TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence
Authors: Feng Jiang
Mangal Prakash
Hehuan Ma
Jianyuan Deng
Yuzhi Guo
Amina Mollaysa
Tommaso Mansi
Rui Liao
Junzhou Huang
Venue: NeurIPS 2025 spotlight ()
Keywords: Molecular representation learning
Molecular property prediction
Multimodal learning
Primary area: machine_learning_for_sciences
TL;DR: TRIDENT integrates molecular structures, text, and taxonomic annotations via volume-based global alignment and local correspondence learning, achieving state-of-the-art performance in molecular property prediction tasks.

Abstract: Molecular property prediction aims to learn representations that map chemical structures to functional properties. While multimodal learning has emerged as a powerful paradigm to learn molecular representations, prior works have largely overlooked textual and taxonomic information of molecules for representation learning. We introduce TRIDENT, a novel framework that integrates molecular SMILES, textual descriptions, and taxonomic functional annotations to learn rich molecular representations. To achieve this, we curate a comprehensive dataset of molecule-text pairs with structured, multi-level functional annotations. Instead of relying on conventional contrastive loss, TRIDENT employs a volume-based alignment objective to jointly align tri-modal features at the global level, enabling soft, geometry-aware alignment across modalities. Additionally, TRIDENT introduces a novel local alignment objective that captures detailed relationships between molecular substructures and their corresponding sub-textual descriptions. A momentum-based mechanism dynamically balances global and local alignment, enabling the model to learn both broad functional semantics and fine-grained structure-function mappings. TRIDENT achieves state-of-the-art performance on 18 downstream tasks, demonstrating the value of combining SMILES, textual, and taxonomic functional annotations for molecular property prediction. Our code and data are available at https://github.com/uta-smile/TRIDENT.

TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence
Feng Jiang'
Mangal Prakash² Hehuan Ma²
Jianyuan Deng²
Yuzhi Guo?
Amina Mollaysa Tommaso Mansi
Rui Liao² Junzhou Huang'
University of Texas at Arlington
2 Johnson Johnson Innovative Medicine
[fxj8843, hehuan. ma, yuzhi guo)guuummaaaaa edu (jzhuang) edu (MPraka12, JDen334, MAminanm, TMansi, RLiao2)8ITS. JNJ.
Abstract
Molecular property prediction aims to learn representations that map chemical structures to functional properties. While multimodal learning has emerged as powerful paradigm to learn molecular representations, prior works have largely overlooked textual and taxonomic information of molecules for representation learning. We introduce TRIDENT, novel framework that integrates molecular SMILES, textual descriptions, and taxonomic functional annotations lear rich molecular representations. To achieve this, we curate a comprehensive dataset molecule-text pairs with structured, multi-level functional annotations. Instead of relying on conventional contrastive loss, TRIDENT employs a volume-based alignment objective jointly align tri-modal features the global level, enabling soft, geometry-awar alignment across modalities. Additionally, TRIDENT in- troduces a novel local alignment objective that captures detailed relationships between molecular substructures and their corresponding sub-textual descriptions. A momentum- based mechanism dynamically balances global and local alignment, enabling the model to learn both broad functional semantics and fineggrained fuructire-uunctonn mappings. TRIDENT achieves state-of-the-is performance on 18 downstream tasks, demonstrating the value of combining SMILES, textual, and taxonomic functional annotations for molecular property prediction. Our code and data are available at https: : //githuu.. com/uta-smile/Teet
smi seaaee//EETT
1 Introduction
Molecular representation learning, which converts complex chemical structures into computational features, has been instrumental in advancing various aspects of drug discovery including virtual screening, and molecular design [15] 4 344.. Multi-modal molecular models further enhance rep- resentation quality by integrating structural, textual, and functional information, enabling better generalization and predictive performance [16]. These approaches hold promise for unlocking deeper insights into chemical space and accelerating the discovery of therapeutic compounds with desired properties.
However, current multimodal approaches 21 35 face three key limitations: (1) Overlooking fine- grained annotations across taxonomies: Most existing methods simplify the representation of molecules by focusing unified functional descriptions, neglecting the nuanced annotations provided by different taxonomic systems. As illustrated in Figure the same molecule may have distinct emphases depending the taxonomy: for example, the LOTUS Tree (3 taxonomy highlights
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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natural product classifications, whereas the MeSH (Medical Subject Headings) Tree [4] taxonomy emphasizes medical functionalities of the same molecule. Ignoring these taxonomy-specifie, fine- grained annotations risks reducing molecules to flat entities, thereby failing to capture the multi- faceted and structured nature of chemical functions. (2) Alignment limitations: Aligning modalities such as molecular structures, textual descriptions, and taxonomic functional annotations is inherently complex. Existing methods rely on pairwise alignment schemes anchored to a single modality, which struggle to model the interdependencies across all modalities [43] 17 38 3) particularly when one modality encodes nested or multi-level information [5). (3) Neglect of local correspondences: Many approaches focus exclusively on molecule level alignment, disregarding the fine- -ne-grained relationships between molecular substructures (e.g.. functional groups) and their corresponding sub-textual descriptions. This omission limits the expressivity of the learned representations and constrains their applicability in molecular property prediction tasks.
To address these limitations, we introduce the TRIDENT (Tri-modal Representation Integrating De- scriptions, Entities, and Taxonomies) framework for molecules that jointly models molecular SMILES, textual descriptions, and multi-faceted Hierarchical Taxonomic Annotation (HTA), Central to TRI- DENT is the HTA modality, which organizes molecular function across hierarchical classification levels. We curate high quality dataset of 47,269 <SMILES, Text, IITA> triplets from PubChem [3] annotated under 32 classification systems. To tackle the challenge of aligning these diverse modalities, TRIDENT leverages a volume- oluume-based contrastive loss, enabling soft. geometry-awarc alignment of all three modalities. While recently proposed for seneral-purpor this formulation to the molecular domain for the first time, where the modalities structurally diverse and include taxonomic semantic labels. Furthermore, TRIDENT introduces a novel local alignment module that links molecular substructures to their associated sub-textual descriptions, capturing fine-grained dynamically integrates global and local alignments to optimize the representation learning process (see Figure D for an overview).
modality alignment [5], we extend
geructureffunctiio relationships. A momentum- based balancing mechanism
We demonstrate that TRIDENT achieves consistent and substantial improvements over existing molecular representation learning methods. Our framework sets a new benchmark, delivering of-te-of-thc-ar performance across 11 downstream molecular property prediction tasks estab- lished benchmarks, while remaining modular and flexible, allowing integration of different modality encoders without the need for architectural modifications. We have also created high-quality, triplets, which forms the foundation for this work
comprehensive dataset of molecule-text-funcee and future research. To summarize, make the following contributions:
Introducing a Hierarchical Taxonomic Annotation (HTA) modality for molecules, supported by a newly curated high-quality multimodal dataset consisting of 47,269 <SMILES, Text, HTA>> triplets annotated across 32 diverse taxonomic classification systems. This enables a structured, multi-level functional understanding of molecules, providing a novel resource for molecular representation learning.
A unified global- local alignment strategy that integrates a volume- based contrastive loss for tri-modal global alignment with novel local alignment module for substructure-subter. correspondence, dynamically balanced via a momentum- -baed mechanism.
Demonstrated state-of-the-ar performance across 11 molecular property prediction tasks, validating the effectiveness of hierarchical taxonomic annotations as modality, the proposed alignment strategies, and the quality of the curated dataset.
2 Related Works
2.1 Molecule-Text Multimodal Learning
Recent advancements in molecular representation learning have demonstrated the power of multi- modal approaches that integrate information from molecular graphs, SMILES strings, and textual descriptions to enhance property prediction and drug discovery. Graph Neural Networks (GNNs) have become the backbone of graph-based methods, with models like GROVER 30) and MoICLR (36) leveraging contrastive learning to produce richer molecular embeddings. Multimodal models such as KV-PLMM (39 and MolTS [7] treat SMILES and text as separate languages for pre-training via auto-encoding objectives, while MoMu [35 and MoleculeSTM [16] utilize independent encoders

