core mechanism of the proposed framework. Another strength is the framework's flexibility in incorporating different types of LLMs, from commercial (GPT-4) to open-source models (BioT5 and MoleculeSTM), making it adaptable to different resource constraints and use cases. This study also demonstrates practical applicability by showing how MOLLEO can improve upon existing molecules in existing chemical datasets like ZINC 250K, indicating its potential value for real-world drug discovery pipeline.", "weaknesses": "The weakness of this paper is particularly regarding the novelty of the methodology. The core idea of MOLLEO is essentially a straightforward substitution, where genetic operators in the existing evolutionary algorithm framework (Graph-GA) are replaced with LLMs. Fundamentally, this method mainly involves leveraging LLMs to perform mutation and crossover operations, which could be seen as a relatively simple extension of existing approaches. \n\nFurthermore, the paper lacks a detailed explanation of how the authors address multi-objective optimization settings. While they acknowledge that determining the weight for each objective function in a sum-aggregate objective could be challenging, they do not specify how these weights are assigned in their experiments. \n\nAdditionally, the authors propose handling multi-objective settings using Pareto set selection, where only the Pareto frontier of the current population is retained. However, this approach is a standard practice in evolutionary algorithms, as seen in well-established methods like NSGA [1] and MOEA/D [2], which leverage Pareto ranking. The authors might assume that Pareto set selection is less frequently applied in molecular optimization, this concept has already been widely applied in molecular optimization problems in previous studies [3-5]. Though the authors do not claim a novel contribution in using Pareto set selection, the paper would benefit from a more thorough discussion of their approach to multi-objective optimization and clearer positioning within the broader landscape of Pareto-based optimization approaches.\n\n[1] Deb, Kalyanmoy, et al. \"A fast and elitist multiobjective genetic algorithm: NSGA-II.\" IEEE transactions on evolutionary computation 6.2 (2002): 182-197.\n\n[2] Zhang, Qingfu, and Hui Li. \"MOEA/D: A multiobjective evolutionary algorithm based on decomposition.\" IEEE Transactions on evolutionary computation 11.6 (2007): 712-731.\n\n[3] Sun, Mengying, et al. \"Molsearch: search-based multi-objective molecular generation and property optimization.\" Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. 2022.\n\n[4] Zhu, Yiheng, et al. \"Sample-efficient multi-objective molecular optimization with gflownets.\" Advances in Neural Information Processing Systems 36 (2023).\n\n[5] Shin, Dong-Hee, et al. \"DyMol: Dynamic Many-Objective Molecular Optimization with Objective Decomposition and Progressive Optimization.\" ICLR 2024 Workshop on Generative and Experimental Perspectives for Biomolecular Design.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "G64MM0XrAz", "reviewer": "Reviewer_XR1K", "summary": "This work discusses an approach to molecular discovery, an optimization problem that faces challenges due to non-differentiable objectives. Evolutionary Algorithms (EAs) typically explore chemical space through random mutations and crossovers, which requires numerous costly evaluations. This work integrates chemistry-aware Large Language Models (LLMs) into EAs, redesigning crossover and mutation operations based on chemical knowledge from LLMs.", "questions": "1. My main concerns is the correctness of this method. How to ensure LLMs can produce better offerspring based on the description and fitness value? I believe the molecular is so complex that it is very difficult to figure out which component contributes to the high fitness.\n\n2. The abstract states that the proposed method addressed problem of expensive evaluation. However, I didn't see any relevatnt discussion on it. It is solved by surrogate model normally in evolutionary computation.\n\n3. I am uncertain about the evaluation function. Could you clarify what is meant by 'black-box'? In my experience, fitness is typically determined either through practical experimentation or by calculation using a specific function.\n\n4. How to turn molecule into text description so that it can be processed by LLMs?\n\n5. I am curious the scale of the molecule and its text description. If the scale is too big, will LLMs suffers the risk of correctness? I mean, the generated molecule is probably infeasible?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The strength of the proposed Molecular Language-Enhanced Evolutionary Optimization (MoLLEO) framework lies in its innovative integration of chemistry-aware LLMs (GPT-4, BioT5, MolSTM) into Evolutionary Algorithms (EAs) to improve molecular discovery. By using LLMs as genetic operators in crossover and mutation, MoLLEO enhances proposal quality and accelerates the optimization process. The framework’s effectiveness is demonstrated across multiple black-box optimization tasks, including complex ones like protein-ligand docking, outperforming baseline EAs and other optimization methods.", "weaknesses": "1. The diagram lacks clarity. Based on the description, this work employs LLMs designed solely for text, but Figure 1 appears to suggest the use of an MLLM.\n\n2. The presentation of the proposed method should be improved. For instance, there is no justification provided for using different prompts for LLMs like GPT-4, BIOT5, and MOLSTM. Introducing these LLMs could be moved to the appendix, and the comparison of the designed strategies would benefit from a clearer presentation, such as in a table format.\n\nMore concerns can be found in the \"Questions\".", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KEz4ba7Unz", "reviewer": "Reviewer_TwFV", "summary": "The paper introduces MOLLEO, an algorithm that combines chemistry-aware large language models (LLMs) with evolutionary algorithms to optimize molecular properties efficiently. The LLMs guide crossover and mutation operations, reducing evaluations needed in black-box optimization for molecular discovery.", "questions": "Question 1: Could the authors provide a computational cost comparison between MOLLEO and traditional methods, given the LLM integration?