GenMol: A Drug Discovery Generalist with Discrete Diffusion
Authors: Seul Lee
Karsten Kreis
Srimukh Prasad Veccham
Meng Liu
Danny Reidenbach
Yuxing Peng
Saee Gopal Paliwal
Weili Nie
Arash Vahdat
Venue: ICML 2025 poster (2025)
Keywords: drug discovery
molecule generation
discrete diffusion
Primary area: applications->chemistry_physics_and_earth_sciences

Abstract: Drug discovery is a complex process that involves multiple stages and tasks. However, existing molecular generative models can only tackle some of these tasks. We present *Generalist Molecular generative model* (GenMol), a versatile framework that uses only a *single* discrete diffusion model to handle diverse drug discovery scenarios. GenMol generates Sequential Attachment-based Fragment Embedding (SAFE) sequences through non-autoregressive bidirectional parallel decoding, thereby allowing the utilization of a molecular context that does not rely on the specific token ordering while having better sampling efficiency. GenMol uses fragments as basic building blocks for molecules and introduces *fragment remasking*, a strategy that optimizes molecules by regenerating masked fragments, enabling effective exploration of chemical space. We further propose *molecular context guidance* (MCG), a guidance method tailored for masked discrete diffusion of GenMol. GenMol significantly outperforms the previous GPT-based model in *de novo* generation and fragment-constrained generation, and achieves state-of-the-art performance in goal-directed hit generation and lead optimization. These results demonstrate that GenMol can tackle a wide range of drug discovery tasks, providing a unified and versatile approach for molecular design.

GenMol: A Drug Discovery Generalist with Discrete Diffusion
Seul Lee' Karsten Kreis 2 Srimukh Prasad Veccham Meng Liu
Danny Reidenbach Yuxing Peng? Saee 2aliwal
Weili Nie*
Arash Vahdat"
Abstract
GenMol (ours)
De novo
Best baseline
generation
Drug discovery is a complex process that involves multiple stages and tasks. However, existing molecular generative models can only tackle some of these tasks. We present Gen- eralist Molecular generative model (GenMol), versatile framework that uses only single discrete diffusion model to handle diverse drug discovery scenarios. GenMol generates Sequen- tial Attachment-based Fragment Embedding (SAFE) sequences through non-autoregressivee bidirectional parallel decoding, thereby allowing the utilization of a molecular context that does not rely on the specific token ordering while having better sampling efficiency. GenMol uses fragments as basic building blocks for molecules and introduces fragment remasking, strategy that optimizes molecules by regenerating masked fragments, enabling effective exploration of chemical space. We further propose molecular context guidance (MCG), a guidance method tai- lored for masked discrete diffusion of GenMol. GenMol significantly outperforms the previous GPT- based model in de novo generation and fragment-conssrainedd generation, and achieves -fate-of-the-art performance in goal-directed hit generation and lead optimization. These results demonstrate that GenMol can tackle wide range of drug discovery tasks, providing unified and versatile approach for molecular design.
54 790
Regment-ccnsttaiin
prey Delimizaioon
generation
.38
16.3%
53.3%
100
28.1%
86.7%
76
18 19.362
po
Hit generation
Figure 1: Results drug discovery tasks. The values qual- ity, average quality, sum AUC top- success rate for de novo generation, fragment-comstrained generation, hit generation, and lead optimization, respectively. The "best baseline" refers to mul- tiple best-performing task-specific models among prior works.
For example, Tengment-constraing molecule generation is popular strategy for designing new drug candidates un- der the constraint of preserving a certain molecular sub- structure already known to exhibit a particular bioactiv- ity (Murray & Rees, 2009). Furthermore, real-world drug discovery pipelines are not single stage but consist of sev- eral key stages, such hit generation and lead optimiza- tion (Hughes et al., 201 1). A drug discovery process that leads to the finding of drug candidates that can enter clini- cal trials should consider all of these different scenarios.
Generative models have emerged as a promising method- ology to accelerate labor-intensive drug discovery pipelines (Olivecrona al., 2017: Jin et al., 2018; Yang et al., 2021; Lee et al., 2023), but previous molecular generative models have common limitation: they focus only one two of the drug discovery scenarios. They either cannot be applied to multiple tasks or require expensive modifications including retraining of a specific architecture for each task (Yang et al., 2020; Guo et al., 2023). Recently, SAFE-GPT (Noutahi al., 2024) has been proposed address this problem by formulating sev- eral molecular tasks fragment-constran task, solved by sequence completion. SAFE-GPT uses Se- quential Attachment-basee Fragment Embedding (SAFE) of molecules, which represents a molecule as an unordered sequence of Simplified Molecular Input Line Entry System (SMILES) (Weininger, 1988) fragment blocks. However,
1. Introduction
Discovering molecules with the desired chemical profile is the core objective of drug discovery (Hughes et al., 201 I). To achieve the ultimate goal of overcoming disease, va- riety of drug discovery approaches have been established.
KAIST. Work during an internship at NVIDIA. NVVIDIA. "Equal advising. Correspondence Scul Lee lee @ krr>.
generation
Proceedings of the 1nnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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GenMol: A Drug Discovery Generalist with Discrete Diffusion
(c1) De novo generatio (c2) Linker generation scaffold morphing
(c.3) Motif extension
GenMol
[=
0X0O
NELBO loss
Transformer blocks
Masked token
RXCOEN
Unmasked token
=
ES60 ESCOUNSESHO
Classffiation
head
(a) GenMol architeturee
(b) Generation with discrete diffusion
(c4) Scaffold decoration
(c5) Superstricture generation
(c6) Fragment remasking (hit /ead/
Figure 2:
(a) GenMol architecture. GenMol adopts the BERT architecture and is trained with the NELBO loss of masked discrete diffusion. (b) Generation process of GenMol. Under masked discrete diffusion, GenMol completes molecule by simulating backward in time and predicting masked tokens at each time step f until all tokens are unmasked. (c) Illustration of various drug discovery tasks that be performed by GenMol. GenMol is endowed with the ability easily perform (cl) de novo generation, (c2-c5) fragment- constrained generation, and (c6) fragment remasking that can be applied to goal-directed generation and lead optimization.
since all sequence-based molecular representations in- cluding SMILES and SAFE assume an ordering of tokens based on heuristic rules such as depth-first search (DFS), autoregressive models that operate in left-to-right order like GPT are unnatural for processing and generating molecular sequences. In addition, the autoregressive decoding scheme limits the computational efficiency of the model and makes it challenging to introduce guidance dur- ing generation. Moreover, SAFE-GPT relies on finetuning under expensive reinforcement learning (RI.) objectives to be applied to goal-directed molecule generation.
sion also makes it possible to apply guidance during gener- ation based on the entire sequence. To this end, we propose molecular context guidance (MCG), guidance method to improve the performance of GenMol by calibrating its pre- dictions with information in given molecular context.
We experimentally validate GenMol on wide range of molecule generation tasks that simulate real-world drug discovery problems, including de novo generation, fragment-constrainee generation, goal-directed hit genera- tion, and goal-directed lead optimizationn Across extensive experiments, GenMol outperforms existing methods by large margin (Figure 1). Note that the best baseline results shown in Figure are not the results of a single model, but of multiple task-specific models. These results demonstrate GenMol's potential as a versatile tool that can be used throughout the drug discovery pipeline.
To tackle these limitations, propose Generalist Molec- ular generative model (GenMol), versatile molecular generation framework that is endowed with the ability to handle diverse scenarios that can be encountered in the multifaceted drug discovery pipeline (Figure 2(c)). Gen- Mol adopts masked discrete diffusion framework (Austin al., 2021; Sahoo al., 2024; Shi al., 2024) with the BERT architecture (Devlin et al., 2019) to generate SAFE molecular sequences, thereby enjoying several advantages: (i) Discrete diffusion allows GenMol to exploit a molec- ular context that does not rely on the specific ordering of tokens and fragments by bidirectional attention. (ii) The non-autoregressive parallel decoding improves GenMol's computational efficiency (Figure 2(b)). (ii) Discrete diffu- sion enables GenMol explore chemical space with sim- ple yet effective remasking strategy. We propose fragment remasking (Figure 2(c6) and Figure 3), strategy to opti- mize molecules by replacing certain fragments in a given molecule with masked tokens, from which diffusion gen- erates new fragments. Utilizing fragments as the explo- rative unit instead of individual tokens is more line with chemists' intuition during optimization in drug discovery, and GenMol can effectively and efficiently explore the vast chemical space to find chemical optima. (iv) Discrete diffu-
We summarize our contributions as follows:
We introduce GenMol, a framework for unified and ver- satile molecule generation by building masked discrete diffusion that generates SAFE molecular sequences.
We propose fragment remasking. an effective strat- egy for exploring chemical space using molecular frag- ments as the unit of exploration.
We propose MCG, guidance scheme for GenMol to effectively utilize molecular context information.
We validate the efficacy and versatility of GenMol on a wide range of drug discovery tasks.
2. Related Work
Discrete diffusion. There has been steady progress in applying discrete diffusion for discrete data generation, especially in NLP tasks (Hoogeboom et al., 2021; Austin et al., 2021; He et al., 2023; Zheng et al., 2024; Lou al.,
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GenMol: A Drug Discovery Generalist with Discrete Diffusion
2024; Sahoo et al., 2024). This is mainly due their non-autoregressive generation property, which leads to potential for better modeling long-rang bidirectional de- pendencies and acclerating sampling speed, and their flex- ible design choices in training, sampling, and controllable generation (Sahoo et al., 2024). Notably, D3PM (Austin et al., 2021) introduced a general framework with Markov forward process represented by transition matrices, and a transition matrix with absorbing state corresponds to the masked language modeling (MLM) such as BERT (De- vlin et al., 2019). Campbell et al. (2022) proposed continuous-time framework for discrete diffusion models based on the continuous-time Markov chain (CTMC) theory. SEDD (Lou et al., 2024) introduced a denoising score entropy loss that extends score matching to discrete diffusion models. Sahoo et al. (2024) and Shi al. (2024) proposed simple masked discrete diffusion frameworks, with the training objective being a weighted average of MLM losses across different diffusion time steps.
local mutation of small probability, they suffer from limited exploration in the chemical space. More recently, f-RAG (Lee et al., 2024a) introduced fragment-level retrieval framework that augments the pre-trained molec- ular language model SAFE-GPT (Noutahi et al., 2024), where retrieving fragments from dynamically updated fragment vocabulary largely improves the exploration- exploitation trade-off. However, f-RAG still needs train information fusion module before adapting to various goal goal-oriented generation tasks.
3. Background
3.1. Masked Diffusion
Masked diffusion models (Sahoo et al., 2024; Shi et al., 2024) are a simple and effective class of discrete diffu- sion models, and we follow MDLM (Sahoo al., 2024) to define masked diffusion. Formally, we define x as sequence of L tokens, each of which, denoted as x', is one-hot vector with K categories (i.e., z E {0, 1)x and Σ Zi=1 x 1). Without loss of generality, we assume the K-th category represents the masking token, whose one- hot vector is denoted by m (i.e., mx- 1). We also define (; as a categorical distribution with a probability π E A K where A K represents the simplex over K categories.
Recently, a few works have applied discrete diffusion for molecular generation. For instance, DiGress (Vignae et al., 2023) followed the D3PM framework generate molecu- lar graphs with categorical node and edge attributes. Other works (Zhang et al., 2023; Lin et al., 2024; Hua et 2024) focused on the 3D molecular structure generation, where they used discrete diffusion for atom type generation and continuous diffusion for atom position generation. How- ever, none of them applied discrete diffusion for molecular sequence generation that can serve as a generalist founda- tion model for solving various downstream tasks.
The forward masking process independently interpolates the probability mass between each token in clean data se- quence x' and the masking token m, defined as
2(2 [x') = Cat(z': oua' + (1 - Or.) 02))m).
(I)
Fragment-based drug discovery. Fragment- based molecular generative models refer class of methods that reassemble existing molecular substructures (i.e., fragments) to generate new molecules. They have been consider as an effective drug discovery approach as (i) as- sembling fragments simplifies the generation process and improves chemical validity and (ii) the unit that determines biochemical effect of a molecule is a fragment rather than an individual atom (Li, 2020). A line of works (Jin et al., 2020; Maziarz. et al., 2021; Kong et al., 2022; Geng et al., 2023) used graph-based VABs to generate novel molecules conditioned on discovered substructures. Xie et al. (2020) proposed to progressively add or delete fragments of molecular graphs using Markov chain Monte Carlo (MCMC) sampling. Yang et al. (2021) and Powers et al. (2023) used a reinforremennt learning (RL) framework and classification, respectively, to progressively add frag- ments to the incomplete molecule. Graph-based genetic algorithms (GAs) (Jensen, 2019; 'Tripp & Hernández- Lobato, 2023) is a strong approach that decomposes parent molecules into fragments that are combined to generate an offspring molecule. However, since their generation from random combinations of existing fragments with
where 2 denotes the I-th token in the noisy data sequence at the time step t E [0. 1), and a E [] denotes the masking ratio that is monotonically decreasing function of
I, with 0g- = 1 to on 0. Accordingly, at time step L = 1, 2e becomes sequence of all masked tokens.
The reverse unmasking process inverts the masking process and independently infers each token of the less masked data 2, from more masked data 2, with 5 < f,, which is given by
(o(2) = [Cat(s];2)) 21)
of , m
(2)
Cat(z);; (1-a)) (o. oa 2 m;
T-m
where (z 1) is denoising network that takes the noisy data sequence 2, as input and predicts L probability vectors for the clean data sequence. This parameterization designs the reverse process such that it does not change unmasked tokens. To train the denoising network 2o to (Z+, l), the train- ing objective, which implicitly approximates the negative ELBO (Sohl-Dickstein et al., 2015), is given by
LNILBO -E, L'
al
Σ log(ab(Er, (), a'))d,, (3)
1- at
3