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Global Aligrmene
Monsentum Soft Aligament
Local Aligrment
Trinabie
FG Functon Group
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PG Panction
Molecular Fanction
Epocls
E. Midocaie Ensoderr
f Molecule Projection Modale
Annoroiic Annoration
E, Ton Encoder
fg
Test Projection Module
Figure I: Overview of TRIDENT. TRIDENT jointly models molecular SMILES, natural language descriptions, and Hierarchical Taxonomic Annotations (HTAs) to learn rich molecular representations. The framework employs volume-based contrastive loss for soft global tri-modal alignment and a local alignment module that links molecular substructures to sub-text spans. A momentum- based mechanism dynamically balances the contribution of global and local objectives during training. This multimodal, multi-level alignment enables precise and semantically grounded molecular understanding.
with cross-modal contrastive learning to align graphs and texts. MolFM 21 extends this paradigm by incorporating molecular structures, biomedical texts, and knowledge graphs capture more comprehensive molecular relationships. However, despite this progress, the textual modality in existing models often derives from unstructured or single-layered descriptions, limiting the capacity to represent molecular functions across diverse biological roles and hierarchical categories. This lack of structured semantic alignment limits the ability of models to reason over complex molecular behaviors and relationships. Our work addresses this gap by introducing high quality dataset to incorporate hierarchical taxonomic annotations for molecules, learning fine- -ne-ggrined hierarchical molecule- molecule-function relationships
2.2 Contrastive Learning for Multimodal Alignment
Contrastive learning has emerged a powerful strategy for aligning representations across modalities. Seminal models such as CLIP (28) demonstrated effective image-text alignment, inspiring extensions to other domains including audio (CLAP) [8], video (CLIP4Clip) [20]. and point clouds (Point- CLIP) 40 These models typically learn by pulling semantically similar cross-modal pairs closer while pushing dissimilar ones apart. More recent approaches such as CLIP4VLA (32] ImageBind (9), and LanguageBind [43] explore multimodal fusion, often anchoring learning around a central modal- ity like images text. GRAM (5) advances this direction by introducing geometry-
volume based contrastive objective, but it primarily focuses on audio-video-text pairs without structured semantic hierarchies. In bioinformatics. multimodal learning has shown promise in integrating diverse biological data sources [1]] 24 such as molecular structures, protein sequences, and biomed- ical text, to enhance understanding of complex biological systems and accelerate drug discovery [12 22 23). Unlike existing methods, our TRIDENT framework tackles the unique challenges of molccule-text alignment by incorporating hierarchical taxonomic relationships to capture functional semantics, and introducing global and local alignment modules with momentum- mechanism. This enables fine-gun grained substructure-functin correspondence and a richer multimodal embedding space tailored to molecular understanding.
3 Method
In this section, we provide detailed introduction to the implementation of the TRIDENT framework, as illustrated in Figure T which addresses the shortcomings of existing methods in capturing structured understanding of molecular functions across different hierarchical functional categories.