\n\nQuestion 2: How does MOLLEO balance chemical space exploration with optimization, and could additional mechanisms for diversity be beneficial?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "MOLLEO showcases a novel application of LLMs within evolutionary processes, yielding superior performance across 26 molecular design tasks and showcasing promise in early drug discovery.", "weaknesses": "MOLLEO’s reliance on proprietary LLMs may limit reproducibility, and the diversity of the chemical space explored could be improved for broader molecular applications.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kq7nrSdqZ4", "reviewer": "Reviewer_bF3X", "summary": "This paper presents a novel approach to molecular optimization by replacing the crossover and mutation operations in GraphGA with Large Language Models (LLMs) to enhance sample efficiency. Instead of employing LLMs for end-to-end molecule generation, the study uniquely integrates LLM capabilities into specific genetic algorithm operations (crossover, mutation), effectively combining the strengths of LLMs with those of GAs. I really enjoyed reading this paper and think it is a good one. However, the method seems too simple, and I believe that if the methodology is reinforced, it could become an excellent paper.", "questions": "1.\tIn replacing the crossover and mutation operations within GraphGA, did the LLM genuinely perform these operations, or did it retrieve known SMILES strings based on the provided inputs? For mutation, does the LLM modify only certain tokens in the existing SMILES, similar to the actual mutation operation, or does it reconstruct the entire sequence? Regarding crossover, how closely does the LLM's output resemble true crossover? Given the prompt used—You can either make crossover and mutations based on the given molecules or “just propose a new molecule based on your knowledge”—it's important to understand the ratio of outputs resulting from crossover, mutation, and LLM retrieval.\n2.\tThe objectives in the PMO benchmark are well-known objectives, with extensive experimental data available. Consequently, the LLM might already be familiar with molecules that perform well for these objectives, especially for targets like JNK3 mentioned in the introduction. Is there a possibility that the LLM is retrieving and outputting known high-performing molecules rather than generating novel ones? Verifying this would be crucial to assess the true innovative capacity of the approach.\n3.\tBuilding on the previous question, did you intentionally leverage the LLM's existing knowledge to rapidly improve performance, perhaps aiming for a zero-shot learning scenario? If so, this strategy has merit. However, it raises concerns about the method's applicability to entirely new objectives where the LLM lacks prior knowledge. How do you address this potential limitation?\n4.\tAs highlighted in Weakness 2, the evaluation of MolLEO is confined to GraphGA. To demonstrate its generalizability and robustness, it would be beneficial to apply the MolLEO framework to other models like Genetic GFN and Saturn.\n5.\tAll the score curves in the paper compare GraphGA and MolLEO. Including Augmented Memory in these comparisons would provide a more comprehensive evaluation of MolLEO's performance relative to other established methods.\n6.\tIn Table 2, the \"Sum\" appears to represent the sum of AUC top-10 scores. To prevent confusion—since row “sum“ and column “sum“ are denoted same—please clarify this in the caption or relabel it as \"Sum(AUC).\" Alternatively, consider using a term like \"Scalarization\" to more accurately reflect the data presented.\n7.\tSince the objectives involve both maximization and minimization, summing their scores may not straightforwardly reflect overall performance. Maybe you applied simple (e.g., using 1−score) score for minimization objectives for scalarization. It's important to explicitly detail these methods to ensure the validity and reproducibility of your results.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.\tUnlike conventional end-to-end LLM methods, the paper strategically replaces only the GA operations where LLMs can be effective. This selective substitution leverages the advantages of LLM and genetic algorithm.\n2.\tIt proposes a prompting method that utilizes LLMs to improve performance in molecular optimization tasks. This approach opens avenues for further advancements if combined with additional methods.", "weaknesses": "1.\tWhile the main contribution appears to be the replacement of evolutionary algorithm (EA) components, the paper lacks a comprehensive analysis of existing EA methods. Particularly in multi-objective optimization, evolutionary algorithms like MOEA/D and NSGA-III [1, 2]. The study introduces Pareto Optimality (PO) using the Pareto Front but does not compare it with these established multi-objective optimization EAs. Additionally, the paper DyMol [3] has proposed a Pareto Sampling method similar to the PO in this study, which should be acknowledged.\n\n2.\tThe MolLEO framework is applied only to GraphGA, which limits its demonstrated versatility. Recent works like Genetic GFN [4] and Saturn [5] have enhanced performance by incorporating crossover and mutation into methods like Augmented Memory and GRU-based GFlowNet. Applying MolLEO to these methods could further validate its effectiveness.\n\n3.\tThe paper could better articulate why specific objectives were chosen, such as maximizing JNK3 and minimizing GSK3β in objective selectivity tasks. Providing rationale for these choices would strengthen the study's context and applicability.\n\n[1] Zhang et. al. (2007) \"MOEA/D: A multiobjective evolutionary algorithm based on decomposition.\" IEEE Transactions on evolutionary computation\n\n[2] Deb and Jain, (2013) \"An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints.\" IEEE transactions on evolutionary computation \n\n[3] Shin et. al. (2024) \"DyMol: Dynamic Many-Objective Molecular Optimization with Objective Decomposition and Progressive Optimization.\" ICLR 2024 Workshop on Generative and Experimental Perspectives for Biomolecular Design. \n\n[4] Kim et. al. (2024) \"Genetic-guided GFlowNets: Advancing in Practical Molecular Optimization Benchmark.\" Neurips.\n\n[5] Guo, and Philippe, (2024) \"Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation.\" arXiv preprint arXiv:2405.17066.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)