SmilesFormer: Language Model for Molecular Design
Authors: Joshua Owoyemi
Nazim Medzhidov
Venue: Submitted to ICLR 2023 (2023)
Keywords: De novo drug design
Language model
Molecule Optimization

Abstract: The objective of drug discovery is to find novel compounds with desirable chemical properties. Generative models have been utilized to sample molecules at the intersection of multiple property constraints. In this paper we pose molecular design as a language modeling problem where the model implicitly learns the vocabulary and composition of valid molecules, hence it is able to generate new molecules of interest. We present SmilesFormer, a Transformer-based model which is able to encode molecules, molecule fragments, and fragment compositions as latent variables, which are in turn decoded to stochastically generate novel molecules. This is achieved by fragmenting the molecules into smaller combinatorial groups, then learning the mapping between the input fragments and valid SMILES sequences. 
The model is able to optimize molecular properties through a stochastic latent space traversal technique. This technique systematically searches the encoded latent space to find latent vectors that are able to produce molecules to meet the multi-property objective. The model was validated through various de novo molecular design tasks, achieving state-of-the-art performances when compared to previous methods. Furthermore, we used the proposed method to demonstrate a drug rediscovery pipeline for Donepezil, a known Acetylcholinesterase Inhibitor.

Under review as a conference paper at ICLR 2023
SMILESFORMER: LANGUAGE MODEL FOR MOLECU- LAR DESIGN
Anonymous authors Paper under double-blind review
ABSTRACT
The objective of drug discovery is to find novel compounds with desirable chemical properties. Generative models have been utilized to sample molecules at the inter- section of multiple property constraints. In this paper we pose molecular design as a language modeling problem where the model implicitly learns the vocabulary and composition of valid molecules, hence it is able to generate new molecules model, which able
interest. We present SmilesFormer, a Transformer-basse to encode molecules, molecule fragments, and fragment compositions as latent variables, which are in turn decoded to stochastically generate novel molecules. This is achieved by fragmenting the molecules into smaller combinatorial groups, then learning the mapping between the input fragments and valid SMILES se- quences. The model is able optimize molecular properties through stochastic latent space traversal technique. This technique systematically searches the en- coded latent space find latent vectors that able produce molecules meet the multi-pros property objective. The model was validated through various de novo molecular design tasks, achieving state-of-the-art performances when compared previous methods. Furthermore, we used the proposed method to demonstrate a drug rediscovery pipeline for Donepezil, a known acetylcholinesterase inhibitor.
INTRODUCTION
Generative models play a major role in discovering and designing new molecules, which key to innovation in in-silico drug discovery Kodaa & Gömez--Bomarell 2020). There is a vast amount of molecular data available, therefore generative models should be able learn concepts of valid and desired molecules using data-centric approach. However, due to the high dimensionality of the molecular space (Probst& & Reymond 2020) and the substantial number of data samples, is still challenging to traverse the space of valid molecules that satisfy desired objectives of de novo molecular design. Other challenges that have been identified for de novo molecule generation task brute-force trial and error approach to searching for hit compounds, the difficulty in designing an effective reward function for molecular optimization, sample efficiency due online oracle calls during molecule optimization (Fu et al.) 2022). Addressing these challenges is still an active area of research with various approaches such as Reinforcement Learning (Zhou et al. 20i9) et al. 2020b). Genetic Algorithms (Wüthrich al.) 2021). Variational Auto-Encoders (VAEs) (Kusner et al. 2017) and Generative Adversarial Networks (GANs) (Schwalbe-Koda & GömezzBombarelli 2020 Prykhodko et al. 2019).
include the reliance
In this work, we employ Transformer-based language model (Vaswani et al. 2017) to encode molecular latent space by generating valid molecule sequences from fragments and fragment compositions. The encoded latent space can then be explored to generate molecules (represented by SMILES strings (Weininger 1988)) that satisfy desired properties.
Similar to Gao et al. (2022) who addressed the problem of synthesizability by modeling synthetic pathways within the molecule design pipeline, we leverage data to minimize costly experiments in downstream generation and optimization tasks using a fragment-based approach that associates synthesizable building blocks with target molecules during training. Our model, however, introduces an online fragmentation approach, removing the need to create separate fragments dataset while learning the intrinsic relationship that makes up the formation of a valid SMILES string. This is essentially approach for learning the SMILES language model.

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While SMILES has been seen to be less informative compared to other forms of molecule repre- sentation, we argue that it is simple and easy to follow as it is linear walk through the molecular structure. Also, see the non-canonical property shown to benefit the training of generative models (Coley 2021). We explored this idea by using only non-canonical SMILES input to our model and teaching the model generate canonical SMILES.
form of data augmentation which has been
Our contributions are summarized as follows: (1) We propose an approach for learning efficient representations of the molecular space using molecule fragments. Our fragment-based training pipeline constrains the model to learn the building blocks necessary to generate stable molecules while also meeting multi- property objectives. (2) We present an optimization strategy that efficiently traverses the molecular space with flexible parameterization. (3) We demonstrate a de novo molecular optimization practical use case with rediscovery pipeline of an established acerylcholinesterase inhibitor.