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Molecwlar Functional Description
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Hievarcioal Taxonomic Annotation
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LOILS Tree
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Hierarchical Functional Description
Summary
Figure 2: Traditional molecular functional descriptions typically obtained by inputting molecule into PubChem, where a general functional annotation is provided, as shown in Steps and 2 of the figure. To achieve more comprehensive knowledge, functional annotations of the molecule are first obtained under different classification systems, as illustrated in Step 3. Then, these annotations are summarized using GPT-4o, resulting in higher-quality textual description, as depicted in Step 4. The blue and green highlighted sections illustrate the different perspectives between traditional text and HTA text descriptions. For detailed processing steps, please refer to the Appendix A
3.1 Hierarchical Taxonomic Annotation (HTA)
To enable structured, hierarchical molecular representations, we introduce the Hierarchical Taxonomic Annotation (HTA) framework, which organizes molecular functions across multiple classification levels. This setup allows the model to capture fine-grained, hierarchical semantics essential for understanding complex molecular properties and their biological roles. We curate high-quality dataset of 47, 27,269 <SMILES, Text, HTA> triplets sourced from PubChem [13] As shown in Figure 2. these triplets annotated across 32 diverse hierarchical classification systems, providing comprehensive, multi-level understanding of molecular behavior. Figure 2 illlstrates the construction pipeline for HTA. Beginning with molecule's SMILES representation, the molecule is queried against PubChem [I3]. This yields a set of traditional functional descriptions, which are typically concise, ontology-aware summaries based on cheminformatics rules. For example, citronellol is described as a monoterpenoid.. with a role as a plant metabolite. While such descriptors are chemically accurate, they often lack broader context, such ecological origin, industrial relevance, or toxicological implications.
To address this limitation, augment the molecule's 's annotation space through structured taxonomic enrichment by mapping it into multiple biological and chemical taxonomies. For example, the LOTUS Tree (33 highlights natural product classifications, whereas the MeSH (Medical Subject Headings) Tree 14 emphasizes medical functionalities the same molecule. Through this multi multi-perspective approach, these hierarchies expand the molecular profile beyond flat descriptors into deeply nested semantic trees spanning chemistry, biology, and pharmacology.
In the final stage, we leverage GPT-4o I 25 to synthesize the retrieved structured annotations into a figeliiye human-reas readable HTA. Unlike traditional descriptors, HTAs encode multi perspective knowledge: they trace the chemical derivation (c.g., from citronellal), mention natural sources (c.g., rose oil), functional applications (e.g.. fragrance in various products), and regulatory or biomedical associations (c.g., environmental protection agencies, blood exposome). This generative synthesis is guided by structural prompts and validated by domain experts to ensure factual accuracy and interoperability.
Crucially, the information content in HTAs complementary to traditional functional annotations. While the latter provides standardized yet narrow chemical definitions, HTAS integrate cross-domain knowledge that aligns better with how biological and industrial experts interpret molecular function. The results (Table 3) indicate that simultaneously incorporating HTAs and traditional functional

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annotations helps the model capture both fine- ine-grained structural features and broader biological semantics, leading to improved performance across a range of molecular property prediction tasks.
3.2 Geometry-based Global Alignment
We aim to learn meaningful multimodal representations by jointly modeling three data modalities: molecule SMILES (M), textual descriptions (7). and HTA (H). SMILES representations utilize the encoder Em, while both textual descriptions (T) and HTA (H) share common text encoder Et.
Traditional multimodal approaches typically rely on pairwise similarity metrics such as cosine (M,,M,,) However, these methods often anchor one modality and align similarity: (os() = |M,
others to it independently, failing to capture higher-order relationships across all modalities. To address this, GRAM [5] introduced a geometry-bau baased alignment approach that uses the volume of the parallelotope spanned by modality vectors
global measure of alignment. Specifically, for three normalized embeddings m, t, and h, the volume of the parallelotope is computed as Vol(m, t, h) = /I (m, t)2 (m, h)2 (t, ht2 2(m, t) (t, h2 (h, m), which reflects the overall geometric alignment of the embeddings. The volume shrinks the modalities converge and grows as they diverge. Unlike pairwise contrastive learning methods, this formulation was shown to capture the global structure of cross-modke interactions in principled and scalable way for audio-video-text pairs [5].
Global Volume-based Contrastive Loss. Following the approach introduced GRAM S we construct global contrastive objective over the three modalitee-SMMIIES, tions, and HT' annotations. Each modality processed through modality-specific encoder followed by modality-peccific projection head (implemented as three-layer MLP) to map the embeddings into a shared latent space, yielding embeddings m, and for SMILES, text, and HTA, respectively.
SMILES, traditional text descrip-
To holistically align the three modalities, we compute the volume of the parallciotope formed by the triplet of unit-normalized vectors (m, t, h). We define a bidirectional global contrastive loss that captures two complementary retrieval directions. In the first direction, denoted LMITH, the model trained to retrieve the correct semantic context- comriciiioom both textual and taxonomic annotations -iven molecular embedding. That is, given mi, the loss encourages the volume Vol(m;; L, h;) to be smaller than volume spanned by any mismatched triplets (m, I,, h2) for j #
1
B
exp(- Vol(V, h.)/T)
LMITH
log 1=1
B
EØ= (oop--ool(m;,tte hT)/T)
where B is the batch size and T is learnable temperature parameter.
Conversely, the second direction, LTHMM, considers the retrieval of the correct molecule given the semantic context. Here, the volume of the correct triplet (mi 4; hx) is minimized relative to all volumes spanned by mismatched triples (m,, t, h4):
exp( Vol L, hz)/T)
LTHZM
Σ log
Ej= - exp(-Vol(mp 14, h:)/r)
The final loss averages both directions to ensure mutual semantic alignment of all three modalities:
1
Cg 2 (CMETH + LTHZM).
This bidirectional formulation encourages robust triadic alignment, capturing global structure across modalities more effectively than traditional pairwise contrastive losses.
3.3 Fine-grained Local Alignment
While the global alignment captures the overall semantic relationships among modality embeddings, it may overlook fine-grained correspondences between molecular functional sub-groups and their sub- textual descriptions. For instance, local features such as aromatic rings, hydroxyl groups, or aliphatic chains often correspond to specific phrases in molecular descriptions or to fine-level taxonomic labels.
5