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Fragment remasking. Therefore, utilizing discrete dif- fusion of GenMol, we propose fragment remasking, an GenMol: A Drug Discovery Generalist with Discrete Diffusion
which is the weighted average of MLM losses (i.e., cross- entropy losses) over time steps.
GenMol's efficieney. Furthermore, the discrete diffusion framework enables GenMol to explore the neighborhood of a given molecule with remasking strategy.
3.2. SAFE Molecular Representation
At each masked index I, GenMol samples z based on the reverse unmasking process p' == po(2 (zx) specified by:
Simplified Molecular Input Line Entry System (SMILES) (Weininger, 1988) is the most widely used molecular string representation, but it relies on heuristic depth-first search (DFS) that traverses the atoms of a molecule. Therefore, atoms that are close in molecular structure can be tokens that are very far apart in molecular sequence, and thus it is not straightforward to perform fragment--oonstrained molecular generation with SMILES.
exp (x,, for 1ori=1,". 1, (4)
Pe.s Σ/= exp loga's (2 (x,, t)/TT
where log (z., 1) is the logit predicted by the model and T is the softmax temperature. All masked tokens are pre- dicted in a parallel manner and GenMol confirms the top-N confident predictions with additional randomness follow- ing Chang al. (2022), where N is the number of tokens to unmask each time step. Trade-offs between molecu- lar quality and diversity often arise in drug discovery, and GenMol can balance them through the softmax temperature and the randomness r. Further details about confidence- based sampling is provided in Section B.
Sequential Attachment-basel Fragment Embedding (SAFE) (Noutahi et al., 2024) has been proposed to alleviate this problem. SAFE represents molecules as an unordered sequence of fragment blocks, thereby casting molecular design into sequence completion. SAFE is a non-canonical SMILES in which the arrangement of SMILES tokens corresponding to the same molecular frag- ment is consecutive. Molecules are decomposed into frag- ments by the BRICS algorithm (Degen et al., 2008) and the fragments are concatenated using a dot token (".") while preserving their attachment points. SAFE is permutation- invariant on fragments, i.e., the order of fragments within a SAFE string does not change the molecular identity.
4.2. Exploration in Chemical Space with GenMol
To perform goal-directed molecular optimization tasks, we propose simple yet effective generation method (Figure 3) that consists of three steps: (1) fragment scoring, (2) frag- ment attaching, and (3) fragment remasking.
4. Method
Fragment scoring. We start with constructing a frag-
We introduce GenMol, a universal molecule generation framework that can solve various drug discovery tasks. We first introduce the construction of the discrete diffu- sion framework on the SAFE representation in Section 4.1. Next, we describe the goal-oriented exploration strategy of GenMol with fragment remasking in Section 4.2. Finally, describe MCG, guidance scheme of GenMol by par- tially masking the given molecular context, in Section 4.3.
ment vocabulary. A set of D molecules 'D { 9(22)) where y( is the target property of molecule xz, is decomposed into set of F fragments F (f using predefined decomposition rule. We define the score of fragment fx following Lee et al. (2024a) as:
v(fx) [s(f
Σ
v(z),
(5)
RES(FN)
4.1. Masked Diffusion for Molecule Generation
where S(fx) = (x : f& is subgraph of x) and the top-V fragments based on Eq. (5) are selected as the vocabulary.
We adopt masked discrete diffusion to generate SAFE se- quences and establish flexible and efficient molecule gen- eration framework. Concretely, GenMol uses the BERT ar- chitecture (Devlin et al., 2019) as the denoising network a and the training scheme of MDLM. Utilizing discrete diffu- sion instead of an autoregressive model more in line with the SAFE representation and has several advantages. First, due to the bidirectional attention in BERT, GenMol can leverage parallel decoding where all tokens are decoded si- multaneously under discrete diffusion (Figure 5). As SAFE is fragment onder-insensitive, this allows GenMol to pre- dict masked tokens without relying on a specific ordering of generation while considering the entire molecule. The non annrerrrrrgrsssvve parallel decoding scheme also improves
Fragment attaching. During generation, a molecule Ginit is first generated by randomly selecting two fragments from the vocabulary and attaching them. Fragments or functional groups influence the chemical properties of a molecule and therefore, fragments that commonly occur in molecules with desirable properties likely to carry them to new molecules. However, with fragment attaching alone, the model cannot generate new fragments that are not included in the initial vocabulary, resulting in subopti- mal exploration in chemical space.
4

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GenMol: A Drug Discovery Generalist with Discrete Diffusion
Lead optimiaatiiinnnn Hit generaation
Iterativve optimizaiion
Fragment
GenMol
Decompose
Sample attach
Reg on pnvceeeedd the initid yolabullry
Malecule dataset
Scoring cracle
Fragment vorabulary
Oracle
Frogmeent updioe
Decompose
Scoring
oracle
Seed molecule
(a) doal-directdd molocular optimization with GenMol
(b) Optimization with fragment remasking
Figure 3: (a) Goal-directed hit generation and lead optimization process with GenMol. An initial fragment vocabulary is con- structed by decomposing an existing molecular dataset (hit generation) or seed molecule (lead optimization). Two fragments are randomly sampled from the vocabulary and attached, and GenMol performs fragment remasking. The fragment vocabulary is updated with the generated molecules for the next iteration. (b) Illustration of the molecular optimization trajectory with fragment remask- ing. With fragment remasking. GenMol can explore beyond the initial fragment vocabulary to find chemical optima.
effective strategy to explore the neighborhood of a given molecule in chemical space to find optimal molecules. Dis- crete diffusion allows GenMol mask and re-predict some
Algorithm Goal-directed Molecular Optimization of GenMol
Input: A set of molecules D, vocabulary size V, decomposition rule for fragment vocabulary Rvocab, decomposition rule for fragment remasking Raemask- number of generations G
tokens of given molecule, making neighborhood explo- ration simple and straightforward. However, although there exist some works that apply token-wise remasking to pro- tein sequence optimization (Hayes et al., 2024; Gruver et al., 2024), it would be suboptimal to naively adopt the same strategy for small molccule optimization. This is be- cause each token in SAFE (or SMILES) sequence rep- resents a single atom or bond, and masking them individ- ually results in localized and ineffective exploration. The units that carry information about molecular properties are fragments or functional groups, not individual atoms or bonds (Li, 2020), and thus fragment remasking modifies a molecule by randomly re-predicting a fragment.
Set F <- fragments obtained by decomposing 'D with Hwocab
Set V +- top-V fragments of F (Eq. 5)
Set Plen +- fragment length distribution of 'D based on Premask
Set M < 0)
while [M] < G do
Select and attach two fragments from V to get Zinit
Sample the fragment length m ~ Pica
Select one of the fragments of Tar based on Ryemssk and
replace it with m mask tokens to get Tmask
Generate Inew by iteratively unmasking Zmmn with GenMol Update M < M U [Irew
Decompose nnew into fragments [f..f..... with Rweeb Update V < top-V fragments from VU [fi,J2.....
end while
Given Fint mmask is constructed by randomly selecting one of the fragments of Tini and replacing it with mask chunk. Here, finer decomposition rule than the one that constructed the vocabulary is used, allowing fragment re- masking to operate at a fine-grained level. GenMol then generates Deew by iteratively unmasking maask The pro- posed fragment remasking can also be viewed as a muta- tion operation in GA where the mutation is performed at the fragment-level rather than at the atom- or bond-level. Inew is decomposed and scored by Eq. (5), and the fragment vocabulary is dynamically updated by selecting the top- V fragments, allowing exploration beyond the initial frag- ments. We summarize the goal-directed generation process of GenMol with fragment remasking in Algorithm
Output: Generated molecules M
Fragment remasking can also be interpreted as Gibbs sam- pling (Geman & Geman, 1984). Assuming SAFE molec- ular sequence x is comprised of F fragments, we can repre- sent æ as set of the fragments {fx } where fx denotes an attachment point-assigned SAFE fragment. To sample a molecule x from p(x) = p(f1..... fF), fragment re- masking repeats the process of uniformly selecting the in- dex k and then sampling fx from pf (k), where f denotes fi,..., f but with fi omitted. This is equiva- lent to performing Gibbs sampling with the Markov kernel ff allowing GenMol to perform a random walk in the neighborhood of the given molecule x.
Instead of using a fixed length, the lengths of the fragment mask chunks are sampled from a predefined distribution, e.g., the distribution of fragment lengths in the training set. The length of the fragment depends on the decomposition rule used, and this strategy allows GenMol to automatically adjust the length based on the rule. This also ensures that GenMol generates fragments of varying lengths, offering users better controllability over molecule generation.
4.3. Molecular Context Guidance
Inspired by autoguidance (Karras et al., 2024), we pro- pose molecular context guidance (MCG), a guidance method tailored for masked discrete diffusion of GenMol.
Karras et al. (2024) generalized classifier-free guidance (CFG) (Ho & Salimans, 2021) and proposed to extrapolate
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GenMol: A Drug Discovery Generalist with Discrete Diffusion
Table 1: De novo molecule generation results. The results are the means and the standard deviations of 3 runs. N, T, and r is the number of tokens to unmask at cach time step, the softmax temperature, and the randomness, respectively. The best results are highlighted in bold.
GenMol
105,1.00
80
(0-100..0.511
Method
Validity (%) Uniqueness (%)
Quality (9)
Diversity
Sampling time (8)
Quality (99)
70
(0.5,30.0)
SAFE-GPT
94.0 0.4
100.0 + 0.0
54.7 + 0.3 0.879 + 0.001
27.7 + 0.1
(1.0.0.00)
60
GenMol w/o conf. sampling
96.7 + 0.3
99.3 + 0.2
53.8 + 1.7 0.896 + 0.001
25.4 = 1.6
GenMol (7 0.5,r 0.3)
SAFE-GPT
N =
100.0 0.0
99.7 + 0.1
84.6 + 0.8 0.818 + 0.001
21.1 + 0.4
N 2
97.6 = 0.7
99.5 + 0.2
76.2 + 1.3 0.843 + 0.002
12.2 = 0.6
3
95.6 0.5
99.0 + 0.1
67.1 +:
0.7
0.861
+ 0.001
10.1 + 0.2
(1.5.10.0)
0.82
0.34
0.86
0.88
0.90
GenMol (N = =1)
Diversity
r 0.5, 0.5
100.0 0.0
99.7
0.1
84.6 + 0.8
0.818 + 0.001
21.1 = 0.4
0.5,r 1.0
99.7 0.1
100.0
1 0.0
83.8 0.5
0.832
0.001
20.5 0.6
Figure 4: The quality
7 == 1.0,r = 1.0
99.8 + 0.1
100.0
+ 0.1
79.1
+
0.9
0.845 + 0.002
21.9 + 0.6
trade-off de novo generation with different values of (T,r).
r 1.0,- =10.0
99.8 1 0.1
99.6
1 0.1
63.0
0.882 1 0.003
21.5 1 0.5
1.5.r= =10.0
95.6 0.3
98.3
+ 0.2
39.7
+ 0.5
0.911 + 0.004
20.9 + 0.5
between the predictions of two denoising networks:
5. Experiments
GenMol is trained on the SAFE dataset (Noutahi et al., 2024), which combines molecules from ZINC (Irwin al.,
( 2)))) = w Dz. + (1 - ()
(6)
2012) and UniChem (Chambers et al., 2013). We empha- size that a single GenMol checkpoint is used to perform the following tasks without any additional finetuning spe- cific to each task. We first conduct experiments on de novo molecule generation in Section 5.1. Next, we conduct experiments on fragment-constrained molecule generation tasks in Section 5.2. We then examine GenMol's '' ability to perform goal- goal-directed hit generation and goal- coal-directed lead optimization in Section 5.3 and Section 5.4, respectively. We perform ablation studies in Section 5.5.
where D1 and Do are high-quality model and a poor model, respectively, y is the condition to perform the guid- ance on, and w > 1 is the guidance scale. Typically the same network Dg is used for D1 and Do with additional degradations applied to Do. such as corrupted input, e.g.. CFG sets D (2, Zz. y) and Do = Dz(ze|ze, 0). The idea of autoguidance is that the weaker version of the same model amplifies the errors, and thus emphasizing the output of Dj over Do by setting w > 1 eliminates these errors.
However, the above guidance method has only been applied to continuous diffusion and its application to discrete diffu- sion has remained unexplored. On the other hand, Nisonoff al. (2024) proposed a CFG scheme for continuous-time Markov chains (CTMCs) (Campbell et al., 2022), sub- class of discrete diffusion models based on a continuous- time formulation. As GenMol is trained under the MDLM framework which can be interpreted as a CTMC (Sahoo et al., 2024), we introduce autoguidance for the MDLM formulation. Specifically,, MCG sets D1 as the original pre- diction of the denoiser Ze and Do as the prediction with partially corrupted input, replacing the logits in Eq. (4) by
5.1. De Novo Generation
Setup. In de novo generation, the goal is to generate valid, unique, and diverse molecules. We generate 1,000 molecules and evaluate them with the following metrics, following Noutahi et al. (2024). Validity is the fraction of generated molecules that are chemically valid. Uniqueness is the fraction of valid molecules that are unique. Diversity is defined as the average pairwise Tanimoto distance be- tween the Morgan fingerprints of the generated molecules. We further introduce quality, the fraction of valid, unique, drug-like, and synthesizable molecules, to provide a sin- gle metric that evaluates the ability to generate chemically reasonable and unique molccules. Here, drug-like and syn- thesizable molecules are defined as those satisfying quan- titative estimate of drug-likeness (QED) (Bickerton al., 2012) 2 0.6 and synthetic accessibility (SA) (Ertl & Schuf- fenhauer, 2009) < 4, respectively, following Jin et al. (2020). Further details are provided in Section D.3.
(w)) log #0.1 (=1,))=== log
(E,,)), (7)
where ž is constructed by masking y 100% of the tokens in z., We provide the derivation in Section C. Intuitively, Eq. (7) compares two outputs from a single GenMol model, with good and poor input, respectively. Specifically, the good input is given partially masked sequence, which is further masked by 7 100% to yield poor input, and the two resulting logits are compared to calibrate GenMol's pre- dictions. Using MCG, GenMol can fully utilize the given molecular context information in fragment-constenn constrained and goal-directed generation.
Results. The results are shown in Table 1. GenMol w/o conf. sampling is a GenMol that uses the standard diffu- sion sampling (Austin et al., 2021; Sahoo et al., 2024) in- stead of confidence sampling. SAFE-GPT (Noutahi et al., 2024), GenMol w/o conf. sampling, and GenMol all show near-perfect uniqueness, while GenMol significantly out-
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GenMol: A Drug Discovery Generalist with Discrete Diffusion
Table 2: Fragment-constrainee molecule generation results. The results are the means and the standard deviations of runs. The
best results are highlighted in bold.
Method
Task
Validity (%)
Uniqueness (%)
Quality (%)
Diversity
Distance
SAFE-GPT
Linker design
76.6 5.1
82.5 = 1.9
21.7 i 1.1
0.545 i 0.007
0.541 + 0,006
Scaffold morphing
58.9 6.8
70.4 5.7
16.7 2.3
0.514 0.011
0.528 + 0.009
Motif extension
96.1 1.9
66.8 1.2
18.6 2.1
0.562 0.003
0.665 + 0.006
Scaffold decoration
97.7 0.3
74.7 2.5
10.0 1.4
0.575 0.008
0.625 0.009
Superstructure generation
95.7 2.0
83.0 = 5.9
3.7
0.573 1 0.028
0.776 1 0.036
GenMol
Linker design
100.0 0.0
83.7 = 0.5
21.9 + 0.4
0.547 + 0.002
0.563 + 0.003
Scaffold morphing
100.0 + 0.0
83.7 = 0.5
21.9 1..90004
0.547 + 0.002
0.563 = 0.003
Motif extension
82.9 0.1
77.5 = 0.1
30.1 0.4
0.617 + 0.002
0.682 + 0.001
Scaffold decoration
96.6 0.6+000. 0.8
82.7 1.8
31.8 +: 0.5
0.591 + 0.001
0.651 + 0.001
Superstructure generation
97.5 0.9
83.6 1.0
34.8
1.0
0.599 0.009
0.762 0.007
performs the other two in terms of validity, quality, and sampling time. Thanks to the non-autoregressive parallel decoding scheme, GenMol can predict multiple tokens si- multaneously and shows much faster sampling N, the number of tokens to unmask at each time step, increases. Notably, GenMol with N = 3 shows higher quality than SAFE-GPT and GenMol w/o conf. sampling with 2.5x shorter sampling time and comparable diversity. Further- more, GenMol can balance between quality and diversity by adjusting the values of the softmax temperature and the randomness r of the confidence sampling. This balance is also shown in Figure 4, demonstrating that GenMol gen- erates molecules along the Pareto frontier of the quality- diversity trade-off. Further analysis on quality and diversity are provided in Section E.2, Section E.3 and Section E.4.
5.3. Goal-directed Hit Generation
Setup. In goal-directed hit generation, the goal to gen- erate hits, i... molecules with optimized target chemical properties. Following Lee et al. (2024b) and Lee et al. (2024a), we construct an initial fragment vocabulary by de- composing the molecules in the ZINC250k dataset (Irwin et al., 2012). We adopt the practical molecular optimization (PMO) benchmark (Gao et al., 2022) which contains 23 tasks. The maximum number of oracle calls set to 10,000 and performance is measured using the area under the curve (AUC) of the average top-10 property scores versus oracle calls. As our baselines, methods, f-RAG (Lee et al., 2024a), Genetic GFN (Kim et al., 2024), and Mol GA (Tripp & Hernández-Lobato, 2023). We also report the results of the top four methods in Gao et al. (2022). Note that since Gao et al. (2022) reported the results of total of 25 methods, comparing GenMol to the top methods is equivalent comparing it 25 meth- ods. Further details are provided in Section D.5.
adopt the recent state-of-the-e
5.2. Fragment-constrainn Generation
Setup. In fragment-constrainee generation, the goal is to complete a molecule given a set of fragments, a frequently encountered setting in real-world drug discovery. We use the benchmark of Noutahi et al. (2024), which uses in- put fragments extracted from 10 known drugs perform linker design, scaffold morphing, motif' extension, scaf- fold decoration, and superstructure generation. In addi- tion to validity, uniqueness, diversity and quality intro- duced in Section 5.1, distance, the average Tanimoto dis- tance between the original and generated molecules, is also measured. 100 molecules are generated for each drug and averaged. Further details are provided in Section D.4.
The results are shown in Table 3. As shown in the table, GenMol significantly outperforms the previous methods in terms of the sum AUC top-10 value and ex- hibits the best performance in 19 out of 23 tasks by a large margin. These results verify that the proposed optimization strategy of GenMol with fragment remasking is effective in discovering optimized hits. The results of additional base- lines are provided in Table 13, Table 14, and Table 15.
Results.
5.4. Goal-directed Lead Optimization
Setup. Given an initial seed molecule, the goal in goal-directed lead optimization is to generate leads. i.c., molecules that exhibit improved target properties while maintaining the similarity with the given seed. Following Wang et al. (2023), the objective is to optimize the binding affinity to the target protein while satisfying the following constraints: QED > 0.6, SA < 4, and sim 2 8 where 8 E (0.4, 0.6) and sim is the Tanimoto similarity between the Morgan fingerprints of the generated molecules and the seed. Performance is evaluated by the docking score of the most optimized lead. Following Lee et al. (2023), we adopt five target proteins, parp1, fa7, 5ht braf, and jak2. For
Results. The results of fragment-connstraneed generation are shown in Table 2. GenMol significantly outperforms SAFE-GPT on most metries across the tasks, demonstrat- ing its general applicability to a variety of fragment con- straint generation settings. Especially, GenMol generates high-quality molecules while preserving high diversity un- der the fragment constraints, again validating GenMol can strike an improved balance between quality and diversity.
7