2 RELATED WORK
The majority of generative models focus on the generation of valid molecules with desired properties (Gao al.) 2022), however with great reliance on domain knowledge in medicinal chemistry to help guide the generation process. For example in Jin et al. (2018), an approach based on VAEs (Kingma Welling 2014) generates a molecular graph by first generating strustruct scaffold over chemical substructures. This involved building cluster vocabulary that increases as the size of the considered chemical space increases. Other more recent state-of-the-e approaches are based on genetic algorithms through expert-guide learning (Ahn et al. 2020 Nigam et al. 202 Wüthrich ctt al. 2021). differentiable scaffolding tree aided by a graph convolutional knowledge network (Fu et al. 2022), and VAE reinforcement learning with expert designed reward function explore encoded latent space (Zhavoronkov et al., 2019).
Our work is motivated by Fabian et al. (020)) who made earlier attempt at using Transformer- based language model applied over a vast amount of data to learn the representation of molecules for deleintrelevant auxiliary tasks, however, did not explore de novo molecular design. Defining molecular design as a language problem enabled us to move away from heavy reliance on expert knowledge and allows datasets. Intuitively, we see fragments as similar to words or phrases in a language sentence. Hence, learning the relationship between fragments and full sequences means a full sequence can be generated from fragments or even a combination of fragments. The most similar works to this idea use molecule fragments as the building block for molecule generation pipelines (Polishchuk) 2020). showing that fragment-based approaches are in-hetween atom-based approaches and reaction reaction-based approaches. While the approach utilizes a database of known compounds to perform chemically reasonably mutation of input structures, ours relied on an online stochastic fragmentation process based on a retrosynthetic combinatorial analysis procedure (RECAP) (Lewell et al., 1998) provided by the RDKit library (Landrum 2022). This fragmentation process only used during training and fragments are not stored for later use. Another method (Firth et al. 2015) coupled a rule-based fragmentation scheme with fragment replacement algorithm broaden the scope of re-connection options considered in the generation of potential solution structures. Fragments were used optionally in Nigam et al. (202l) bias the operators in the genetic algorithm pipeline. The effect of this optional bias, however, was not reported.
choose method that can scale over a massive amount of available
Similar to the REINVENT Blaschke al., 2020), which was developed as a production-reade tool for de novo design. applicable to drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space, we adopt transfer learning approach that utilizes pretrained generative model as a prior to further train smaller set of compounds which are relevant for the desired outcome in downstream tasks.
To evaluate generative models for molecular design, distribution benchmarks and goal-directed benchmarks (Brown et al. 2019 Polykovskiy et al., 2020) have been proposed. However, since the task of molecular design is usually related to specific targets, earlier proposed benchmarks do not fully represent how a generative model is utilized in the drug discovery pipeline. We observe that recent works have been more focused on multi-objective property optimization this is more practically useful. Aided by standardized libraries like TD Commons (Huang et al. 2021), recent works from Jin et al. 2020b): Nigam al. (202T): Fu et al. 2002), and Gao al. (2022) evaluated their models
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using optimization for activities such JNK3 and GSK33 3 (Li al. 2018 in combination with QED (Bickerton et al. 2012) and Synthetic Accessibility (Ertl Schufienhauer 2009). In this work, we further evaluate approach by simulating known drug discovery pipeline to demonstrate how model can be utilized in a more practical setting.
3 METHOD
3.1
SMILESFORMER MODEL
Our generative pipeline is Transformer-basee VAE, trained to obtain non-deterministic outputs of a molecular sequence. In our implementation. we constrain the latent representation between the encoder and decoder on the standard Normal distribution to achieve the variational auto-encoding. More details on the Transformer model is in section and our model's (SmilesFormer) architecture and molecule optimization overview is shown figure
The model accepts input sequence tokens $1, ..... SK, which can be from full sequences, partial sequences, fragments, fragment compositions or an unknown fragment (Figure [ combined with query sequence tokens .l.......1 (r,, mapped to probability distributions of output tokens P(x1), ...., P(xT). The encoder encodes the input sequence obtain latent space representation constrained on the standard Normal distribution p(z) N [Oa, Ia,), such that ~ where dg is the dimension of the latent space. The decoder g(z, Q) then output tokens probability:
P(XIQ, 2) - (c.)))
(1)
where ~ Xet In a greedy approach, the model obtains the output token T, argmax ((X)).
The parameters of the encoder fg are trained to minimize the Kullback-Leibe -Libleer (KL) D (.11.) divergence between the latent space distribution and Normal distribution:
[(0x) (pz
The parameters of the decoder In are trained to minimize the Negative Log-Likelihood:
N L(OD) = Σ Im LnP(x,=x,12,,
(3)
For weight variables we and wD used balance the encoding objective and the sequence reconstruc- tion objective respectively, the final objective function is then given by.