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To address this limitation, we introduce a local alignment contrastive loss that complements the global volume-basee objective. Unlike GRAM [5], which operates solely at the level of full modality embeddings, our method leverages the compositional nature of molecules to align substructures with their semantic counterparts in text and taxonomy.
By decomposing each molecule into interpretable substructures and anchoring them to matched textual or taxonomic segments, encourage the model learn fine-grained correspondences across modalities, This local supervision enforces semantic consistency not only at the global level but also within the internal structure of molecular representations.
3.3.1 Functional Group-Level Representation
To enable fine-grained alignment, we construct high-quality dataset that links functional group structures with their corresponding semantic descriptions. Using RDKit, screen and identify functionally significant groups frequently found in drug-like molecules. These groups include moieties such as hydroxyls, amines, carboxyls, and aromatic systems. For each group, comprehensive textual descriptions are curated through a hybrid process involving GPT-4o [[] and expert review by professional chemists. This ensures both semantic richness and domain accuracy, resulting in curated dataset of 85 functional groups paired with high-quality textual annotations.
During training, extract all (k) prominent functional groups from each molecule based its SMILES string using the RDKit parser. Each functional group is encoded into the shared latent space using mopality-peecific encoders: the SMILES encoder and projector are used obtain the structural embeddings /9,,....... Jg.. while the corresponding textual descriptions are processed through the text encoder and projector produce the text embeddings fgh,, f'gl:--.... Jgl4. To obtain consolidated representation, we apply max maxx-oooingg operation over the individual embeddings:
f Spooled Pool(fg1: fg2. f9k).
(4)
f foooodd Pool(fgt, fgt2. fgtt).
(5)
This process forms the basis to align sets of fine-grained functional units in molecules with their semantic counterparts natural language for our local contrastive alignment loss.
3.3.2 Local Alignment Loss
Using these pooled representations, we define our bidirectional local alignment contrastive loss as follows.
LGG2T 1
exp(J Ipooted: S'glpooteaa//?
log
j=1 exp( Spooted,; f pootedd.
B
LT2FG = - Σ
log
explfgpooted. fglpooted
(6)
Σ exp(f (ppootedd f poolecde)
i=1
L - (FFC2T LT2Fa),
where B is the batch size and T is the same temperature parameter used in the global loss. The local contrastive loss operates bidirectionally groups and text. The first term, LEG2T, can be seen as asking: Given a structural embedding of functional groups, can we retrieve the correct description from pool of candidates? Conversely, the second term, LT2FG, poses the reverse query: Given a textual description, can we recover the correct functional group structure from batch of molecules? This dual supervision encourages the model to not only generate chemically meaningful embeddings of functional substructures but also associate them with precise and unambiguous textual counterparts.
ensure mutual semantic grounding between functional
3.4 Momentum-hasee Integration
To effectively integrate global and local alignments, we adopt a momentum based approach that dynamically adjusts the importance of each alignment component:
= aLz + (1 CI
(7)