---

GenMol: A Drug Discovery Generalist with Discrete Diffusion
Table 3: Goal-directed hit generation results. The results are the means of PMO AUC top- of 3 runs. The results for f-RAG (Lee al., 2024a), Genetic GFN (Kim et al., 2024) and Mol GA (Tripp & Hernández- Lobato, 2023) are taken from the respective papers and the results for other baselines are taken from Gao al. (2022). The best results highlighted in bold.
SELFIES- REINVENT
Oracle
GenMol
f-RAG
Genetic GFN
Mol GA
REINVENT
Gruph GA
GP BO
albuterol similarity
0.937
0.977
0.949
0.896
0.882
0.838
0.826
0.898
amlodipine mpo
0.810
0.749
0.761
0.688
0.635
0.661
0.607
0.583
celecoxib.redisoove
0.826
0.778
0.802
0.567
0.713
0.630
0.573
0.723
deco_hop
0.960
0.936
0.733
0.649
0,666
0,619
0.631
0.629
drd2
0.995
0,992
0.974
0.936
0.945
0.964
0,943
0.923
fexofenadine.mpp
0.894
0.856
0.856
0.825
0.784
0.760
0.741
0.722
gsk3h
0.986
0.969
0.881
0.843
0.865
0.788
0.780
0.851
isomers. 3oomers_c7h8220/
0.942
0.955
0,969
0.878
0.852
0.862
0.849
0,680
2ommerrs.9h1102222222
0.833
0,850
0.897
0.865
0.642
0,719
0.733
0.469
jnk3
0.906
0,904
0.764
0.702
0.783
0.553
0.631
0.564
medianl
0.398
0.340
0.379
0.257
0.356
0.294
0.355
0.301
median2
0.359
0.323
0.294
0,301
0.276
0.273
0.255
0.297
mestranol similarity
0,982
0.671
0.708
0.591
0.618
0,579
0.620
0.627
osimertinib mpo
0.876
0.866
0.860
0.844
0.837
0.831
0.820
0.787
perindopril_mpp
0.718
0.681
0.595
0.547
0.537
0.538
0.517
0.493
ged
0.942
0.942
0.941
0.941
0,940
0,940
0.937
ranolazine_mpe
0.821
0.820
0.819
0.804
0.760
0.728
0.748
0.735
_hop sitagliptin mpo
0.628
0.576
0.615
0.527
0.560
0,517
0.525
0.548
0.584
0.601
0.634
0,582
0.021
0.433
0.194
0.186
thiothixene rediscovery
0.692
0.584
0,519
0.534
0.479
0.495
0.559
troglitazone rediscovery
0.867
0.448
0.511
0.427
0.441
0.390
0.348
0.410
valkartan_smart
0.822
0.627
0.135
0.000
0.178
0.000
0.000
0.000
zaleplon_mpo
0.584
0.486
0,552
0.519
0.358
0.346
0.333
0.221
Sum
18.362
16.928
16.213
14.708
14.196
13.751
13.471
13.156
each target, three molecules from its known active ligands are selected and each is given as a seed molecule, yield- tasks. An initial fragment vocabulary is constructed by decomposing the seed molecule. If the gen- erated molecule is lead, its fragments are added to the vo- adopt Graph
GPT instead of diffusion. A + Fragment remasking (F) re-predicts a fragment mask chunk with discrete diffusion. First, A + Token remasking, A + GPT remasking and + Fragment remasking all outperform A, highlighting the importance of exploration through remasking. A Frag- ment remasking outperforms ing that using fragments as the exploration unit is aligned with chemical intuition and effective in chemical explo- ration. + Fragment remasking is also superior to A + GPT remasking, proving the effectiveness of the masked discrete diffusion that does not rely on specific ordering of tokens with bidirectional attention. We also conduct the ab- lation studies on lead optimization in Table 17. Although the naive baseline A outperforms other previous baselines in hit generation, it cannot generate new fragments outside of the vocabulary and therefore fails frequently in lead op- timization, and applying fragment remasking on top of largely improves lead optimization performance.
ing a total
cabulary. Following Wang et al. (2023), GA (Jensen, 2019) and RetMol (Wang et al., 2023) our baselines. Further details are provided in Section D.6.
Token remasking, prov-
Results. The results of goal-directed lead optimization are shown Table 4. As shown the table, GenMol out- performs the baselines in most tasks. Note that baselines often fail, i.., they cannot generate molecules with higher binding affinity than the seed molecule while satisfying the constraints, especially under the harsher (6 = 0.6) similar- ity constraint. In contrast, GenMol is able to successfully optimize seed molecules in 26 out of 30 tasks, validating its effectiveness in exploring chemical space to optimize given molccules and discover promising lead molecules.
Molecular context guidance. To verify the effect of MCG, we present the results of GenMol with (A + MCG) and without (A i.e., 7 = 0) MCG in Table 5. A MCG shows its superiority over A + F, demonstrat- ing that calibrating GenMol's predictions with molecular context information with MCG improves GenMol's perfor- mance. The full results are shown in Table 16, where A F + MCG achieves the best performance in 19 out of 23 tasks. The same trend also observed Table 12, where GenMol w/ MCG outperforms GenMol w/o MCG across various tasks on fragment-constrrn generation.
5.5. Ablation Study
Fragment remasking. To examine the effect of the pro- posed fragment remasking with masked discrete diffusion, conduct ablation studies with alternative remasking strategies in Table 5. Attaching (A) is baseline that at- taches two fragments from the vocabulary without further modifications. On top of it, A Token remasking ran- domly re- re-predicts individual tokens instead of a fragment chunk with discrete diffusion and + GPT remasking re- predicts randomly chosen fragment chunk with SAFE-
8

---

GenMol: A Drug Discovery Generalist with Discrete Diffusion
Table 4: Lead optimization results (kcal/mol). The results are the mean docking scores of the most optimized leads of runs. Lower is better.
Table 5:
Ablation study on the goal- directed hit generation task. The results the mean sums of PMO AUC top-10 of runs. The best results are highlighted in bold. The full results are shown in Tahle 16.
Target
Seed score
6
0.4
8 0.6
protein
GenMol
RetMol
Graph GA
GenMol
RetMol
Graph GA
-7.3
-10.6
-9.0
-8.3
-10.4
-8.6
parp1
-7.8
-11.0
-10.7
-8.9
-9.7
-8.1
-8.2
-11.3
-10.9
-9.2
Method
Sum 17.641
-6.4
-8.4
-8.0
-7.8
-7.3
-7.6
-7.6
fa7
6.7
-8.4
-8.2
-7.6
-7.6
Attaching (A)
88.5
-4.5
-12,9
-12.1
-11.7
-12.1
-11.3
A + Token remasking
18.091
Shtlb
-7.6
-12.3
-9.0
-12.1
-12.0
-10.0
-12.0
(A404450)
-9.8
-11.6
-10.5
A GPT remasking
18.074
-9.3
-10.8
-9.8
(A40433)
braf
-9.4
10..8
-1..6
-9.7
-9.8
-10.6
-11.6
-10.5
-10.4
A Fragment remasking (F)
18.208
(A+0.567)
-7.7
-10.2
-9.3
-8.1
jak2
-8.0
-10.0
-9.0
-9.2
-9.4
-9.2
F MCG
18.362
-8.6
-9.8
(A-F-00.154)
6. Conclusion
design. Journal of chemical information and modeling. 1963):1096-1108 2019.
We proposed GenMol, molecule generation framework designed to deal with various drug discovery scenarios ef- fectively and efficiently by integrating discrete diffusion with SAFE. Especially, fragment remasking allows Gen- Mol to effectively explore chemical space and MCG further improves GenMol's performance. The experimental results showed that GenMol can achieve state-of-the-ant results in wide range of drug discovery tasks, demonstrating po- tential unified and versatile tool for drug discovery.
Campbell, A., Benton, J., De Bortoli, V., Rainforth, T., Deligiannidis, G., and Doucet, A. A continuous time framework for discrete denoising models. Advances in Neural Information Processing Systems, 35:28266- 28279, 2022.
Chambers, J., Davies, M., Gaulton, A., Hersey, A., Velankar, S., Petryszak, R., Hastings, J., Bellis, L., McGlinchey, S., and Overington, J. P. Unichem: uni- fied chemical structure cross-referencing and identifier tracking system. Journal of cheminformatics, 5(1):3, 2013.
Impact Statement
In our paper, we showed that GenMol is capable of address- ing a broad spectrum of drug discovery challenges, provid- ing unified and versatile solution for molecular design. However, as effective as GenMol is in drug discovery tasks, has the potential generate harmful drugs if used mali- ciously. To prevent this, GenMol could be equipped with features that incorporate target properties that take toxicity into account, exclude toxic fragments from the fragment vocabulary, or filter the proposed drug candidates by pre- dicting the toxicity.
Chang, H., Zhang, H., Jiang, L., Liu, C., and Freeman, W. T. Maskgit: Masked generative image transformer. Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pp. 11315-11325, 2022.
Degen, J., Wegscheid- Gerlachh C., Zaliani, A., and Rarey, M. On the art of compiling and using 'drugllike' chem- ical fragment spaces. ChemMedChem: Chemistry En- abling Drug Discovery, 3(10):1503-1507, 2008
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GenMol: A Drug Discovery Generalist with Discrete Diffusion
(a) GenMol (ours)
(b) A utoregressivve models
Step 0
0300
Step 0
01008
Step 1
0SGOEN
Step 1
080000
Step 2
Gaca
Step 2
0100100
Step 3
eNGOEN
Step 4
eaconN
Step 5
eaGOEn
Step 11
0300
Step 6
Step 12
Step
Step 13
Step
Step
Step 0NC0
Given token
Mask token
Predicted token
Figure 5: Illustration of the fragmnent-constrained motif extension process (a) GenMol and (b) autoregressive models. GenMol starts by sampling the length the sequence and then filling the sequence with mask tokens corresponding to the sampled length. GenMol employs parallel decoding where all tokens are decoded simultancously under discrete diffusion, and confirms only the most confident predictions. The decoding proceeds progressively until all mask tokens predicted. In contrast, autoregressive models like SAFE-GPT (Noutahi al., 2024) need predict token per step, requiring many more decoding steps.
A. Limitations
The proposed GenMol is designed with the goal of being versatile tool for various drug discovery scenarios without any task-specific finetuning. In addition to its versatility, GenMol shows improved molecular generation in terms of both effectiveness and efficiency thanks to its masked discrete diffusion with non-autoregressive bidirectional parallel decoding. However, while the parallel decoding scheme reduces sampling time, unmasking more than one token at each time step (i.e., setting > 1) results in degraded generation quality. Overcoming this trade-off between generation quality and sampling efficiency left as future work.
B. Confidence Sampling of GenMol
Following Chang et al. (2022), GenMol adopts confidence sampling that decides which tokens to unmask at each sampling
step based on the confidence scores of the sampled tokens. After sampling 2 according to Eq. (4), let pb.:- denote the corresponding prediction score, where is the index the sampled category. With introduction of a Gumbel noise decreasing over sampling process, the confidence score ef of the 1-th token at time step defined as follows:
d = looogre. +r.b.e.
Gumbel 1),
(8)
where r the randomness.
Based on their confidence scores of (Eq. (8)), GenMol unmask the top-N currently masked tokens. other words, this equivalent to predicting all tokens simultaneously, but only confirming the most confident predictions. The other tokens exploit the dependencies between
masked again and predicted in the next step. With confidence sampling, GenMol tokens in given molecular sequence for better sampling quality, rather than randomly and independently selecting tokens to unmask, as in the standard diffusion sampling (Austin et al., 2021; Sahoo et al., 2024).
12