L(0g, 0D) we I(Og) + wD I(Op)
3.2 MOLECULE OPTIMIZATION
The objective of molecule optimization is to take molecule X as input and output molecule X that is similar to and has more desirable properties than X. We formulate latent space manifold sampling strategy based on Mueller al. (2017) which operates under the standard supervised learning setting, assuming the availability of dataset D. = (Ii, y:); Pay of sequence-outcome pairs. The distribution Px from the outputs our learned decoder and Py is the distribution of molecule scores. By fitting models to D,,, we seek to obtain revised sequence:
argmaxzeCy F
(5)
where X is set of valid sequences or revisions, that is molecules that satisfy the multi-property objective. The relationship between X and is parameterized by a simple feedforward network. Given input sequence X be optimized, first compute its latent space representation 20 E(S, p(2)) using our trained encoder. Using the gradients of the local optimum of the simple feedforward network, obtain updated latent variable =* z + a grad(z), where is
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Dusputs
Imputs
Latent Space Halleyy
Input
*
Transfomerr
Muki cuje-cbjeectvve 400
Optimization
Quary "Cei"
QED 0.6 SA 0.4
gradfa)
Latent space revision
Figure 1: Overview of the SmilesFormer model and molecule optimization. The model is VAE-based eqquence-to-sequenee Transformer. The inputs into the model can be full sequences, fragments, fragment compositions, dummy token. The input query sequence dictates the starting sequence for the output molecules.
learning rate constant. Our revised sequence X D(Q, 2 is then obtained using the trained decoder. For scoring, we use a weighted sum of scores of individual properties c,.
Σ my (x()) Σ Wi
(xx)
Intuitively, the latent space manifold can be seen as a grid with nodes representing latent variables that satisfy certain multi-objective constraints. The optimization procedure, therefore, traverses this latent space towards node that satisfies our chosen multi-objective properties, Each step of the latent space update produces variables that are judged whether to better worse than previous step. Essentially, when node has better evaluation than a previous node, the latent variables are further updated in that direction. To halt the optimization, we introduce maximum history constant. This the number of previous nodes which can be revisited when subsequent node gives worse outcome. The process can also be halted by the maximum number of molecules expected to be generated.
See algorithm
[
3.3 MODEL INPUTS AND AUGMENTATION
Molecules fed into the model by randomly choosing between two fragmentation strategies and full sequence transformation. This helps to generate wide range of input into the model and serves input augmentation. In the case of full sequence, the input canonical SMILES are transformed into cano-ncniiall SMILES by changing the starting atom from which the SMILES string is formed. Note that while this done, the canonical SMILES are expected model output. This ensures that the model does not simply memorize the mapping between the input and output but is able to learn the intrinsic vocabulary of the SMILES strings. Examples of the SMILES transformation are shown
in figure 2 In the case of fragmentation, we use two strategies follows:
This strategy breaks down a molecule at the single bonds, resulting in fragments that indicate functional groups without the characteristic elements (Wüthrichl ct al. 202I). The intuition is that this should help the model to learn the general structure of molecule without specific functional groups and atoms.
Fragment-oon-ooete
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Algorithm 1 Molecule Optimization Algorithm
Seed sequence žo X
Query sequence y E Q
Learning rate E (0, 1)
Latent vectors 20 + (zo. (())
History H
[20]
score Y +-
while len H)) * do
I; -- g(2;.0) Q)
yi ((;;)
2 Y then
Y Yi
+ 2, + x grad(z))
H < [a.,.. H Marr)
else
2: + H.pop()
end if end while
Recap decomposition: Based on a function supplied by RDKi Lewell et al. 1998), this strategy uses a set of chemical transformations mimicking common reactions carried out in the lab in order to decompose molecule into series of reasonable fragments. This kind of fragment more complete as it includes characteristic elements and structures that form functional group in the molecule. See fragmentation examples in figure
Finally, the inputs are tokenized as in Schwaller et al. (2019) where each part of the SMILES string is represented by a single token allowing any arbitrary molecule to be transformed into the model input. To allow fragments composition in which multi- ple fragments can be fed into the model. we introduced a new token, [COMP), as in [[Jddcc(((NV======)c)]]]]]C
m/a/////EE same as combining the fragments in figure a) into single model input.
This is the
Tclo:s5(NNNOO0-OP (k (ctos())cc
["]ctecct] "jcet (*((")X-0))
cttC)ad F2att (an-iiin2--aaaaaa (cad5500(-0))-)ua (io(CiF)FFF)uut (2aa(() (0) OXX0=OX-0xleee -ineeee-neeerrrri Cie2-cc ox)ceee inteetii GPPP (hre peee-22enn Gieetjen -oeeeert--neeeer
Figure 2: Model inputs and augmentation. The canonical SMILES are randomly fragmented by (a) Recap decomposition (b) Fragment-on-bonds. transformed into (c) non-canonical full
sequence. The [*] in the fragments signify connection points.