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where a is a momentum coefficient that balances global and local alignments. Instead of using fixed a, we employ an exponential moving average update it during training:
= - + (1 (1-
(8)
C
+ c
where 3 is momentum parameter (0.9), and C/ and c are the respective loss values training step 1. This dynamic adjustment ensures that the model focuses more on the alignment component
that currently has higher loss, effectively addressing the most pressing alignment challenges each training stage.
Experiments
In this section, we present the main results of the proposed multimodal alignment framework across several downstream molecular property prediction tasks. We aim to assess how well our method leverages information from SMILES strings, textual descriptions, and hierarchical taxonomic annotations. We begin by describing the datasets, tasks, and baselines used in our study, followed by a discussion of the main results. Finally, we provide an ablation study to isolate the contribution of each component in our framework. A detailed experimental setup can be found in the Appendix [
Pre-training Datasets. The pre-training dataset is sourced from PubChem, initially following the method described in [16] to obtain approximately 380k SMILES-text pairs. After a series of filtering steps (for detailed processing steps, please refer the Appendix (A) and obtaining HTA information for each molecule, our final pre-training dataset consists of 47 17,269 SMILES-Tex -eXI-TTA triplets. The annotations cover various biological roles, molecular functions, mechanisms of action, and multi-level bioactivity information.
Molecular property prediction benchmarks. We evaluate our model on a broad range of molecu- lar property prediction tasks drawn from two major benchmarks: MoleculeNet (37] and the Ther- apeutics Data Commons (TDC) [TO]. For MoleculeNet, we include 8 classification datasets and regression datasets. The classification tasks comprise toxicity prediction (BBBP, Tox21, ToxCast), side-effect and clinical toxicity prediction (Sider, Clin and bioactivity classification (MUV, HIV, Bace), with performance reported using the ROC-AUC metric. The regression tasks include molecular solubility (ESOL), solvation free energy (FreeSolv), and lipophilicity (Lipophilicity), with performance reported using RMSE.
For the TDC benchmark, we evaluate on 7 datasets, including 6 classification datasets (DILI, Car- cinogens (Languin), Skin Reaction, hERG, AMES, and CYP P450 2C19) and regression dataset (Caco-2). For classification tasks, we report both AUC and accuracy following TDC guidelines, while for the regression task, we report RMSE. Following standard practice, we adopt the scaffold split throughout our methodology to evaluate generalization to novel chemical scaffolds. Each experiment repeated across three random seeds, and we report the mean and standard deviation. A detailed dataset description can be found in Appendix
Table 1: Performance comparison on molecule property prediction. We present the ROC-AUC(SA, scores the molecular property prediction task on MoleculeNet. For baselines that report results, we directly use their reported outcomes. Note that MoICA-SMILEN does not report results for the MUV and HIV datasets. The best results are marked in bold, and the second-best are underlined.
Method
BBBP
Tox21
ToxCast
ClurTox
MUV
Avg
MOLFORMER
70.74&1.3
74.7420.56
(5.5140.63 63
61.75+1 23
77.6440.98
67,58=1.01
75.64t1.77
78.6462.35
71.53
KV-PLM
70.50M0.54
72. 12s1.00
55.00 55.03g1.60 65
59.83
89. 89.1742.75
54.63e4.81
65.40=1.69
75.80s2.73
67.81
MeysMoBART
68.5910.17 17
73.8910.67
63 C0.3210.79 79
59 12.1.9
78.1211162
61.5112.75
71.0111.70
82 $2/1610.84
69.81
MokculeSTM--N
70.7511.00
75.7110.99
65.1710.35
63.701081
65,6911
77.0210.44
81 $1.9990.41
MolFM
72
77.2000.70
40.40H0220 20
64.20100900
76-00=110
76.00H0
78 Mi- 1110
83.90=1.10 110
74.64
MoMu
70.5012.00 00
75.6010030 30
63. 1010..0
60.5010.90
79.90111.1 10
70.5011 /10
75.9010.80
76.7012.10 10
71.63
Atomas
73.72x1.65 67
77.8810.35 36
66 94.00.0
64.4011 90
93. 23.1610.35
76.3010.70
50.5510.43
33.14411.1
77.01
MolCA- SMILES
70,0000.0
76,000).5
56
61.10e1
39.00111.70 1.70
TRIDENT (N--)
73.1440.44
7X 2340..111 12
67.79H0.56
95.75 64.62:0.47
82 88-8 441
79.64 09.6441177
84.19=0.95
78.78
TRIDENT (M-M)
73.95+1.01
79.36+0.13
67.80+0937
63. 63.6441155
95.41+0.66
83.51+0.88
81.6314052
82.39+0.56
78.46