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GenMol: A Drug Discovery Generalist with Discrete Diffusion
C. Derivation of MCG (Eq. (7))
Nisonoff et al. (2024) proposed a CFG scheme for CTMC-based discrete diffusion models (Campbell et al., 2022) as follows:
2. Ri (z,, (2,,
where z') is rate matrix that describes the transition probability for z'.
Applying the same generalization of CFG proposed by Karras et al. (2024) (Eq. (6)) to Eq. (9), we have the following guided rate matrix:
R (o))
(z y) (z,, 2s y)" (o))"oo, (z, 2. //)
(10)
where R1 and Ro, are the rate matrices describing a high-quality model and poor model, respectively.
On the other hand, MDLM can be interpreted as a CTMC with the following reverse rate matrix (Sahoo et al., 2024):
I-ax (zo t), 2.)
2. 7 m. 2,
2z)
1-ai
2 2t
(11)
0
otherwise.
If set RI.t and Ro as the rate matrices resulted by the original prediction of the denoiser (2 and the prediction with partially corrupted input zo (2t, f), respectively, substituting Eq. (11) for RI, and Ro. in Eq. (10) yields:
of (2o 1), 2x) (zo( (2) 2x)
2. m, 2,
R2(") (Zt, (,)
1-0:
23 otherwise
Z:
(12)
of (zo(2,, t) t)
2, + m,
2, = m,
otherwise,
where @ denotes the Hadamard product. Here, we utilized the facts that 2, is an one-hot vector and 2; m + 2; 2e is the corrupted (i.e., more masked) 21.
-m as
Therefore, using the guided rate matrix [(w) in Eq. (12) is equivalent to using the following guided prediction x/")
B (Zr, t) = x0(2,, t)a + xe (²),tt)²-
(13)
log D (w) (zz, t) = w log xe (ze, t) + (1 - w) log (ā1,)(
13

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GenMol: A Drug Discovery Generalist with Discrete Diffusion
Table 6: Statisties of the SAFE dataset.
Train
Test
Validation
Number of examples
945,455,307
118,890,444
118,451,032
D. Experimental Details
D.1. Computing Resources
GenMol was trained using 8 NVIDIA A100 GPUs. The training took approximately 5 hours. All the molecular generation experiments were conducted using single NVIDIA A100 GPU and 32 CPU cores.
D.2. Training GenMol
In this section, we describe the details for training GenMol. We used the BERT (Devlin et al., 2019) architec- ture of the HuggingFace Transformers library (Wolf et al., 2019) with the default configuration, except that we set max paa.itoiioonnnoom wtwppppiooeeeeeedlleeoe embeddings to 256. We used the SAFE dataset and SAFE tokenizer (Noutahi et al., 2024) that has a vo- cabulary size of K = 1880. The statistics of the dataset is provided Table 6. We set the batch size to 2048, the learning rate 3e 4, and the number of training steps 50k. We used the log-linear noise schedule of Sahoo et al. (2024) and the A optimizer (Loshchilov & Hutter, 2019) with 81 0.9 and 32 0.999.
D.3. De Novo Generation
In this section, we describe the details for conducting experiments in Section 5.1. We used the RDKit library (Landrum et al., 2016) to obtain Morgan fingerprints and the Therapeutics Data Commons (TDC) library (Huang et al., 2021) to calculate diversity, QED, and SA. The lengths of the mask chunks were sampled from the ZINC250k distribution.
D.4. Fragment-constrrinee Generation
In Section 5.2, used the benchmark proposed by Noutahi et al. (2024). The benchmark contains extracted fragments from 10 known drugs: Cyclothiazide, Maribavir, Spirapril, Barieitinib, Eliglustat, Erlotinib, Futibatinib, Lesinurad, Lio- thyronine, and Lovastatin. Specifically, from each drug, side chains, a starting motif, the main scaffold with attachment points, and core substructure are extracted, and then serve input for linker design & scaffold morphing, motif exten- sion, scaffold decoration, and superstructure generation, respectively. Linker design and scaffold morphing are tasks where the goal is to generate a linker fragment that connects given two side chains. In GenMol, linker design and scaffold morphing correspond to the same task. Motif extension and scaffold decoration are tasks where the goal is to generate a side fragment to complete new molecule when a motif scaffold and attachment points are given. Superstructure gen- eration is task where the goal is to generate molecule when a substructure constraint is given. Following Noutahi et al. (2024), we first generate random attachment points on the substructure to create new scaffolds and conduct the scaffold decoration task.
We used N 1. We performed the grid search with the search space - E (0.5. 1, 1.2, and r E {1, 1.2, 2, and set r to 3 for linker design and scaffold morphing, 1.2 for motif extension, and for scaffold decoration and superstructure generation. We set 7 1.2 for all the tasks. The lengths the mask chunks were sampled from the ZINC250K distribution. For MCG, we set w 2 and performed search with the search space 7 E 00, 0.2, 0.3, 0.4, 0.5). The values of y are provided in Table 7.
D.5. Goal-directed Hit Generation
In this section, we describe the details for conducting experiments in Section 5.3. To construct an initial fragment vocabu- lary, we adopted simple decomposition rule Rvecab that randomly cut one of the non-ring single bonds of given molecule three times and apply it to the ZINC250K dataset. With this decomposition rule, we can ensure that all fragments have one attachment point and are of appropriate size. For the finer decomposition rule Rremask that determines which fragments fragment remasking will operate on, we used the rule that cut all of the non-ring single bonds in a given molecule. We set the size of the fragment vocabulary to V = 100. We applied the warmup scheme that let GenMol generate molecules by concatenating two randomly chosen fragments without fragment remasking for the first 1, 1,000 generations. We used N = 1,
14

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GenMol: A Drug Discovery Generalist with Discrete Diffusion
Table 8: in hit generation.
Oracle
Y
albuterol simiarry
0.2
Table 9: 7 in lead optimization.
amlodipine mpo
0.3
celecoxib_rediscovee
0.0
Target protein
Seed
7
deco_hop
0.2
-7.3
0.2
drd2
0.0
parpl
-7.8
0.2
Table 7: in fragment-constrained generation.
Texofenadine mpo gsk3b
0.0
-8.2
0.2
0.0
-6.4
0.3
3oomers.c788n200
0.5
Task
Coomers_chh1022222pp
0.0
fa7
-6.7
0.4
jnk3
0.5
-8.5
0.0
Linker design
0.0
medianl
0.2
-4.5
0.3
Scaffold morphing
0.0
median2
0.2
Sht
-7.6
0.0
Motif extension
0.3
mestranol_similari
0.0
-9.8
0.4
Scaffold decoration
0.3
Superstructure generation
0,4
osimertinib_me .mp
0.0
-9.3
0.2
perindopril mpo qed
0.4
braf
-9.4
0.1
0.0
-9.8
0.5
ranolazine mpo
0.0
-7.7
0.5
scaffold_hoy sitagliptin
0.0
jak2
-8.0
0.0
0,2
-8.6
0.1
thiothixene rediscovery
0.3
troglitazone rediscovery
0.0
baisarta_smarrt
0.4
zalepion_mpo
0.4
== 1.2, and = 2. For MCG, we set w = and performed search with the search space y E (0,001,002,03330444,003 The values of y are shown Table
D.6. Goal-directed Lead Optimization
In this section, we describe the details for conducting experiments in Section 5.4. For each target, three molecules were randomly selected from known active compounds from DUD-E (Mysinger et al., 2012) (parp1, fa7, braf and jak2) or ChEMBL (Zdrazil et al., 2024) (5htlb) seed molecules. The same decomposition rules Rvocab and Reemask explained in Section D.5 were used and the fragment vocabulary size was (2023), for each target protein and each seed molecule, rum 10 optimization iterations with 100 generation per iteration. We used N 1, 1.2, and 2. For MCG, we set w = 2 and performed search with the search space {0,0.1 00, 0.2, 0.3, 0.4005), The values of y are shown in Table 9.
V ac. Following the setting of Wang et al.
E. Additional Experimental Results
E. Additional Baseline in De Novo Generation
We provide comparison of GenMol with another widely used baseline trained on ZINC250K (Irwin al., 2012), JT- VAE (Jin al., 2018), and a graph discrete diffusion model trained GuacaMol (Brown et al., 2019), DiGress (Vignac et al., 2023), in Table 10. GenMol significantly outperforms JT- VAE and DiGress in terms of quality and sampling time. We used the fast version of the JT- code and the official code of 2
E.2. QED and SA Distributions in De Novo Generation
We provide the QED and SA distributions of molecules generated by GenMol and SAFE-GPT, respectively, in Figure 6. The distributions of 100k molecules randomly sampled from the test set are also shown in the figure. As shown in the figure, GenMol is able to generate molecules of higher QED (more drug-like) and lower SA (more synthesizable) values
'https: ptttps://gii thub. com wwwp///lyyutttttssaatt/. NNNp3 Pttpps: //gi thub. com wttp../.o//w//////eeeeeeeee htt///vignac/DiiGresss...
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GenMol: A Drug Discovery Generalist with Discrete Diffusion
Table 10: De novo molecule generation results. The results are the means and the standard deviations of 3 runs. N, 7, and r is the number of tokens to unmask at cach time step, the softmax temperature, and the randomness, respectively. The best results highlighted in bold.
Method
Validity (%)
Uniqueness (%)
Quality (%)
Diversity
Sampling time (s)
JT-VAE
100.0 + 0.0
65.9 1.3
45.2 1.4
0.855 + 0.001
96.5 + 2.1
DiGress
89.6 + 0.8
100.0 0.0
36.8 + 1.0
0.885 + 0.002
1241.9 + 9.2
GenMol (N 1)
== 0.5, r 0.5
100.0 + 0.0
99.7 = 0.1
84.6 + 0.8
0.818 + 0.001
21.1 + 0.4
7 1.0, 10.0
99.8 1 0.1
99.6 0.1
63.0 0.4
0.882 1 0.003
21.5 0.5
QED
SA
6-
0.5
GenMol (N = 11,
data
5
0.5
GenMol (N 1, 1,,,r
SAFE-GPT
4-
0.4
3-
0.3
0.2
1-
0.1
0-
0.0
Figure 6: QED and SA distributions of molecules in de novo generation.
Table 11: Quality (%) results in de novo molecule generation with various QED and SA thresholds. The results are the means and the standard deviations of 3 runs. The best results are highlighted in bold.
Quality
Method
QED 2 0.6, SA 5
QED 2 0.5, 5
QED 2 0.7, S
SAFE-GPT GenMol (N 1, = 0.5, = 0.5)
54.7 + 0.3
78.3 1.7
18.5 + 0.1
84.6 +
97.1 0.1
50.6 + 1.0
than SAFE-GPT, resulting in high quality in Table 1. Furthermore, GenMol can freely control these distributions by adjusting the values of the softmax temperature 7 and the randomness
E.3. Analysis on Quality and Diversity in De Novo Generation
The quality and diversity values of 100k molecules randomly sampled from the test set are 38.2% and 0.897, respectively. The quality and diversity values of molecules generated by GenMol (N 1, 0.5, r 0.5) are 84.6% and 0.818, respectively, shown in Table 1. We can think of this as sacrificing diversity and selecting specific mode of high quality. GenMol can control this mode-selecting behavior by adjusting 7 and (c.g., the quality and diversity values of GenMol (N = 1, 7 1.5, 10) shown in Table 1 are 39.7% and 0.911, respectively).
E.4. Analysis on QED and SA Thresholds in Quality Metric
In Section 5.1 and Section 5.2, we introduced quality to provide metric that summarizes the model's ability to generate chemically plausible molecules. We followed Jin et al. (2020) and set the QED and SA thresholds to 0.6 and 4, respectively. To validate the robustness of the quality metric to threshold selection, we additionally provide quality results on the de novo generation task with different thresholds in Table 11. QED > 0.5, SA < 5 corresponds to the softer condition, while QED 2 0.7, SA* < 3 is the harsher condition. As shown in the table, different QED and SA thresholds yield a consistent quality trend, with GenMol outperforming SAFE-GPT in all three settings.
E.5. Additional Baseline in Fragment-constraie Generation
We compare the results of GenMol and DiGress in the Tragment-constraiine generation tasks in Table 12. As shown in the table, GenMol significantly outperforms DiGress in validity and quality.
16