3.4 MODEL STOCIASTICITY AND LATENT SPACE EXPLORATION
Dropout was introduced as a simple way to prevent neural networks from overfitting (Srivastaval et al. 2014). However, it has recently been used as Bayesian approximation to represent model uncertainty (Gal & Ghahramani] 2016), showing that using dropout mathematically equivalent to approximation to the probabilistic deep Gaussian process. In this work, utilize dropout to introduce stochasticity in the generator model thereby allowing us to explore multiple decodings
https//www..dkit.rrg

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of a single latent space vector. This means that each vector be sampled severally produce different output sequences. To our knowledge, the only other work in which a similar idea has been explored is in the use of dropout as auxiliary exploration to reinforcement learning for continuous action problems (Sung et al. 2018).
3.5 TRANSFER LEARNING FOR SPECIFIC TASKS
To apply the trained model to specific tasks, the model is pre-trained with generative capacity and the potential to sample compounds from a vast area of the available chemical space. This pre-trained model becomes a prior for training for specified tasks over smaller set of compounds that relevant for the given task. For example, if we aim to optimize generated molecules for a certain property, we would curate a dataset of molecules that were evaluated for this property. This will result in a model that produces molecules similar to the target dataset with higher probability. Subsequently, generated molecules can be scored in order to sample the most promising molecules for the chosen target. We demonstrate this pipeline in section 4.2
EXPERIMENTS
We carry out experiments on the proposed model with various de novo molecular design tasks. Evaluations are based on previously proposed tasks (Jin et al., 2020b) Nigam al., 202T] Fu et al.]
2022). We also demonstrate drug rediscovery pipeline for donepezil, a known acetylcholinesterase inhibitor (Sugimoto et al. 2002). Our model is trained on the ChEMBL dataset (Mendez et al. 2018). containing 1.9 million molecules. Actual training molecules were limited to molecular weight of 900 g/mol making the trained model more applicable to a small molecules setting. To encourage diversity in the generated molecules, query tokens were obtained from the training dataset by first tokenizing the molecules, then selecting a certain number of start tokens. The model generates molecules based on the query tokens by predicting the remaining sequence.
4.1 MULTI-OBJECTIVE de novo DESIGN
We consider the following target molecular properties: (1) Quantitative estimate of drug-likeness (QED) (Bickerton et al. 2012]; (2) Octanol-water partition coefficient (LogP) (Kusner et al. 2017). an indicator for the molecule's 's solubility in water; (3) Synthetic Accessibility (SA), which is measure of how hard or easy it is to synthesize a given molecule; (4) Inhibition against c-Jun N-terminal Kinases-3 (JNK3), belonging to the mitogen-activatee activated protein kinase family, and are responsive to stress stimuli, such as cytokines, ultraviolet irradiation, heat shock, and osmotic shock; and (5) Inhibition against Glycogen synthase kinase 3 beta (GSK339), an enzyme associated with an increased susceptibility towards bipolar disorder. (4) and (5) are evaluated using predictive models trained on positive and negative compounds according to Li et al. (2018). We conduct molecules generation that optimizes the mean value of "JNK3+GSK33" and "ODD-SA+INK+GGS33" and compare our results against state-of-the-an artt generative methods; (1) Molecule Deep Q-Network (MoIDQN) (Zhou et al. 2019). which combines state-of-the-aa reinforcement learning techniques with chemistry knowledge optimize molecules; (2) RationaleRL (Jin et al., (020a)), which builds vocabulary of substructures to be expanded into full molecules; (3) Markov Molecular Sampling (MARS) (Xie al. 202T), which employs Markov chain Monte Carlo sampling on molecules.; (4) Differentiable Scaffolding Tree (DST) (Fu et al. 2022). which utilizes a learned network to convert discrete chemical structures locally differentiable one; and (5) Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks (JANUS) (Nigam et al., 2021). which propagates two populations, one for exploration and another for exploitation, improving optimization by reducing expensive property evaluations.
The results show that SmilesFormer performs comparatively with the state-of-the-arr approaches, outperforming metries such as success rate and novelty. Our model can also find the best scoring molecules in all the tasks except for QED+SA+INK++GSS3. SmilesFormer, however, does not perform well on the diversity metric. This could be primarily due starting sequences, which may lead to many similar molecules being generated for the same starting sequence. Qualitatively, molecules from SmilesFormer tend to be much simpler compared with other methods. We believe this

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Table I: Results of Multi-objectivv de novo design
JNK3+GSK3
QED+SA+INK3+GSS33
Method
Best
SR
Nov
Div
Best
SR
Nov
Div
MolDQN
0.46
0.028
1.000
0.605
0.45
0.127
1.000
0.599
RationaleRL.
0.81
1.000
0.973
0.824
0.75
0.748
0.568
0.701
MARS
0.78
0.802
0.979
0.699
0.72
0.734
0.984
0.714
JANUS
1.000
0.744
0.877
1.000
0.184
0.841
DST
0.89
0.920
0.975
0.745
0.83
0.798
0.988
0.774
SmilesFormer
0.90
1.000
1.000
0.715
0.805
1.000
1.000
0.726
Table Results of single objective de novo design
JNK3
GSK333
Method
Best
SR
Nov
Div
Best
SR
Nov
Div
MolDQN
0.64
0.63
0.63
0.54
0.53
0.53
0.53
0.53
RationaIRI.