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Table 2: Performance of different methods on DILI, Carcinogens, and Skin Reaction tasks, reporting AUC and Accuracy. The best results are marked in bold, and the second-best are underlined.
DILI (475 drugs)
Carcinogens (278 drugs)
Skin Reaction (404 drugs)
Method
AUC
ACC
AUC
ACC
AUC
ACC
MOLFORMER
85.59+1.39
76.3945.24
77.27=0.76
77.32+1.47
63.75=1.41
60.9843.4 44
KV-PLM
73.46=0.61
62.50+2.08
75.18=3.71
76.01
62.88+2.30
59. 59.7645.1 17
MofTS
77.37=1.15
69.44+1.20
86,89=1.00
84.45+1.11
68.67+3.99
62.22+1.41
MoMu
80,44+2.47
75.00+4.17
80.11
78.00+2.62
61.63+1.94
56.1043.45
MoICA-SMILES
SMILES
88.34+1.28
80.56+2.40
82,00+1.80
78.7640.52
65.13+0.88
62.20+1.72
MolsculSTTM-SMIIFSS
91.20+2.02
84.72+2.41
83.87+1.30
81.0540.63
67.72+0.50
61.60#0.73
MolXPT
91.67+0:76
84.03+3.19
75.76+2.73
80,90+2.06
61.08+1.28
62. 62.60+1.44
BioT5
82,45+1.81
76.39+3.18
82.83+4.31
76.19+2.06
68,27=4.41
62.21=1.06
BioT5+
82.58+1.65
80,56
86.62+2.32
77.41+2.00
65.25=0.66
62.27=1
Atomas
90.17=1.30
85 26612.11
82.4742.11
80.7540.50
70.33+0.88
61.7946.14
TRIDENT (M-S)
95.08%0..0
86.81
83.42+1.10
81.47=0.92
70.33+0.66
63.42+4.22
TRIDENT (M-M)
94,56+0088
86.80+3.18 188
87.07=0.77
84.62+1.07
72.00=1.09
62.60+1.40
Baselines. We compare our TRIDENT approach against a range of recent state-of-the-art base- lines. These include transformer-basee models that use SMILES representations, such as MOL.- FORMER [31, MegaMolBART 29. MolXPT [8] BioTS 27 and BioT5+ (26) We also com-
pare against multimodal approaches incorporating additional textual and molecular information, including Mo1FM[21], MoMu (35). MoleculeSTM [16), MoICA-SMILES [1). KV-PLM [39] and Atomas [4]. Additionally, we compare with Uni-Mol [42), which employs 3D molecular confor- mations for representation learning. A detailed baseline introduction can be found in Appendix
4.1 Results and Analysis
Molecular property prediction. As shown in Table [ our TRIDENT model achieves state-of- the-art performance across the diverse set of MolcculeNet tasks, consistently outperforming prior methods. We evaluate two encoder configurations: TRIDENT (M-S), which uses MOLFORMER (3I as the SMILES encoder and SciBERT [2] as the text encoder, and TRIDENT (M-M), which combines MOLFORMER with MolT5 as text encoder. On average, TRIDENT (M-M) achieves the highest ROC-AUC score of 78.5%, outperforming strong baselines such as Atomas (77.01 1) and MolFM (74.62%). It achieves best-in-class performance on of the tasks, including challenging bench- marks such as BBBP, Tox-21, Toxcast, MUV, and HIV. The M-S variant is also highly competitive, outperforming nearly all baselines and obtaining the top score on Bace, Sider, and ClinTox.
Furthermore, we evaluate our method on MoleculeNet regression tasks, as shown in Appendix E TRIDENT (M-M) consistently demonstrates superior performance, achieving the best competitive RMSE scores across all three regression datasets. These results further validate the effectiveness of our multimodal learning approach.
One reason TRIDENT performs best is that most prior methods rely solely on generic textual descriptions of molecular function, lacking the by our HTA dataset. Furthermore, the vast majority of approaches overlook the importance of local alignment between molecular subgraphs and their corresponding textual fragments. While Atomas does introduce a local alignment component via attention, that static attention scheme cannot dynamically balance the competing demands of global context and fine-grained substructure matching throughout training. In our framework, we integrate a momentum-based alignment mechanism that
hierarchical annotations provided
Table 3: Performance comparison of molecular property prediction methods based on different input modalities (SMILES, Text, and HTA) across various datasets (UOC- Best results bold.
Method
Input
Datasets
SMILES
Text
HTA
BBBP
Tox21
ToxCast
Sider
Bace
TRIDENT (M-M) TRIDENT (M-M)
72.02=0.36
78.21=0.19 19 67 07.0444..8
63. 1840.31 $1.2840.92 63.6420.56
73.95=1.01
79.3620.13
67.80=0.37
82.39:0.56
8

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Tos21
ToxCast
SeSP
Sance
612-
4379
414-
4794
eon maat aiparent
with imaa aignoeet
PrR
Thazetrent
wit boal aignomnt
not toal algemard
eis selorm
Hin rofemm
polumee
Figure 3: The ablation experiments are conducted on the Tox21, ToxCast, BBBP and Bace datasets. "w/o HTA" denotes that only not use hierarchical taxonomic annotation; "w/o local alignment" denotes that the local alignment is removed; and "w/o volume loss" indicates that only the volume- based loss is changed to the standard contrastive loss.
adaptively reweights global and local objectives. Our experiments show that combining HTA with this dynamic balancing of global and local alignment yields substantial improvements across wide array of molecular property prediction tasks.
To further validate the effectiveness of our model, we select three small datasets from TDC. This choice is motivated by the challenging nature of data acquisition for toxicity, making small datasets more reflective of the model's ability to generalize and perform well in scenarios with limited data. By evaluating on these data-scarce tasks, we aim demonstrate the robustness and adaptability of our approach in real-world settings. As shown in Table 9 TRIDENT again achieves new state-of-the-arr results across all three datasets. TRIDENT (M-S) obtains the highest AUC and accuracy scores on DILI, alongside strong performance Carcinogens and Skin Reaction. The TRIDENT (M-M) variant further improves AUC on Carcinogens and Skin Reaction, outperforming all baselines. Notably, TRIDENT excels not only on these smaller datasets but also demonstrates superior performance on larger-scale benchmarks, including AMES (7,255 drugs), CYP P450 2C19 (12.665 drugs), and the regression dataset Caco-2 (906 drugs) (see Appendix E.. These results highlight the broad applicability of TRIDENT across diverse molecular property prediction tasks, ranging from data-scarce to data-rich settings.
4.2
Ablation Study
To understand the contribution of different components in our TRIDENT framework, conduct detailed ablation study. We compare several model variants on representative tasks to disentangle the impact local functional group and sub-textual description alignment loss, hierarchical taxonomic supervision as well as the volume loss for global alignment.
As shown in Figure removing HTA infor- mation (w/o HTA) leads to a noticeable drop in model performance, highlighting the impor- tance of HTA in capturing a rich, multi-level understanding of molecular behavior through hierarchical taxonomy annotations. Similarly, excluding the local-alignment component (w/o local alignment) results in a clear performance decline, showing how fine- eneeggrinedd alignment plays a critical role in enhancing the model's capability. Interestingly, replacing our volume loss with standard contrastive loss (w/o volume loss) causes significant instability on datasets like Tox21, ToxCast, and BBBP. This is likely because traditional alignment approaches struggle to handle multiple modalities effectively (5). addition, our momentum- -basd mechanism further strengthens generalization by dynamically balancing global and local objectives during training, as demonstrated Table Overall, the full TRIDENT framework consistently outperforms all ablated versions, confirming the value and necessity of each individual component.
Table Strategies for combining global and local loss functions (ROC-AUC%). Sum: direct addi- tion; Curve: segmoid-weighted combination with increasing local loss weight; Momentum: dynamic alignment approach. Best results bold.
Method
Tox21
ToxCast
BBBP
Bace
77.79.0.3 81 66.7349.65
72.1520.81
81.4240.60
Curve 76.6840.79
65.4940.82
71.08+0.79
80.91
Momentum
79.3620.13
67.50+0.37
73.95=1.01
82.3920.56