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GenMol: A Drug Discovery Generalist with Discrete Diffusion
Table 12: Fragment-constrainge molecule generation results. The results are the means and the standard deviations of 3 runs. The
purple parentheses indicate the performance gain MCG.
Method
Task
Validity (%)
Uniquemess (%)
Quality (%)
Diversity
Distance
DiGress
Linker design
31.2 : 1.2
84.3 0.4
6.1 0.2
0.745 0.001
0.724 0.003
Scaffoll murphing
31.2 : 1.2
84.3 0,4
6.1 0.2
0.745 0.001
0.724 0.003
Motif extension
21.8 + 0.8
94.5 = 0.3
4,2 = 0.1
0.818 +: 0.003
0.794 0.003
Scaffold docoration
0.7
91.0 0.8
9.1 0.4
0.793 1: 0.003
0.785 0.002
Superstructure generation
26.7 1.3
85.9 1.4
7.4 0.9
0.789 0.005
0.776 0.004
GenMol w/o MCG
Linker design
10000
83.7 = 0.5
21.9 0.4
0.547 0.002
0.563 0.003
Scaffold morphing
100.0
83.7 = 0.5
21.9 0.4
0.547 + 0.002
0,563 0.003
Motif extension
7..2+
77.8 0.2
27.5 0.8
0.617 0.002
0.682 0.002
Scaffold decoration
78.0 1.2
29.6 = 0.8
0,576 0.001
0,650 0.001
Superstructure generation
9 1.1
78.3 3.4
33.3
0.574 0.008
0.757 0.003
GenMol
Linker design
100.0 0.0 (+00))
83.7 = 0.5 (+1.0)
21.9 0.4 (+00)
0.547 = 0.002 (+0000)
0,563 + 0.003 (-0,000)
Scaffold morphing
100.0 (00))
83.7 = 0.5 (+0.))
21.9 0.4 (:00)
0.547 0.002 (00,000)
0,563 = 0.003 (+0.000)
Motif extension
82.9 (657)
77.5 (-03)
(+26))
(400000)
0.682
(-0000))
Scaffold decuration
96.6 : 0.8
82.7 (+4.7)
31.8
0.5 (+22)
0.591
0.001 (H0U8)))
0.651 + 0.001 (-0000))
Superstructure generation
97.5 = 0.9 (0.7)
83.6 1.0
34.8 1.0 (+15)
0.599 0.009 (+00125)
0,762 + 0.007 (-0.005)
E.6. Ablation Study on MCG in Fragmeet--oonstrained Generation
We compare the results of GenMol and GenMol without MCG in the Tragment-constrain generation tasks in Table 12. As shown in the table, using MCG improves the performance of GenMol across various tasks and metrics, verifying the effectiveness of the proposed MCG scheme.
E.7. Full Goal-directed Hit Generation Results
We provide the full results of Table 3 including the additional baselines from Gao et al. (2022) in Table 13, Table 14, and Table 15. As shown in the tables, GenMol outperforms all baselines by large margin.
On the other hand, simulate the fragment- drug discovery (FBDD) scenario following Lee al. (2024a), we have assumed that high-quality fragment vocabulary of size 100 is given for each task in Table 3. Here, similar to Wang al. (2023), also provide the results of GenMol (10 fragments), which starts with only 10 of these fragments to simulate scenario where high-quality fragments are sparse, in Table 13.
E.8. Full Goal-directed Hit Generation Results in Ablation Study
We provide the full results of the ablated GenMol variants baselines in the goal-directed hit generatino task in Table 5 in Table 16. As shown in the table, A, A + Token remasking, A GPT remasking and A Fragment remasking show inferior performance to A + F MCG, the full GenMol, discussed in Section 5.5.
E.9. Ablation Study in Goal-directed Lead Optimization
We compare the results of the ablated GenMol baselines in the goal-directed lead optimization task in Table 17. As in Table 5, A Token remasking, A + GPT remasking and A Fragment remasking all outperform A, demonstrating the importance of further modification on top of A through remasking. Moreover, A + Fragment remasking generally outper- forms A + Token remasking and A + GPT remasking, showing the effectiveness of the proposed remasking strategy that uses fragments as the exploration unit under discrete diffusion. Lastly, + F MCG shows further improved performance compared F, validating the effectiveness of MCG in the lead optimization task.
E. Examples of Generated Molecules
We provide examples the molecules generated by GenMol (N = 1, = 0.5, r 0.5) on de novo generation Figure 7. We provide examples of generated molecules on fragment-consstraneed generation Figure 8.
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GenMol: A Drug Discovery Generalist with Discrete Diffusion
Table 13: Goal-directed hit generation results. The results are the means and standard deviations of PMO AUC top- 10 of runs. The results for f-RAG (Lee al., 2024a), Genetic GFN (Kim al., 2024) and Mol GA (Tripp & Hemández-Lobatt 2023) are taken from the respective papers and the results for other baselines taken from Gao al. (2022). The best results highlighted in bold.
GenMol
Oracle
GenMol
f-AG
Genetie GFN
Mol GA
(10 fragments)
albuterol.similee similarity
0.937 0.010
0.847 = 0.036
0.977 0.002
0.949 = 0.010
0.896 = 0.035
amlodipine _mp
0.810 1 0.012
0.762 1 0.012
0.749 + 0.019
0.761 + 0.019
0.688 1 0.039
dalecoxib_rediscoome
0.826 + 0.018
0.619 + 0.005
0.778 + 0.007
0.802 + 0.029
0.567 + 0.083
deco_hop
0.960 + 0.010
0.957 + 0.016
0.936 0.011
0.733 0.109
0.649 + 0.025
drd2
0.995 = 0.000
0.995 = 0.000
0.992 + 0.000
0.974 0.006
0.936 = 0.016
fexofenadine.mpp
0.894 = 0.028
0.806 = 0.008
0.856 - 0.016
0.856 - 0.039
0.825 = 0.019
gsk3b
0.986 + 0.003
0.985 + 0.004
0.969 0.003
0.881 + 0.042
0.843 + 0.039
isomers 2ommers_chh82222
0.942 = 0.004
0.984 + 0.002
0.955 + 0.008
0.969 = 0.003
0.878 = 0.026
isomers co
0.833 + 0.014
0.866 + 0.010
0.850 0.005
0.897 + 0.007
0.865 + 0.012
jnk3
0.906 = 0.023
0.828 = 0.007
0.904 = 0.004
0.764 0.069
0.702 = 0.123
medianl
0.398 = 0.000
0.336 = 0.008
0.340 + 0.007
0.379 = 0.010
0.257 = 0.009
median2
0.359 0.004
0.354 0.000
0.323 0.005
0.294 + 0.007
0.301 = 0.021
mestranol_similnn
0.982 = 0.000
0.991 + 0.002
0.671 + 0.021
0.708 = 0.057
0.591 = 0.053
osimertinib mpo
0.876 + 0.008
0.870 + 0.004
0.866 + 0.009
0.860 0.008
0.844 = 0.015
popoo
0.718 0.012
0.695 0.004
0.681 1 0.017
0.595 0.014
0.547 = 0.022
qed ranolazine mmp scaffold_hov hop sitagliptin mpo thiothixene hhidhhxeeeerediscooeery
0.942 + 0.000
0.943 + 0.000
0.939 + 0.001
0.942 + 0.000
0.941 + 0.001
0.821 + 0.011
0.777 + 0.016
0.820 + 0.016
0.819 + 0.018
0.804 + 0.011
0.628 = 0.008
0.648 0.005
0.576 0.014
0.615 0.100
0.527 = 0.025
0.584 + 0.034
0.588 + 0.064
0.601 + 0.011
0.634
0.582 1 0.040
0.692 + 0.123
0.569 0.013
0.584 + 0.009
0.583
0.519 = 0.041
troglitazone_rediin rediscovery
0.867 + 0.022
0.848 0.040
0.448 0.017
0.511 0.054
0.427 + 0.031
valsartan smarts
0.822 + 0.042
0.803 + 0.011
0.627 + 0.058
0.135 + 0.271
0.000 + 0.000
zaleplon_mp mpo
0.584 - 0.011
0.571 = 0.016
0,486 + 0.004
0.552 = 0.033
0.519 = 0.029
Sum
18.362
17.643
16.928
16.213
14.708
SELFIES REINVENT
Oracle
REINVENT
Graph GA
GP BO
STONED
albuterol similarity
0.882 + 0.006
0.838 + 0.016
0.826 + 0.030
0.898 + 0.014
0.745 + 0.076
amlodipine mpo
0.635 0.035
0.661 0.020
0.607 = 0.014
0.083
0.608 0.046
selecoxib_rediscover
0.713 0.067
0.630 0.097
0.573 = 0.043
0.753
0.382 = 0.041
hopo_hop
0.666 0.044
0.619 = 0.004
0.631 = 0.012
0.629
0.611 + 0.008
drd2
0.945 0.007
0.964 = 0.012
0.943 = 0.005
0.923
0.913 0.020
fexofenadine_mpo
0.784 + 0.006
0.760 = 0.011
0.741 + 0.002
4.005
0.797 + 0.016
gsk3b
0.865 0.043
0.788 + 0.070
0.780 + 0.037
0.851 + 0.041
0.668 0.049
isomers isomers.c7h8n22. c788222
0.852 0.036
0.862 = 0.065
0.849 = 0.034
4.101
0.899 0.011
isomerssc9h1102222p222
0.642 0.054
0.719 0.047
0.733 = 0.029
0.469
0.805 0.031
jnk3
0.783 0.023
0.553 = 0.136
0.631 = 0.064
0.564
0.523 1 0.092
medianl
0.356 0.009
0.294 = 0.021
0.355 = 0.011
0.301 0.014
0.266 0.016
median2
0.276 + 0.008
0.273 + 0.009
0.255 + 0.005
0.297 0.009
0.245 + 0.032
mestranol similarity
0.618 + 0.048
0,579 = 0.022
0.620 = 0.029
0.627=0008
0.609 + 0.101
osimertinib_me _mpo
0.837 0.009
0.831 = 0.005
0.820 = 0.003
0.787
0.822 0.012
perindopril_nn _mpo
0.537 + 0.016
0.538 = 0.009
0.517 = 0.021
0.01
0,488 1 0.011
ged ranolazine_mo _mpo scaffold hop sitagliptin mpo
0.941 0.000
0.940 = 0.000
0.940 = 0.000
0.007
0.941 1 0.000
0.760 0.009
0.728 0.012
0.748 + 0.018
0.735
0.765 0.029
0.560 + 0.019
0.517 + 0.007
0.525 + 0.013
+
0.521 + 0.034
0.021 0.003
0.433 = 0.075
0.194 = 0.121
0.055
0.393 0.083
thiothixene.rediscoovrry
0.534 0.013
0.479 = 0.025
0.495 = 0.040
0.559
0.367 0.027
troglitazone rediscoveryy
0.441 + 0.032
0.390 = 0.016
0.348 0.012
0.320 1 0.018
bassartanssmart
0.179 + 0.358
0.000 = 0.000
0.000 = 0.000
0.000 0.000
0.000 1 0.000
zaleplon mpo
0.358 0.062
0.346 + 0.032
0.333 + 0.026
0.221 0.072
0.325 0.027
Sum
14.196
13.751
13.471
13.156
13.024
18

---

GenMol: A Drug Discovery Generalist with Discrete Diffusion
Table 14: Goal-directed generation results (continued).
Oracle
LSTM HC
SMILES-GA
SynNet
DoG-Gen
DST
albuterol similarity similarryy
0 0.018
0.661 + 0.066
0.584 0.039
0.676 = 0.013
0.619 = 0.020
amlodipine.mpp
0.593 0.016
0.549 = 0.009
0.565 + 0.007
0.536 + 0.003
0
celecoxib celecoxib_redisccver) rediscovery
0.539 + 0.018
0.344 + 0.027
0.441 0.027
0.464 0.009
0.380 1 0.006
deco_hop drd2 fexofenadine mpo
0.826+00011
0.611 = 0.006
0.613 = 0.009
0.800 = 0.007
0.608 - 0.008
+
0.908 + 0.019
0.969 0.004
0.948 + 0.001
0.820 + 0.014
0
0.721 0.015
0.761 0.015
0.695 0.003
0.725 = 0.005
gsk3b
0.629 = 0.044
0.789 0.032
0.831 0.021
0.671 = 0.032
isomers coomersc7h8n2022
0.485 + 0.045
0.913 + 0.021
0.455 0.031
0.465 0.018
0.548 + 0.069
1somerss.9hh1022222222
0.342 0.027
0,860 + 0.065
0.241 0.064
0.199 0.016
0.458 + 0.063
jnk3
0.661 + 0.039
0.316 + 0.022
0.630 + 0.034
0.595 0.023
0.023
0.556 + 0.057
medianl
0.255 + 0.010
0.192 + 0.012
0.218 0.008
0.217 0.001
0.232 + 0.009
median2
0.248 0.008
0.198 0.005
0.235 0.006
0.212 0.000
0.185 = 0.020
mestranol -itirarilliiillrtty
0.526 0.032
0.469 + 0.029
0.399 = 0.021
0.437 0.007
0.450 = 0.027
osimertinib mpo
+ 0.002
0.817 + 0.011
0.796 1066:0000 0.003
0.774 0.002
0.785 + 0.004
perindopril_mpo
0.489 + 0.007
0.447 0.013
0.557 0.011
0.474 0.002
0.462 = 0.008
qed ranolazine mpo scaffold hop
0.939 + 0.000
0.940 + 0.000
0.941 0.000
0.934 + 0.000
0.938 + 0.000
0
0.699 = 0.026
0.741 0.010
0.711 0.006
0.632 = 0.054
0.533 0.012
1494
0.502 0.012
0.515 0.005
0.497 = 0.004
sitagliptin _mp
0.066 + 0.019
0.363 + 0.057
0.025 0.014
0.048 + 0.008
0.075 0.032
booohiixene_redssoveee
0.438 + 0.008
0.315 0.017
0.401 + 0.019
0.375 + 0.004
0.366 = 0.006
troglitazone_redin rediscovery
0.354 + 0.016
0.263 + 0.024
0.283 0.008
0.416 0.019
0.279 + 0.019
valsartan smarts
0.000 + 0.000
0.000 + 0.000
0.000 + 0.000
0.000 + 0.000
0.000 + 0.000
zaleplon.mg mpo
0.206 + 0.006
0.334 + 0.041
0.341 0.011
0.123 = 0.016
0.176 = 0.045
Sum
12.223
12.054
11.498
11.456
10.989
SELFIES-
Oracle
MARS
MIMOSA
MolPal
DoG-AE
LSTM HC
albuterol albuterol_simmlarigy ssiillarity
0.597 + 0. 124
0.618 + 0.017
0.609 + 0.002
0.664 + 0.030
0.533 + 0.034
amlodipine mpo
0.504 0.016
0.543
0.003
0.582 + 0.008
0.532 0.004
0.507 0.005
celecoxib redicsovbred
0.379 0.060
0.393 = 0.010
0.415 = 0.001
0.385 1 0.008
0.355 + 0.012
hop
0.589 + 0.003
0.619 = 0.003
0.643 = 0.005
0.590 + 0.001
0.765 + 0.055
drd2
0.891 0.020
0.799 = 0.017
0.783 = 0.009
0.729 0.034
0.943 0.009
fexofenadine_mpo
0.711 0.006
0.706 0.011
0.685 = 0.000
1.693
0.679 0.017
gsk3b
0.552 0.037
0.554 + 0.042
0,555 + 0.011
0.423 + 0.018
0.601 0.091
ccomers.c7h8n222
0.728 0.027
0.564 + 0.046
0.484 + 0.006
0.587 0.031
0.239 0.077
isomers_c9h110222pp222
0.581 = 0.013
0.303 = 0.046
0.164 = 0.003
0.352
0.049 + 0.015
jnk3
0.489 0.095
0.360 = 0.063
0.339 = 0.009
0.207 0.013
0.469 1 0.138
medianl
0.207 0.011
0.243 = 0.005
0.249 = 0.001
0.239
0.171 0.009
median2
0.181 + 0.011
0.214 + 0.002
0.230 + 0.000
0.205+00 0.005
0.182 + 0.006
mestranol similarity
0.388 0.026
0.438 + 0.015
0.564 + 0.004
0.446 0.009
0.370 0.014
osimertinib mpo
0.777 0.006
0.788 0.014
0.779 = 0.000
0.750 0.012
perindopril.mp
0.462 + 0.006
0.490 = 0.011
0.467 = 0.002
0.448 0.006
0.432 + 0.013
qed
0.930 0.003
0.939 = 0.000
0.940 = 0.000
0.938 0.000
0.926 1 0.003
ranolazine -mpo scaffold_ho sitagliptin mpo
0.740 0.010
0.640 = 0.015
0.457 = 0.005
0.614
0.689 0.015
0.469 + 0.004
0.507 + 0.015
0.494 + 0.000
0..72 + 0.002
0.489 + 0.010
0.016 0.003
0.102 + 0.023
0.043 + 0.001
0.116 + 0.012
0.009 + 0.005
bhiotixeene_ecdiccovea
0.344 0.022
0.347 = 0.018
0.339 = 0.001
0.339 0.009
0.314 0.015
troglitazone Beelicazone_eddisooverry
0.256 + 0.016
0.299 = 0.009
0.268 = 0.000
0.257 1 0.002
0.259 1 0.016
valsartan_sma smarts
0.000 + 0.000
0.000 = 0.000
0.000
= 0.000
0.000 1 0.000
0.000 1 0.000
zaleplon_mpel
0.187 0.046
0.172 0.036
168 + 0.003
0.218 0.020
0.049 0.027
Sum
10.989
10.651
10.268
10.246
9,790
19