0.97
0.97
0.97
0.95
0.95
0.95
0.95
0.95
MARS
0.92
0.91
0.90
0.95
0.93
0.92
0.93
0.92
JANUS
1.000
0.744
0.877
1.000
0.184
0.841
DST
0.97
0.97
0.97
0.95
0.95
0.95
0.95
0.95
SmilesFormer
0.99
1.000
1.000
0.737
0.99
1.000
1.000
0.725
because the model outputs are constrained due to the learned association with the input fragments. Hence, generated molecules are easily decomposable into individual fragments. See figures and
4.1.1 ORACLE EFFICIENCY
Based on previous works, realistic molecule optimization tasks can be time-consuming and expensive, therefore it is necessary evaluate the efficiency oracle calls needed for generative model to reach the multi-objectinn efficiency of our approach other state-of-the-ae methods, shown Figure Bl Compared other methods, our method showed significant advantage by reaching optimum performance with much fewer oracle calls. By leveraging model parallelization, our model can suggest multiple candidates per iteration thereby speeding up the optimization process.
goal. We follow the example in Fu et al. (2022) to compare the oracle
INK3
GSK38
JNK3+G5K30
MoIOON Rational RL
0.8
0.8
0.4
Ours Hop 1) Ours (Tp 100)
0.4
0.2
Figure 3: Oracle efficiency test. Top-100 average score v.s. the number oracle calls. Top results for Smilesformer are also shown. The results have been limited to 10000 molecules since our model reaches optimal molecules much faster than other methods.
4.2 DONEPEZIL REDISCOVERY
In this section, drug discovery pipeline is demonstrated using model and optimization strategy. Our goal was to evaluate the capability the presented approach in generating novel scaffolds with complex molecular structures not represented in the training data. As protein target, selected
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acetylcholinesterase (AChE), which has been successfully targeted to improve cognitive function in Alzheimer's s disease with molecules such as tacrine, physostigmine, rivastigmine, galantamine, and donepezil that exhibit diversity in molecular scaffolds (Marucci al. (02I). This allowed us to remove data associated with one of the chemotypes (i.e donepezil) from the training data to test our approach while having enough data for training. The removed data was used for evaluating if SmilesFormer can generate and rank as top suggestions any of the hidden known potent AChE inhibitors. Successful generation of such molecules would indicate the ability of the model reach the potent novel scaffolds.
Due to the retrospective nature of the problem setting, one critical aspect of data preparation was to accurately remove molecules associated with the donepezil scaffold from both pretraining and training datasets. First, the pretraining dataset (ChEMBI database Gaulton et al. (201 TD) was filtered to exclude all molecules containing donepezil scaffold (Figure 4a). This ensures that our model does not simply memorize the target molecule and output it in the generation step. We further trained
the model with AChE inhibitors curated from ChEMBL database. This dataset consists of 3950 molecules with IC50 activity measurements and was also filtered to exclude the donepezil scaffold and other relevant molecules containing major donepezil pharmacophores such as 1 1-indanone, the simultaneous presence of indan with either benzylpiperazine or benzylpiperidine (Figure (4b) since such combination will have a high similarity to the pursued donepezil scaffold and make the problem setting trivial.
During generation step, multi-objective function was used to optimize the generated molecules with the following properties: (1) SA C; < 1, Target = 0; (2) QED C2 > Target = 1; (3) PLogP
C >> 0, Targt = 40; (4) Molecular Weight 250 C; 750; (5) Number of Hydrogen Bonds
5 < C, > 10; (6) Topological Polar Surface Area (TPSA) 75 < C > 150; (7) Number of Rotatable
Bonds < C2 > 10; (8) Similarity to tacrine C. < 1. T.rget = O; (9) Similarity to physostigmine
G < L, Target = 0 (10) Similarity to rivastigmine C, < 1, T,rget = O; and (11) AChE inhibitor activity PIC50 (negative log IC50), determined by a trained predictive model on AChE Inhibitor active compounds C; > 1, Target = 1. Similarity assessment to tacrine, physostigmine, and rivastigmine molecules was used to direct the model towards exploring novel scaffolds not represented in the training data, since discovering novel molecules not documented before is of high priority in drug discovery due to patentability concerns. Up to 30,000 molecules were generated and ranked according to the aggregate generative score.