---

In addition, to further explore the relationship between HTA and general molecular descriptions, we directly use HTA and molecular SMILES as inputs for the global module during pretraining, replacing the volume loss with standard contrastive learning loss while keeping other settings unchanged. The results are shown in Table S When using HTAA as the sole text input, the model already outperforms most baselines but still falls short of the tri-modal input. This may be because HTA text and traditional molecular descriptions complement each other terms of information representation. HTA text contains up to 32 categorical annotations, providing more diverse and multi-angled molecular information, while traditional functional descriptions are more direct and highlight the core features of molecular structures. Therefore, by simultaneously leveraging HTA text and traditional descriptions as multimodal inputs, the model captures molecular characteristics more comprehensively, thereby further improving its performance.
Conclusion
TRIDENT is tri-modal molecular representation framework that unifies chemical SMILES, natural- language descriptions, and hierarchical taxonomic annotations into single, semantically rich em- bedding space. Trained on over 47,269 <SMILES, Text, HTA> triplets, uses geometry-aware volume-based contrastive loss for global alignment and a local contrastive module for precise substruc- tuxe-text matching. overeoming modality misalignment and flat representations. A momentum- based weighting scheme balances global and local objectives, delivering state- state-of-the-ar performance on 11
benchmarks without altering the architecture. These results highlight the power of structurally and semantically grounded multimodal alignment in molecular learning. More broadly, this work underscores the importance of hierarchical, multi-resolution reasoning in molecular modeling and opens new directions for scalable, and biologically meaningful representation learning in the chemical sciences. One limitation of our work is that molecular properties such as toxicity depend not only molecular structure but also targets and metabolites, which are not currently captured and slated for future research.
6
Acknowledgments
This work was partially supported by a Johnson & Johnson grant for LLM-based toxicity prediction, US National Science Foundation IIS-2412195, CCF-2400785, the Cancer Prevention and Research Institute of Texas (CPRIT) award (RP230363), the National Institutes of Health (NIH) RO1 award (1R01 A11900030)))) and Microsoft Accelerate Foundation Models Research (2024).
References
[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023.
[2] Iz Beltagy, Kyle Lo, and Arman Cohan. Scibert: A pretrained language model for scientific text. arXiv preprint arXiv: 10676, 2019.
[3] Sihan Chen, Handong Li, Qunbo Wang, Zijia Zhao, Mingzhen Sun, Xinxin Zhu, and Jing Liu. Vast: A vision-audio-suuritleee omni-modality foundation model and dataset. Advances in Neural Information Prucessing Systems, 36:72842-72866, 2023.
[4] Siwar Chibani and Slançois-Xaviee Coudert. Machine learning approaches for the prediction of materials properties. Apl Materials, 8(8), 2020.
[5] Giordano Cicchetti, Eleonora Grassucci, Luigi Sigillo, and Danilo Comminiello. Gramian multimodal representation learning and alignment. arXiv preprint arXiv:2412.11950 2024.
[6] Thao Dang, Haiqing Li, Yuzhi Guo, Hehuan Ma, Feng Jiang, Yuwei Miao, Qifeng Zhou, Jean Gao, and Junzhou Huang. Hage: Hierarchical alignment gene-enhanced pathology representation learning with spatial transcriptomics. In International Conference on Medical Image Computing and Computer- Assisted Intervention, pages 228-238 Springer, 2025.
10