---

GenMol: A Drug Discovery Generalist with Discrete Diffusion
Table 15: Goal-directed generation results (continued).
SELFIES-
SMILES-
Oracle
GelowNet
GA+D
Screening
VAE BO
VAE BO
albuterol similarit
0.447 + 0.012
0.495
0.025
0.494 0.012
0.483
0.006
0.489 + 0.007
amlodipine
0.444 + 0.004
0.400 + 0.032
0.516 + 0.005
0.535 + 0.001
0.533 + 0.009
0.327 + 0.004
0.223 + 0.025
0.326 + 0.007
0.351 + 0.005
0.354 = 0.002
deco hop
0.583 + 0.002
0.550 0.005
0.579 0.001
0.590 0.001
0.589 + 0.001
drd2
0 0.070
0.382 = 0.205
0.569 0.039
0.545 = 0.015
0.555 = 0.043
fexofenadine.mpp
0.693 0.006
0.587 = 0.007
0.670 = 0.004
0.666 = 0.004
0.671 = 0.003
gsk3b
0.651 + 0.026
0.342 0.019
0.350 0.034
0.438 0.034
0.386 + 0.006
isomers.c7h8n202
0.366 + 0.043
0.854 + 0.015
0.325 0.028
0.168 + 0.034
0.161 + 0.017
1oommcss.9h11n222p222
0.110 + 0.031
0.657 + 0.020
0.200 + 0.030
0.106 + 0.021
0.084 + 0.009
jnk3
0.440 + 0.022
0.219 + 0.021
0.208 0.022
0.238 + 0.024
0.241 + 0.026
medianl
0.202 = 0.004
0.180 = 0.009
0.201 0.003
0.205 0.005
0.202 = 0.006
median2
0.180 + 0.000
0.121 +: 0.005
0.185 0.001
0.200 + 0.004
0.195 + 0.001
mestranol simiaatylssmmilrity
0.322 + 0.007
0.371 0.016
0.386 0.009
0.409 0.019
0.399 1 0.005
osimertinib_me
0.784 0.001
0.672 + 0.027
0.765 + 0.002
0.764 0.001
0.771 = 0.002
perindopril mpo qed
0.430 + 0.010
0.172 + 0.088
0.429 0.003
0.445 + 0.004
0.442 + 0.004
0.921 + 0.004
0.860 0.014
0.936 0.001
0.938 = 0.000
0.938 = 0.000
ranolazine_nd _mp
0.652 = 0.002 0.002
0.555 0.015
0.452 = 0.025
0.411 0.010
0.457 = 0.012
scaffold_hoy hop
0.463 + 0.002
0.413 + 0.009
0.455 0.004
0.471 1 0.002
0.470 = 0.003
sitagliptin
0.008 + 0.003
0,281 0.022
0.084 + 0.015
0.022 0.003
0.023 = 0.004
thiothixene_rediieee
0.285 + 0.012
0.223 + 0.029
0.297 0.004
1317+0000
0.317 + 0.007
troglitazone rediscovery
18888 + 0.001
0.152 + 0.013
0.243 0.004
0.249 + 0.003
0.257 + 0.003
valsartan smarts
0.000 = 0.000
0.000 0.000
0.002 0.003
0.000
0.000
0.002 = 0.004
zaleplon_mpp
0.035 + 0.030
0.244 0.015
0.206 0.015
0.072 = 0.014
0.039 = 0.012
Sum
9.131
8.964
8.887
8.635
8.587
Oracle
Pasithca
GFlowNet-AL
JT-VAE BO
Graph MCTS
MoIDQN
-obuterol-similarii
0.447 + 0.007
0.390 = 0,008
0.485 1 0.029
0.580 +
0.320 + 0,015
amlodipine_mp mpo
0.504 0.003
0.428 0.002
0.519 + 0.009
0.447 0.008
0.311 0.008
celecoxib. rediscovery
0.312 0.007
0.257 0.003
0.299 + 0.009
0.264 + 0.013
0.099 0.005
hop
0.579 0.001
0.583 = 0.001
0.585 + 0.002
0.554 0.002
0.546 + 0.001
drd2
0.255 0.040
0.468 = 0.046
0.506 + 0.136
0.050
0.025 + 0.001
fexofenadine_mpo
0.660 0.015
0.688 0.002
0.667 + 0.010
0.574
0.478 0.012
gsk3b
0.281 0.038
0.588 + 0.015
0.350 + 0.051
0.281 +.022
0.241 0.008
isomers isomers.c7h8n22 c7h8n262
0.673 0.030
0.241 = 0.055
0.103 0.016
0.530 0.035
0.431 0.035
isomerssc9h1102222p222
0.345 + 0.145
0.064 = 0.012
0.090 + 0.035
0.454 +
0.342 + 0.026
jnk3
0.154 + 0.018
0.362 = 0.021
0.222 + 0.009
1.110+0.000
0.111 + 0.008
medianl
0.178 0.009
0.190 = 0.002
0.179 + 0.003
0.195 +
0.122 0.007
median2
0.179 0.004
0.173 + 0.001
0.180 + 0.003
0.132 0.002
0.088 + 0.003
mestranol similarity
0.361 + 0.016
0.295 + 0.004
0.356 + 0.013
0.281 + 0.008
0.188 + 0.007
osimertinib mpo
0.749 0.007
0.787 = 0.003
0.775 + 0.004
4.700
0.674 0.006
perindopril
0.421 0.008
0.421 = 0.002
0.430 + 0.009
0.277 0.013
0.213 + 0.043
qed ranolazine_ _mpo scaffold hop sitagliptin mpo thiothixene.rediscveeryy
0.931 0.002
0.902 = 0.005
0.934 1: 0.002
1.892
0.731 + 0.018
0.347 0.012
0.632 0.007
0.508 + 0.055
0.239
0.051 0.020
0.456 + 0.003
0.460 + 0.002
0.470 + 0.005
0.412
0.405 + 0.004
0.088 0.013
0.006 = 0.001
0.046 - 0.027
0.056
0.003 = 0.002
0.288 0.006
0.266 = 0.005
0.282 + 0.008
0.231 0.004
0.099 0.007
troglitaoone.rddicovverr
0.240 + 0.002
0.186 = 0.003
0.237 1 0.005
0.224 0.009
0.122 1 0.004
valsartan smarts
0.006 0.012
0.000 = 0.000
0.000 + 0.000
0.000
0.000
0.000 + 0.000
zaleplon mpo
0.091 0.013
0.010 0.001
0.125 + 0.038
0.058 0.019
0.010 0.005
Sum
8.556
8.406
8.358
7.803
5.620
20

---

GenMol: A Drug Discovery Generalist with Discrete Diffusion
Table 16: Ablation study on goal-directed hit generation. The results the and standard deviations of PMO AUC top-10 of 3 runs. best results are highlighted bold.
A GPT remasking
A
Oracle
Attaching (A)
A F+ MCG
Token remasking
Frag. remasking (F)
albuterol_smiin similarity
0.872 = 0.032
0.895 = 0.033
0.908 = 0.039
0.932 = 0.007
0.937 0.010
amlodipine_mp mpp
0.769 0.029
0.802 + 0.016
0.780 = 0.032
0.804 = 0.006
0.810 0.012
celecoxib_rediscovee
0.859 + 0.008
0.821 + 0.010
0.847 + 0.006
0.826 = 0.018
0.826 0.018
deco_hop
0.917 = 0.009
0.945 + 0.006
0.955 = 0.005
0.953 = 0.016
0.960 0.010
drd2
0.995 = 0.000
0.995 + 0.000
0.995 = 0.000
0.995 = 0.000
0.995 0.000
fexofenadine mpo
0.875 = 0.019
0.886 + 0.017
0.905 = 0.012
0.894 = 0.028
0.894 0.028
gsk3b
0.985 + 0.003
0.985 + 0.003
0.986 0.003
0.986 0.003
0.986 0.001
niomens_c7h8n222
0.897 + 0.016
0.934 + 0.003
0.915 + 0.008
0,934 = 0.002
0.942 0.004
isomers @oomerss_9h11022222200
0.816 + 0.025
0.830 0.016
0.820 + 0.018
0.833 0.014
0.833 0.014
jnk3
0,845 = 0.035
0.848 + 0.016
0.840 = 0.021
0.856 = 0.016
0.906 0.023
median
0.397 0.000
0.397 0.000
0.396 0.000
0.397 0.000
0.398 0.000
median2
0.349 = 0.004
0.350 0.006
0.353 = 0.004
0.355 = 0.003
0.359 0.004
mestramol simiilatt
0.970 0.004
0.980 + 0.002
0.980 + 0.002
0.981 0.003
0.982 0.000
osimertinib.nn mpp
0.876 = 0.003
0.876 + 0.008
0.873 = 0.003
0,876 = 0.008
0.876 0.008
perindopril.mpo
0.697 0.014
0.703 = 0.006
0.703 0.000
0.703 0.009
0.718 0.012
qed
0.927 = 0.000
0.942 0.000
0.941 = 0.000
0,942 = 0.000
0.942 0.000
ranolazine mpo scaffold.hov sitagliptin.mg mpo
0.809
0.818 = 0.016
0.824 0.012
0.821
0.821 0.011
0.617 1: 0.002
0.621 + 0.012
0.626 1 0.005
0,628 + 0.008
0.628 0.008
0.573
0.560 T 0.037
0.566 0.010
0.573 0.050
0.584 0.034
thiothixene rediscovery
0,650 1 0.073
0.686 0.121
0.677 1 0.122
0,687 1 0.125
0.692 0.123
trolitazone rediscovery
0.801 0.062
0.853 1 0.035
0.832 0.052
0.867 0.022
0.867 0.022
valsartan smarts
0.739 + 0.043
0.797 + 0.036
0.764 + 0.038
0.797 = 0.033
0.822 0.042
zaleplon_mpo
0.406 0.002
0.569 + 0.014
0.586 0.011
0.569 0.005
0.584 0.011
Sum
17.641
18.091
18.074
18.208
18.362
Table 17: Ablation study on goal-directed lead optimization (kcal/mol) with 0.4. The results are the mean docking scores of the most optimized leads of 3 runs. Lower is better and the best results are highlighted in bold.
Target protein
Seed score
Attaching (A)
A Token remasking
A GPT remasking
+
A+F+ MCG
Frag. remasking (F)
-7.3
-8.2
-8.7
-10.4
-10.6
-10.6
parp1
-7.8
-8.3
-8.2
-11.5
-11.0
-11.0
-8.2
-10.9
-11.1
-10.9
-11.3
-6.4
-7.2
-7.8
-8.1
-8.4
-8.4
fa7
-6.7
-8.3
-8.0
-8.1
-8.4
-8.4
-8.5
-12.8
-12.1
-12.9
-12.9
Shilb
-7.6
-11.9
-11.8
-12.1
-12.3
-12.3
-9.8
-11.3
-11.2
-11.8
-11.8
-9.3
-9.7
-10.7
-10.8
-10.8
-10.8
braf
-9.4
-10.8
-10.1
-10.2
-10.8
-988
-00.5
-10.6
-10.6
-10.6
-7.7
-8.6
-8.7
-10.2
-10.0
-10.2
jak2
-8.0
-8.9
-9.0
-10.1
-9.9
-10.0
-8.6
-9.2
-9.8
-9.5
-9.8
21