In a post-processing step, the generated molecules were filtered using a set of Medicinal Chemistry Filters (MCF) remove molecules with undesirable toxic and unstable structures. Molecules were also assessed for novelty by filtering out molecules with (a) substructure match to tacrine, physostigmine, and rivastigmine scaffolds in order to avoid building on these scaffolds (b) scaffold match to the molecules in the training set, and (c) Tanimoto similarity score > 0.5 to the molecules in the training set. Moreover, AChE protein is found in the brain, thus successful molecules should be able to pass the blood brain barrier (BBB) and reach the target tissue to exert activity. Therefore, generated molecules were passed through a BBB permeability threshold of 0.99 based on a trained predictive model on BBB permeability. The final list of molecules is then clustered and ranked with a desirability score of normalized (QED- + pl C50) /2. The generation and post-processing steps were run 10 times and the top 20 scaffolds from the combined runs were considered as the final result. The top 10 scaffolds are shown in figure 9 In the final set of molccules, we found the molecule shown in figure 4c which was a late-stage intermediate molecule leading to the discovery of Donepezil (Sugimoto et al., 2002). The results show that our model is able to generate a molecule that contains the scaffold and important pharmacophores of a known potent drug, and ranks such molecule high after an extensive post-processing pipeline. This result provokes a thought of a potential presence of other potent compounds among generated molecules that are yet be tested experimentally.
4.3
EXPERIMENTS WITH OPTIMIZATION PARAMETERS
4.3.1
DROPOUT AS EXPLORATION
In this experiment, the effect of dropout was examined on the performance of the exploratory capability of our model. The final model was trained with a dropout value of 0.15 and tested in the generation and optimization step with varying dropout values. We used the same objective as in section 4.2 It can be observed that increasing the dropout value results in the model being able to
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Fittered Pottern
(a) Donepezil (top) and Scaffold (hottom)
(c) Identified Molecule
(b) Structures filtered from the training dataset
Figure 4: Donepezil rediscovery experiment pipeline.
10
500
3.0
Score
1500
306-
2000
02
veessiyy
diversity
best score
top50
top50 score
1000
scacoodd_uucooss success
355
samples
recap
Strategy
(a) Result varying dropout values on optimization results
(b) Result of fragmentation strategies on optimization results.
Figure Optimization parameters experiment result.
explore more latent space representations evidenced by the number of valid samples generated and the diversity of the molecules generated (see figure 5a). However, the model seen to perform worse when the dropout value is increased above 0.25.
4.3.2 FRAGMENTATION STRATEGIES
In this experiment, the effect of the fragmentation strategies was examined. The model was tasked with optimizing a molecule from the donepezil rediscovery experiment. This was done by setting the molecule input and applying combinations of the fragmentation strategies introduced in section The mixed strategy (see figure 5b)) randomly switches between the bonds fragmentation and recap fragmentation. The recap strategy achieves higher success in finding molecules with donepezil scaffold, however, the mixed strategy achieved a marginally higher best score and molecule diversity.
3.3
CONCLUSIONS
In this work, we introduced SmilesFormer, a language model for de novo molecule generation and optimization. Our model learns from vast amount of training data to represent and explore chemical spaces in order to generate new compounds. We also introduced the optimization strategy. which systematically and more efficiently traverses the space of latent representation of the model to generate compounds that satisfy a multi-objective function. We demonstrated promising results for variety of tasks with relevance drug discovery and molecular optimization.

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A APPENDIX
A.1 TRANSFORMER MODEL.
The transformer model a stepwise autoregressive encoder-decoder model composed of combi- nation of multi-head attention layer and positional feed- forward layers (Schwaller et al. 2019). A multi-head attention layer computes the attention as follows:
QK dde
(itention(,,Ai V) softmax
V
where K, V, and Q are the key, value, and query of the layer. The sequential nature of the data is encoded with positional encoding allow the network to know the relative position of tokens in sequence:
pos
pos
PE PEpos:21 sin
PEpos.2i-y COs
10000 Remu
10000 SENL6
Our approach treats the transformer model variational autoencoder with the aim of learning smooth latent space representation of the input data.
A.2 MODEL AND TRAINING CONFIGURATION
Dataset preparation: All preprocessing and preparation were performed online during training. In all experiments the following rules were followed; (1) Maximum molecular weight of 900, to limit molecules to small molecules; (2) minimum token length of 8; (3) maximum token length of 104;
Hardware: Model training and molecular design inference carried out the Intel(R) Xeon(R) CPU e5-2698 v4 @ 2.20GHz processors, 256GB RAM, and Tesla V100-DGXS graphics cards with 32GB VRAM.
The model's hyperparameters are as follows: 12 transformer layers, 12 transformer heads, an embedding size of 288, and 1024 hidden units. Training was done with a batch size of per GPU and optimized with the ADAM optimizer (Kingma & Ba 2015) with an initial learning rate of 0.0001. To construct the input sequence during training, the fragmentation strategies are randomly mixed with the fragment composition probability (the probability of composing 2 or more fragments as input) of 0.75, full sequence probability of 0.5, and dummy input probability of 0.05.