---

GenMol: A Drug Discovery Generalist with Discrete Diffusion
Roo
Figure 7: Examples of generated molecules on de novo generation.
Task
Input
Generated molecules
Linker design Scaffold morphing
otoroo dsoto
oXrOo
Motif extension
Scaffold decoration
Superstructure generation
goo
Figure 8: Examples of generated molecules on frastennt generation Eliglustat.
22


Reviews:
[{"review_id": "kCuZS0MFGu", "reviewer": "Reviewer_TVyV", "summary": "The paper introduces a versatile molecular generative model based on discrete diffusion applied to the Sequential Attachment-based Fragment Embedding (SAFE) representations. The method can address various drug discovery tasks uniformly (de novo generation, fragment-constrained generation, hit generation, and lead optimization) using a single backbone model. Following are the key contributions of the paper.\n\n1. Present a discrete diffusion model that works for sequence generation for diverse drug discovery scenarios. \n2. Introduce fragment remasking.\n3. Introduces a new guidance method called Molecular Context Guidance (MCG) for fragment-constrained generation.\n4. An ablation study that highlights the quantitative impact of each of the novel contributions stated above.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "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": "We sincerely thank you for your comments. We appreciate your positive comments that our paper introduces a versatile molecular generative model that can address various drug discovery tasks using a single backbone model, that all the claims are supported well by the experiments, and that the experiments are carefully designed. We address your concerns and questions below.\n\n---\n> The paper can be improved by comparing the proposed guidance method to existing methods in the literature like Nisonoff et al. (2024). In MCG (Eq. (7)), how is the goal value $y$ involved?\n\nWe appreciate the suggestion. MCG and Discrete Guidance (Nisonoff et al., 2024) are not directly comparable: MCG does not rely on the target property conditions for guidance while Discrete Guidance does. To use Discrete Guidance, one has to train a conditional generative model that is conditioned on the target properties, which cannot be easily transferred to other tasks. Instead, **MCG is specifically designed for the discrete diffusion of GenMol that is property-unconditional and general-purposed**. For example, MCG can be used for fragment-constrained generation as well as goal-directed generation, while Discrete Guidance cannot. Therefore, the two methods have different objectives and can be used orthogonally.\n\nThe score $y$ (Eq. (5)) is used for fragment vocabulary selection which allows GenMol to effectively perform goal-directed generation without any target property-based diffusion guidance, and is not related to MCG.\n\n---\n> $x^l_\\theta(z_t, t)$ is already a probability distribution over the token vocabulary, and you could introduce the temperature in the softmax used in itself. Is there a specific reason for applying the softmax(log(x)) transformation again?\n\nTo avoid introducing additional notation, we used $\\log(x^l_\\theta(z_t, t))$ to denote the logits generated by the model, and in the actual implementation, we applied the temperature $\\tau$ to the generated logits during inference (Eq. (4)). We will clarify this in the revised version.\n\n---\n> Why does the diversity increase with $N$ (in Table 1)? Is the diversity increasing solely at the expense of validity?\n\nA larger $N$ means the model predicts more tokens at once at each time step, which means the model is given fewer tokens to complete the sequence on average. The less conditional information results in more freedom in the generation, leading to an increase in diversity. On the other hand, it also puts more burden on the model capacity, which may lead to a drop in molecular validity (Table 1 and Figure 4).", "claims_and_evidence": "All the claims mentioned in the summary above are supported well by the experiments.", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "There are no proofs as such. I checked the derivation in Appendix C.", "experimental_designs_or_analyses": "The experiments are carefully designed to support the main claims of the paper.", "relation_to_broader_scientific_literature": "The paper introduces the non-autoregressive counterpart to SAFE-GPT (Noutahi et al., 2024). Both GenMol and SAFE-GPT use SAFE representations to solve various tasks in the drug discovery pipeline using a single model. The paper uses the MDLM (Sahoo et al (2024)) and introduces a new guidance method, which is inspired from autoguidance (Karras et al. (2024)).", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "The paper can be improved by comparing the proposed guidance method to existing methods in the literature like Nisonoff et al. (2024).", "other_comments_or_suggestions": "1. $x_{\\theta}^l(z_t, t)$ is already a probability distribution over the token vocabulary, and you could introduce the temperature in the softmax used in $x_{\\theta}^l(z_t, t)$ itself. Is there a specific reason for applying the softmax(log(x)) transformation again? If there is, please state it.", "questions_for_authors": "1. Why does the diversity increase with N (for example in table 1)? I would expect the diversity to be low for higher N. Is the diversity increasing solely at the expense of validity? \n\n2. I'm struggling to understand how MCG is implemented for goal-directed generation. All we can see from eq 7 is that the scores of the tokens are constructed with the scores when some more tokens are masked (governed by $\\gamma$). Reframing the question, how is the goal value $y$ involved in eq 12 in Appendix C?", "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": "dKIfxw51zk", "reviewer": "Reviewer_5s8Z", "summary": "GenMol is a generalist molecular generative model that uses a masked discrete diffusion framework with a BERT-based architecture to generate SAFE molecular sequences, enabling efficient, non-autoregressive decoding and better sampling efficiency. It introduces fragment remasking to optimize molecules by selectively regenerating fragments, enhancing chemical space exploration, and employs molecular context guidance (MCG) to refine predictions using full molecular context. GenMol outperforms existing models across various drug discovery tasks, demonstrating its performance in molecular design.\n\n## update after rebuttal\n\nI believe the authors have adequately addressed all of my concerns. I have also reviewed the points raised by the other reviewers and continue to find this work solid, novel, and impactful. There are no remaining concerns—minor or major—from my side, so I have adjusted my score accordingly.", "questions": "", "limitations": "", "rating": 5, "confidence": null, "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": "We sincerely appreciate your comments that all our claims are supported by extensive evaluations, that our proposed fragment-based generation approach aligns well with industry needs, and that our paper demonstrates strong methodological innovation. We address your questions below.\n\n---\n> Additional baselines, such as reinforcement learning-based optimization, could provide a more comprehensive validation. (Also related to Reviewer 9oFx’s comment: *In de novo generation and fragment-based generation, only one baseline is included.*)\n\nWe used the PMO benchmark (Gao et al., 2022) for the goal-directed hit generation task (Tables 3, 11-13), and **compared our GenMol with a total of 28 baselines**. These baselines span a broad class of molecular optimization methods, including reinforcement learning methods, genetic algorithm methods, and Bayesian optimization methods. The proposed GenMol achieves the best performance against all of these baselines and its ablated baselines (Table 5).\n\nFurthermore, here we compare GenMol with DiGress (Vignac et al., 2023), a graph discrete diffusion method on the *de novo* and fragment-constrained generation tasks, where **GenMol significantly outperforms DiGress in both quality and sampling efficiency**.\n\n**Table: *De novo* generation results.**\n| Method | Validity (%) | Uniqueness (%) | Quality (%) | Diversity (%) | Sampling time (s) |\n| --- | --- | --- | --- | --- | --- |\n| DiGress | 89.6 | 100.0 | 36.8 | 0.885 | 1241.9 |\n| GenMol ($N=1,\\tau=0.5,r=0.5$) | 100.0 | 99.7 | 84.6 | 0.818 | 21.1 |\n| GenMol ($N=1,\\tau=1.5,r=10$) | 95.6 | 98.3 | 39.7 | 0.911 | 20.9 |\n\n**Table: Fragment-constrained generation results.**\n| Method | Task | Validity (%) | Uniqueness (%) | Quality (%) | Diversity (%) | Distance |\n| --- | --- | --- | --- | --- | --- | --- |\n| DiGress | Linker design (scaffold morphing) | 31.2 | 84.3 | 6.1 | 0.745 | 0.724 |\n| | Motif extension | 21.8 | 94.5 | 4.2 | 0.818 | 0.794 |\n| | Scaffold decoration | 29.3 | 91.0 | 9.1 | 0.793 | 0.785 |\n| | Superstructure generation | 26.7 | 85.9 | 7.4 | 0.789 | 0.776 |\n| GenMol | Linker design (scaffold morphing) | 100.0 | 83.7 | 21.9 | 0.547 | 0.563 |\n| | Motif extension | 82.9 | 77.5 | 30.1 | 0.617 | 0.682 |\n| | Scaffold decoration | 96.6 | 82.7 | 31.8 | 0.591 | 0.651 |\n| | Superstructure generation | 97.5 | 83.6 | 34.8 | 0.599 | 0.762 |\n\n---\n> How molecular context guidance (MCG) is integrated into the diffusion process could be more explicitly explained. (Also related to Reviewer 9oFx’s comment: *How is the molecular context defined in Lines 106-107?*)\n\nMCG is Autoguidance (Karras et al., 2024) specifically designed for the discrete diffusion of GenMol to fully utilize the given molecular context information. This is done by comparing two outputs from a single model, with good (i.e., less masked) and poor (i.e., more masked) input, respectively. Specifically, the good input $z_t$ is a given partially masked sequence, which is further masked by $100 \\cdot \\gamma$ % to yield poor input $\\tilde{z}_t$, and the two resulting logits are compared and calibrated as Eq. (7). Intuitively, the logits with the poor input exaggerate errors in the logits with the intact input, and $w>1$ removes the errors from the good logits.\n\nFollowing the use cases of *context* in LLM literature, *molecular context* in MCG denotes the given information in the form of the input sequence, i.e., given fragments in fragment-constrained generation and partially masked sequences during the fragment remasking in goal-directed generation.\n\nWe apologize for the brief description due to space limitations and will clarify this in a future revision.\n\n---\n> What would be the cost of training this model on a broader space like the Enamine 1B dataset to fully explore drug space by leveraging this model's strengths?\n\nThe cost of training GenMol on a new SMILES-formatted dataset comes from two sources: (1) preprocessing SMILES -> SAFE, and (2) training with new SAFE strings. SMILES to SAFE conversion of 249,455 ZINC250k molecules takes ~6 minutes. For GenMol to go through 1B training molecules with the same setting in the paper would take ~60 hours.\n\n---\n> Is there any possibility of conditioning this model to guide the generation towards a specific biological target?\n\n**GenMol can be easily extended to be conditioned on specific biochemical conditions** by adopting any off-the-shelf conditioning methods. For example, protein pocket embeddings obtained from a pretrained protein model can be incorporated into GenMol via cross-attention to generate corresponding ligands [A, B].\n\n---\n*References*\n\n[A] Fu et al., Fragment and geometry aware tokenization of molecules for structure-based drug design using language models, ICLR, 2025.\n\n[B] Wang et al., Token-Mol 1.0: tokenized drug design with large language model, arXiv, 2024.", "claims_and_evidence": "All of the claims are supported by extensive empirical evaluations and experiments. The paper is overall convincing and presents extensive empirical results.", "methods_and_evaluation_criteria": "Yes, it follows previous works' evaluation metrics and datasets to evaluate itself.", "theoretical_claims": "There is just a simple derivation of MCG which is simple and straight-forward. Thus, I have checked all of the proofs for theoretical claims.", "experimental_designs_or_analyses": "The experimental design in the paper is generally well-structured, covering a range of drug discovery tasks to validate GenMol’s capabilities. The use of a single checkpoint without task-specific finetuning strengthens the claim of model generalization. However, I would like to mention this aspect:\n**Fragment Remasking & MCG Ablations**: The ablation studies show that fragment remasking and molecular context guidance (MCG) improve performance, but it is unclear how these compare to alternative molecular generation strategies beyond SAFE-GPT. Additional baselines, such as reinforcement learning-based optimization, could provide a more comprehensive validation.\nOverall, the experimental design is sound, but a more rigorous comparison with additional baselines would further strengthen the findings.", "relation_to_broader_scientific_literature": "The key contributions of GenMol build upon and extend several existing ideas in molecular generative modeling, discrete diffusion, and drug discovery optimization. Here’s how they relate to the broader scientific literature:  \n\n1. **Discrete Diffusion for Molecular Generation**: GenMol leverages a **masked discrete diffusion model**, which aligns with prior work on discrete diffusion in natural language processing (Austin et al., 2021) and molecular modeling (Sahoo et al., 2024; Shi et al., 2024). This approach contrasts with continuous diffusion models (e.g., Hoogeboom et al., 2022), which have been more commonly used in generative chemistry. By adopting a **BERT-like bidirectional attention** mechanism, GenMol improves upon **autoregressive generative models** (e.g., Gomez-Bombarelli et al., 2018) by enabling parallel token prediction and increased efficiency.  \n\n2. **Fragment-Based Molecular Design**: The **fragment remasking strategy** extends previous work in fragment-based drug design, which has long been a key principle in medicinal chemistry (Congreve et al., 2003). While prior generative models often treated molecules as atomic graphs (Jin et al., 2018; You et al., 2018), GenMol aligns more closely with chemist intuition by using fragments as the primary unit of generation. This also improves **chemical space exploration**, addressing limitations in previous sequence-based molecular design models like SAFE-GPT (Noutahi et al., 2024).  \n\n3. **Molecular Context Guidance (MCG)**: The proposed MCG technique enhances discrete diffusion by incorporating context-dependent constraints, similar in spirit to **controlled generation in NLP** (Keskar et al., 2019) and **conditional molecular design** (Zhavoronkov et al., 2019). Unlike previous methods that relied on reinforcement learning (Olivecrona et al., 2017) or constrained optimization (Gao et al., 2022), GenMol integrates context-awareness **directly into the generative process**, enabling more targeted molecular design.  \n\n4. **Efficiency and Generalization Across Drug Discovery Tasks**: GenMol’s ability to perform **de novo generation, fragment-constrained generation, and goal-directed optimization** in a unified manner improves upon **task-specific models** such as GraphAF (Shi et al., 2020) and f-RAG (Lee et al., 2024a). Unlike previous approaches that required separate fine-tuning for different tasks, GenMol’s single checkpoint generalizes across multiple drug discovery scenarios, making it more adaptable to real-world applications.  \n\nIn summary, GenMol synthesizes advances in **discrete diffusion, bidirectional sequence modeling, fragment-based design, and guided molecular generation** to offer a more **efficient, generalizable, and chemically intuitive** approach to molecular generation, surpassing limitations of existing autoregressive and fine-tuned models in drug discovery.", "essential_references_not_discussed": "Maybe the work can cite (Nie, S., Zhu, F., You, Z., Zhang, X., Ou, J., Hu, J., ... & Li, C. (2025). Large Language Diffusion Models. arXiv preprint arXiv:2502.09992.) as the most recent work on sequence generation with diffusion models which has amazing results and has caught attention of the community recently.", "other_strengths_and_weaknesses": "One of the key strengths of this paper is its **fragment-based generation approach**, which aligns well with industry needs. In practical drug discovery, designing and synthesizing molecules from predefined building blocks is significantly more efficient than synthesizing entirely new structures from scratch. This makes GenMol's fragment remasking strategy particularly valuable, as it reflects real-world workflows and can accelerate the molecular design process.  \n\nAdditionally, the paper demonstrates strong **methodological innovation** by combining **masked discrete diffusion with bidirectional sequence modeling**, enabling non-autoregressive parallel decoding. This enhances both sampling efficiency and the quality of generated molecules, addressing limitations in prior autoregressive and graph-based models. The **unified generative framework** that generalizes across multiple drug discovery tasks without requiring task-specific fine-tuning further adds to its significance, as it simplifies deployment in practical settings.  \n\nOn the other hand, while the paper presents extensive empirical validation, its clarity could be improved in certain sections. Some methodological details, such as how molecular context guidance (MCG) is integrated into the diffusion process, could be more explicitly explained to enhance reproducibility. Furthermore, while GenMol shows superior performance over existing methods, an **ablation study on model scalability and robustness**—such as how it performs on structurally diverse datasets beyond ZINC and UniChem (e.g. Enamine)—would further strengthen the work.  \n\nOverall, this paper makes an **original and significant contribution** by developing a **computationally efficient, chemically intuitive, and versatile generative model** for molecular design. Its alignment with both **theoretical advancements in generative modeling and practical drug discovery needs** makes it a valuable addition to the field.", "other_comments_or_suggestions": "Nothing in mind.", "questions_for_authors": "- What would be the cost of training this model on a broader space like Enamine 1B dataset, or even bigger datasets to fully explore drug space by leveraging this models' strengths?\n- Is there any possibility of conditioning this model on a specific gene expression or the protein embedding of a specific target to guide the generation towards a specific biological target or not?", "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": "lOQtop7sga", "reviewer": "Reviewer_9oFx", "summary": "The paper focuses on drug discovery and introduces a Generalist Molecular Generative Model (GenMol), a new discrete diffusion model for molecular generation. This approach has the potential to be applied to various drug-related tasks, including de novo generation, linker generation, and hit/lead generation. The model represents molecules as sequential data using fragment-based SMILES strings, known as SAFE strings. According to Figure 2, the work still utilizes atom-level tokens, which may be somewhat counterintuitive, as one might expect fragments to be used as tokens. The discrete diffusion model operates through a masking (forward) and unmasking (reverse) process.\n\nFor goal-directed generation, the work incorporates fragment remasking and molecular context guidance to enhance generation quality. Experiments are conducted on (1) De novo generation, (2) fragment-based generation, (3) hit generation using the Practical Molecular Optimization benchmark, and (4) lead generation for five protein targets. The results, along with ablation studies, suggest that the proposed approach is promising. However, further clarification on certain aspects of the methodology and evaluation could strengthen the manuscript.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "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": "We sincerely appreciate your comments that our contribution could aid drug discovery and GenMol is a generalist. We address your concerns below.\n\n---\n> One baseline in de novo and fragment-based generation.\n\nWe included JT-VAE (Jin et al., 2018) in Table 9, and have provided the results of DiGress in our *first response to 5s8Z*.\n\n---\n> No new developments in discrete diffusion. Graph discrete diffusion should be discussed.\n\n**GenMol is the first discrete diffusion framework on small molecule sequences**, which is superior to graph discrete diffusion that suffers from low validity and efficiency (GenMol vs. DiGress above). We also showed **confidence sampling achieves better quality and efficiency in molecular sequence generation**. Moreover, we proposed **MCG tailored for discrete sequence diffusion**.\n\n---\n> It is unclear how many diffusion timesteps are used.\n\nGenMol uses confidence sampling with $N$ denoting the number of tokens to unmask at each step (line 323). GenMol with $N=1$ uses the same number of steps with AR models, and higher $N$ improves efficiency. In Table 1, GenMol with $N=3$ shows higher quality than the AR model with 2.5x faster sampling.\n\n---\n> Could the fragment remasking be adapted to AR models?\n\nYes, but it cannot be applied without relying on a specific generation order in AR models, leading to inferior performance (Table 5).\n\n---\n> Why are fragments not used as tokens?\n\nUsing fragments as tokens is challenging due to **the large vocabulary, limited generalizability, and the difficulty of tokenizing attachment points**. Applying BRICS to ZINC250k yields 39034 fragments, resulting in an imbalance with sequence lengths of 3~5. It also does not allow the model to generate novel fragments. We have introduced **a simpler yet effective approach that generates tokens at atom level and performs the remasking at fragment level**.\n\n---\n> A reason for using SAFE instead of SMILES? Could the approach be adapted to SMILES generation?\n\n**SAFE makes fragment remasking simpler and more effective**, and adapting the approach to SMILES is non-trivial. As tokens that belong to the same fragment are not contiguous in SMILES, SMILES models need to classify each token into fragments before every remasking and cannot guarantee the re-predicted tokens belong to the same fragment. On the other hand, SAFE allows GenMol to easily identify fragments in a sequence and generate new fragments.\n\n---\n> Tokenization details are unclear.\n\nWe adopt the SAFE tokenizer (Noutahi et al., 2024) (lines 725-726), which is a BPE tokenizer with the SMILES regular expression. It covers 1873 different atoms, bonds, and ions, and thus can be universally used for molecule-related tasks.\n\n---\n> Do Sections 5.1 and 5.2 utilize the framework in Figure 3a? It is unclear how the iterations in Figure 3 contribute to the quality and efficiency of generation.\n\nNo, Sections 5.1 and 5.2 use the process of Figure 2(c1-c5) (line 173, Figure 3(a) caption). The generic quality and efficiency stem from the discrete diffusion process, and the iterations in Figure 3(a) are multiple rounds of the process, not the process itself.\n\n---\n> What if the number of calls is low or the oracle is incorrect?\n\nWe followed Gao et al., 2022 in Section 5.3, where the 10k oracle calls were set for all baselines. Nevertheless, we observed that **GenMol consistently performed better in both low and high oracle call regimes**, so we hypothesize that GenMol would perform better with fewer calls or an incorrect oracle, and will include this in the revision.\n\n---\n> Only two baselines in lead optimization. Missing values in Table 4.\n\nIn Table 4, baseline selection and the use of the ‘-’ symbol to indicate failure (no successful leads generated) followed Wang et al., 2023 (lines 412-413).\n\n---\n> The same name was used for the model and the framework.\n\n**GenMol is the framework that uses discrete diffusion in versatile ways for diverse drug discovery tasks**. GenMol adopts the sampling of Figure 2(c1-c5) for *de novo* and fragment-constrained generation and adds fragment remasking of Figure 3(a) for goal-directed optimization.\n\n---\n> How is the molecular context defined?\n\nSee our *second response to 5s8Z*.\n\n---\n> The fragment vocabulary is not sufficiently discussed.\n\nThe training SAFEs are based on BRICS following Noutahi et al., 2024. During inference, we used two other rules (lines 234-236, 756-762): $R_{vocab}$, that cuts one non-ring single bond, to construct the vocabulary, and $R_{remask}$, that cuts all non-ring single bonds, to determine which fragments the remasking will operate on.\n\n---\n> Data statistics should be provided.\n\nWe used the dataset of Noutahi et al., 2024, and will include the statistics in the revision.\n\n---\nWe hope our response could address your questions and clarify any confusion. If our response is satisfactory, we would like to kindly ask you to consider raising your score. Otherwise, we will be happy to further discuss and update the paper.", "claims_and_evidence": "1. It is unclear how many diffusion timesteps are used, making the claims about efficiency unconvincing. These claims appear around Lines 92–94, 200–208, and 634. For example, the diffusion timestep could be set to 500 or 1000, which is a common choice for graph and image diffusion models. But an autoregressive model would perform decoding in a number of steps equal to the maximum number of nodes, which is typically smaller than 500. Additionally, one advantage of autoregressive models is that they do not necessarily need to consider the entire molecular context during generation, whereas GenMol does. This raises further concerns regarding efficiency. The authors should explicitly state the hyperparameters used and provide a comparison of generation times.  \n\n2. The claim around Lines 93–96 that discrete diffusion models enable GenMol to explore chemical space with remasking should also apply to autoregressive models. While the claim holds for GenMol, it is somewhat unclear why this point is specifically listed as an advantage (Line 88). Further clarification would be helpful.  \n\n3. Regarding Lines 99–102, the authors suggest that using fragments as units aligns better with chemists’ intuition for chemical space exploration. If this is the case, why are fragments not used as tokens in the discrete model? It also introduces an additional concern in the following point.  \n\n4. The manuscript lacks a convincing discussion on why SAFE is chosen over SMILES, given that the discrete diffusion model operates at the atom level. In other words, when extending discrete diffusion models to molecular sequential strings, one might first consider SMILES as a more direct option. Is there a specific reason for opting for SAFE instead of SMILES? While the manuscript discusses fragment-based remasking, this concept could also be applied to SMILES, as SAFE is derived from SMILES, and the molecular structures represented by SAFE could also be represented using SMILES.", "methods_and_evaluation_criteria": "1. The details regarding tokenization are unclear. The vocabulary size is stated as 1880 (Line 726), but it is not explained how this vocabulary is constructed. Additionally, it is not discussed whether this vocabulary can generalize to broader tasks, which is important given the claim about the model being a generalist. Clarification on these aspects would be beneficial.  \n\n2. The fragment vocabulary derived from the dataset is not sufficiently discussed. The BRICS algorithm is briefly mentioned in Line 177, and I assume it serves as the underlying algorithm for defining fragments. However, BRICS tends to prioritize synthetically relevant fragments. As noted in previous work [1], BRICS has several limitations: it breaks bonds based on a limited set of chemical reaction rules, tends to generate a few large fragments, creates variations of the same underlying structures, and results in a very large vocabulary with many infrequent fragments. \n\n3. It is unclear how the iterations in Figure 3 contribute to the quality and efficiency of generation. Additional analysis on this aspect would be helpful to better understand its impact.  \n\n4. Figure 3 shows that the framework heavily relies on the scoring oracle. What happens if the oracle is not available? Additionally, if the function used for the oracle does not provide accurate scores, how would this affect performance? Does the proposed GenMol model still perform well under these conditions? \n\nReference:\n\n[1] Motif-based Graph Self-Supervised Learning for Molecular Property Prediction. NeurIPS 2021.", "theoretical_claims": "The work presents the derivation of Eq. (7) in Appendix C. I have no issues about it.", "experimental_designs_or_analyses": "1. The authors should provide data statistics for the dataset used to pretrain GenMol. \n\n2. In molecular and fragment-based generation, only one baseline is included. To ensure a comprehensive evaluation, the authors should consider testing additional baselines, including discrete diffusion models, graph diffusion models, and autoregressive models (e.g., SMILES-based models). This would help demonstrate the advantages of SAFE-based discrete diffusion in a fair comparison.  \n\n3. The benefits of the method proposed in Section 4 (or Figure 3a) could also be applicable to SMILES-based generation. Could the approach be adapted to other baselines? Discussing this possibility would enhance the generalizability of the proposed method.  \n\n4. There are several missing values in Table 4.", "relation_to_broader_scientific_literature": "The contribution could aid drug discovery in the fields of chemistry and biology.", "essential_references_not_discussed": "There is a lack of survey and discussion on discrete molecular diffusion models. There  are many graph-based discrete diffusion models, the authors should discuss their efficiency and performance and compare them in experiments if necessary. For example, the authors have discussed about DiGress, which could be used for fragment-based generation as presented in the original paper, but it is not tested in Table 2.", "other_strengths_and_weaknesses": "The contributions of this work are multifaceted, including the GenMol model, which refers to the checkpoint used in the experiments and is expected to function as a generalist model. Additionally, the work introduces the GenMol framework (e.g., Figure 3a), which appears to have the potential for generalization to other models. However, the authors use the same name for both the model and the framework, which at times makes the manuscript difficult to follow. It is unclear whether certain benefits discussed apply specifically to the model or to the framework.  \n\nFor instance, the authors present discrete diffusion as a major contribution (Lines 80–108), but it does not appear to introduce any new developments to the discrete diffusion model itself (as described in Section 4). Using discrete diffusion models for molecules is not a new idea as well.\n\nFurthermore, does Section 5.1 focus on unconditional generation? If so, how can the proposed GenMol framework in Figure 3a be adapted to this problem?\n\nThe goal of this work is to develop a generalist model, and accordingly, it has been tested on multiple tasks with results presented in various tables. However, the selection of baselines for each task may not be comprehensive. For example, in de novo generation and fragment-based generation, only one baseline is included, and for lead optimization, only two baselines are considered. Expanding the comparison to include more relevant baselines would strengthen the evaluation.", "other_comments_or_suggestions": "NA", "questions_for_authors": "1. How is the molecular context defined in Lines 106–107?  \n\n2. Could the fragment remasking approach be adapted to other autoregressive models?  \n\n3. The molecular optimization performance heavily depends on frequent Oracle calls (e.g., 10,000 times). What would happen if the number of calls were significantly reduced to fewer than 10 or if inaccurate models were used as Oracles?  \n\n4. Do Sections 5.1 and 5.2 utilize the framework presented in Figure 3a?", "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:

Meta-review: The paper proposes GenMol, a generalist molecular generative model based on a masked discrete diffusion framework with a BERT-based architecture to generate SAFE molecular sequences. It introduces fragment remasking to optimize molecules by selectively regenerating fragments, enhancing chemical space exploration, and employs molecular context guidance (MCG) to refine predictions using full molecular context. The method can address various drug discovery tasks uniformly (de novo generation, fragment-constrained generation, hit generation, and lead optimization) using a single backbone model. 


Strengths:
- The paper develops a discrete diffusion model for sequence generation for diverse drug discovery scenarios.
- The paper introduces a fragment remasking technique and Molecular Context Guidance (MCG) for fragment-constrained generation
- Experiments show superior performance on multiple drug design tasks for five protein targets. 

Weaknesses:
- Ablation studies on model scalability and robustness are missing. 
- In molecular and fragment-based generation, only one baseline is included.
- The paper lacks discussion on SAFE vs. SMILES representations.
Decision: Accept (poster)