A.3 ADDITIONAL EXPERIMENTS
A.3.1 SINGLE OBJECTIVE MOLECULAR DESIGN
We compare the performance of various methods on single-objective de novo molecular generation for optimizing QED and LogP scores respectively. The results are shown in Table We have added results from EGEGL (Wüthrich al. 202T). An interpretablliyy-ammmeeet Genetic Expert for Deep Molecular Optimization. We show the top molecules in single, as well as multi-propen objectives optimization f
A.3.2 CORRELATION BETWEEN INPUT AND OUTPUT VALUES ON CHEMBL MOLECULES
Molecules in the ChEMBL dataset were used input into the model generate new molecule. The relationship between the input and output molecule properties is shown in figure g We observe somewhat weak correlation between input and output properties, and a visible group of outliers in properties like LogP and Molecular Weight.
A.4 EXPERIMENT WITH STARTING SEQUENCE LENGTH
We demonstrate the ability of the model to suggest complete valid sequences from partial se- quences. The model is supplied with partial sequences of molecules sampled from the AChE
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0.947
0.947
0.947
0.947
0.947
(a) Top molecules optimized for QED with scores
1.86 0.77 0.87 0.77
1.93 0.79
1.93 0.79
2.00 0.66
1.77 0.77
0.80 0.82
0.80 0.82
0.85 0.94
0.76 0.85
(b) Top molecules optimized for QED+SA+UNK3+GS335 The scores are SA, QED, JNK3, and GSK38
respectively
Figure Top molecules in single and multi-objective optimization tasks.
Seed (input) molecule
Recognized
Generated
Fragments in generated molecules
fragments
molecules
Figure 7: Fragment analysis for input and generated molecules. We observe the fragments in the generated molecules are also similar to the fragments recognized in the input molecules. The model utilizes the recognized fragments in the input molecules to generate new molecules optimized toward the objective function.
Table 3: Highest scores of generated molecules on single-objective de novo molecular generation
LogP
QED
Method
1st
2nd
3rd
1st
2nd
3rd
MolDQN
11.8
11.8
11.8
0.948
0.948
0.948
MARS
45.0
44.3
43.8
0.948
0.948
0,948
EGEGL
34.6
34.4
34.4
0.948
0.948
0.948
DST
49.1
49.1
49.1
0.947
0.946
0.946
SmilesFormer
40.8
40.8
40.4
0.947
0.947
0.947
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Fearson Cerclationn 0.3040
Carcldiin
2-4856e-156 2+
Pvalue 21526-262
Irput
Hout
(a) QED comparison
(b) SA Comparison.
rearsons corcladion 0.28-1
vallue: 2 2.2220-22 20
Pearson Conrelat on: 0.1875
P. Paalee: 1.78012 30
206
-10
Log?
Melcuala Weugh)
(c) LogP comparison
(d) Molecular Weight Comparison.
Figure 8: Correlation between input and output values ChEMBL molecules.
aapo
Figure 9: Top 10 scaffolds from the generated molecules. The numbers signify how many times the scaffold appears out of 10 experiment runs.
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(a) Start sequence "Celecee"
(b) Start sequence "CN(CCCN".
(c) Start sequence "COelec2". Donepezil is shown with green borders.
Figure 10: Molecules generated from input start sequences in a lead optimization task. The top 10 molecules from each experiment are shown. They all have the donepezil scaffold.
inhibitors as query sequences in a lead optimization task. The generated molecules are also optimized to be similar to given scaffold. The multi-objective function is set up such that
(1) Scaffold similarity c; > 0, Target = 1; (2) Scaffold presence G; = 1; (3) SA c; < Target 0; (2) QED C, 0, Target 1; (3) logP -0.4 C, 5.6; The lead molecule COc1cc<2((1))C(= (CCcCcicccC(CCcccececccC11C donepezil discovery pipeline. Results from the top 3 partial sequences shown in figure 10
an intermediate molecule in the
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0.8
0.7
Score
0.5
0.4
0.2
0.1
0.0
0.5 Sequence ity
0.25
(b) Samples of molecules generated by the model trained with full sequence probability of 0.5. The imput molecule is shown top.
(a) Result of varying training full sequence probabilities
Figure 11: Experiment results for training full sequence probability.
A.5 EXPERIMENT FULL SEQUENCE PROBABILITY
We investigate the effect of the full sequence probability parameter on the model's performance in generating similar molecules as the input. In a lead optimization task, model expected to explore the chemical space of lead molecule to generate similar molecules which could possess better properties than the lead molecule. this experiment, the model tasked with generating similar molecules to varying full sequence probabilities. Results are presented in figure 11
after being trained with
18


Reviews:
[{"review_id": "nV2Kj651w42", "reviewer": "Reviewer_zCNZ", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper presents SmilesFormer, a transformer based method that aims at generating novel molecules with a multiple properties objective. This is achieved by training a variational auto-encoder fed with fragments of molecules that are then encoded into an embedding space. Afterwards, they are decoded to reconstruct the molecule by generating a valid canonical smile sequence. In order to obtain more desirable properties for the molecule, its representation is optimized in the embedding space by maximizing a score that represents the required properties. The authors claim that the model performs well against state-of-the-art methods.\n", "summary_of_the_review": "It is clear the authors understand the problem at hand, but the paper lacks a clear description about the experiments that were performed to validate the claims. The explanation of the proposed model still needs work to be understandable and not let the readers make assumptions about how the molecule generation process works. The evaluation of the model is not clear, and it is not enough to sustain all the claims made by the author.  ", "strength_and_weaknesses": "Strengths\n- the process of data-augmentation by using different strategies of smile sequence fragmentation or modification but still aiming at reconstructing valid canonical smile sequences is very interesting\n- the introduction and related works sections are well written, it explained well the overall functional parts of the model and also have interesting points about the challenges for molecule design using generative models.\n\nWeaknesses\n- the description of the method should be improved, I had to re-read it multiples times before I was able to understand it. I believe that with a clear division of the description about how the diverse parts of the model are trained and another section with description about how the molecule generation process works could improve the readability of this paper.\n- the paper lacks proper descriptions of the datasets used for the experiments\n- This work cannot be reproduced. It lacks descriptions about how the experiments were run. The \"model and training\" section from the appendix should be expanded and moved to the main text.\n- there are references for the evaluation, but the main text should at least briefly explain the evaluation measures. Now, it’s not easy to see why these measures show that the method performs well.\n- although the method architecture is interesting, none of its parts are novel, authors should highlight how their modifications to existing parts of the model compare to the original papers. In particular, the multi-objective traversal of the latent space is not properly cited, but it is not novel for protein and drug engineering, this is not clear in the current version of this paper.\n- the donepezil rediscovery is interesting, but the effectiveness of the model can’t be derived from a single case study. I think that process could have been replicated with other drugs in order to give more confidence to the rediscovery evaluation.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "iars9Ma6hg", "reviewer": "Reviewer_TxfB", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper proposes SmilesFormer, a transformer-based model that learns a latent space between its encoder and decoder module. The model is trained with 2 main objectives: a) s molecule reconstruction objectives where the model takes in fragments of a molecule and learn to reconstruct its original structure and b) a KL divergence loss between its learned latent space and a normal distribution prior. Input augmentations, such as non-canonical SMILES and fragments made through the Recap decomposition or fragment-on-bonds approaches, were used to better learn molecular representations through reconstructions. During inference, the model can take in an input query and generate a modified variant through gradient step backpropagated from oracle models that predict the target properties of the generated molecules, with stochasticity introduced through dropout. The authors conducted experiments to show that the SmilesFormer can outperform baseline methods in several instances in the applications of multi-objective de novo design with better oracle efficiency. ", "summary_of_the_review": "The paper studies an important application of de novo molecular design and proposes novel contributions such as the fragmentation-based optimization. The other parts of the SmilesFormer mostly relies on existing approaches. My main concern about this manuscript is the “Donepezil rediscovery” section, as mentioned in the weakness, where much post-processing is involved and many candidates are generated for the compound of interest to be discovered. A comparison with baselines in this section would help the reader to better appreciate the model’s impact.", "strength_and_weaknesses": "Strengths:\nProposed methods showed competitive performances compared to existing baselines\nThe fragment-based input augmentation is a novel approach to enhancing molecular representations in molecular deep learning. \nWeaknesses:\nThe experimental section on “Donepezil rediscovery” relies on many post-processing steps on top of the generation with SmilesFormer and generates as many as 30k candidates for the Donepezil scaffold to be discovered. Without comparison with a suitable baseline in this section, it is hard for the reader to appreciate the impact of this section.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "OeCNw3Q4Nb", "reviewer": "Reviewer_US7Z", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper describes a transformer-based language model for SMILES strings. The model is a VAE trained on entire molecules, molecular fragments, or their composition. It can be used for molecular (multi-)property optimization with a stochastic latent space traversal technique. The experiments show that it can be applied with success on multiple property optimization, as well as in real-world drug design scenarios (in particular, the authors apply the method to rediscover Donepezil).", "summary_of_the_review": "In summary, this seems to be a borderline paper. However, at the moment the weaknesses greatly overcome the strengths, so I'm forced to recommend rejection.\n\nBesides addressing the points raised above (see Quality/Novelty), I'd like the authors to answer/comment on the following questions/comments in order to let me be able to eventually raise my score:\n\n- What are the valid/unique fractions of the molecules generated in tables 1 and 2?\n- Can you show the distribution of molecular features (such as the number of rings, bonds, atoms, etc.) of the generated molecules in comparison to the molecules in the test set?\n- Can you provide an explanation as to why the model seems to perform not so well in the logP optimization task? (see Table 3)\n", "strength_and_weaknesses": "**Strengths**\n- Works well in multi-property optimization scenarios\n- Very efficient in terms of oracle calls\n- Re-discovering of Donepezil looks impressive.\n\n**Weaknesses**\n- not evaluated on standard benchmarks such as GuacaMol, MOSES\n- limited novelty\n- literature not fully discussed", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "Not applicable"}, "extra_text": {}}]

Author rebuttal:

Decision: Reject