---

[7] Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji. Translation between molecules and natural language. arXiv preprint arXiv:2204. 11807, 2022.
[8] Benjamin Elizalde, Soham Deshmukh, Mahmoud Ismail, and Huaming Wang. Clap learning audio concepts from natural language supervision. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2023.
[9] Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15180-15190, 2023.
[10] Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik. Therapeutics data commons: Machine learn- ing datasets and tasks for drug discovery and development. arXiv preprint arXiv:2102.099 2021.
[11] Feng Jiang, Yuzhi Guo, Hehuan Ma, Saiyang Na, Weizhi An, Bing Song, Yi Han, Jean Gao, Tao Wang, and Junzhou Huang. Alphaepi: Enhancing b cell epitope prediction with alphafold 3. In Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pages 1-8, 2024.
[12] Feng Jiang, Yuzhi Guo, Hehuan Ma, Saiyang Na. Wenliang Zhong, Yi Han, Tao Wang, and Junzhou Huang. Gte: a graph learning framework for prediction of t-cell receptors and epitopes binding specificity. Briefings in Bioinformatics, 25(4):bbae343, 2024.
[13] Sunghwan Kim, Paul A Thiessen, Evan E Bolton, Jie Chen, Gang Fu, Asta Gindulyte, Lianyi Han, Jane He, Siqian He, Benjamin A Shoemaker, et al. Pubchem substance and compound databases. Nucleic acids research, 44(D1):D1202-D112
2016.
[14] Carolyn E Lipscomb. Medical subject headings (mesh). Bulletin of the Medical Library Association, 88(3):265, 2000.
[15] Shengchao Liu, Hongyu Guo, and Jian Tang. Molecular geometry pretraining with se (3)- invariant denoising distance matching. arXiv preprint 1xX202 2022.
[16] Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, and Animashree Anandkumar. Multi-modal molecule structure-text model for text-based retrieval and editing. Nature Machine Intelligence. 1(12):1447-1457, 2023.
[17] Ye Liu, Siyuan Li, Yang Wu, Chang-Wen Chen, Ying Shan, and Xiaohu Qie. Umt: Unified multi- modal transformers for joint video moment retrieval and highlight detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3042-3051, 2022.
[18] Zequn Liu, Wei Zhang, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Ming Zhang, and Tie- Yan Liu. Molxpt: Wrapping molecules with text for generative pre-training. arXiv preprint arXiv:2305. 10688, 2023.
[19] Zhiyuan Liu, Sihang Li, Yanchen Luo, Hao Fei, Yixin Cao, Kenji Kawaguchi, Xiang Wang, and Tat-Seng Chua. Molca: Molecular graph-language modeling with gross-modal projector and und-l adapter. arXiv preprint arXiv:2310. 127.9998,, 2023.
[20] Huaishao Luo, Lei Ji, Ming Zhong, Yang Chen, Wen Lei, Nan Duan, and Tianrui Li. Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning. Neurocomputing. 508:293-300 304, 2022.
[21] Yizhen Luo, Kai Yang. Massimo Hong, Xing Yi Liu, and Zaiqing Nie. Molfm: A multimodal molecular foundation model. arXiv preprint arXiv.2307.0948. 2023.
[22] Hehuan Ma, Feng Jiang, Yu Rong, Yuzhi Guo, and Junzhou Huang. Robust self-training strategy for various molecular biology prediction tasks. In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pages 1-5, 2022.
11

---

[23] Hehuan Ma. Feng Jiang, Yu Rong, Yuzhi Guo, and Junzhou Huang. Toward robust self-training paradigm for molecular prediction tasks. Journal of Computational Biology, 31(3):213-225, 2024.
[24] Yuwei Miao. Yuzhi Guo, Hehuan Ma, Jingquan Yan, Feng Jiang, Rui Liao, and Junzhou Huang. Gobert: Gene ontology graph informed bert for universal gene function prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 622-630, 2025.
[25] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730-27 27744, 2022.
[26] Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, and Rui Yan. BiotS+: Towards generalized biological understanding with iupac integration and multi-task tuning. arXiv preprint 2r02i:2202..1 17810, 2024.
[27] Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, and Rui Yan. Biot5: Enriching cross-modal integration in biology with chemical knowledge and natural language associations. arXiv preprint arXiv:2310.0722 2023.
[28] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PmLR, 2021.
[29] Danny Reidenbach, Micha Livne, Rajesh K Ilango, Michelle Gill, and Johnny Israeli. Improving small molecule generation using mutual information machine. arXiv preprint arXiv:2208. 09016, 2022.
[30] Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. Self-supervised graph transformer on large-scale molecular data. Advances in neural information processing systems, 33: 12559-1217,,,, 2020.
[31] Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan Inkit Padhi, Youssef Mroueh, and Payel Das. Large-scale chemical language representations capture molecular structure and properties. Nature Machine Intelligence, 4(12): 2022.
[32] Ludan Ruan, Anwen Hu, Yuqing Song, Liang Zhang, Sipeng Zheng, and Qin Jin. Accommodat- ing audio modality in clip for multimodal processing. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 9641-9649, 2023.
[33] Adriano Rutz, Maria Sorokina, Jakub Galgonek. Daniel Mictchen, Egon Willighagen, Arnaud Gaudry, James G Graham, Ralf Stephan, Roderic Page, Jiří Vondrášek, et al. The lotus initiative for open knowledge management in natural products research. elife, 11:770780, 2022.
[34] Jie Shen and Christos A Nicolaou. Molecular property prediction: recent trends in the era of artificial intelligence. Drug Discovery Today: Technologies, 32:29-36, 2019.
[35] Bing Su, Dazhao Du, Zhao Yang, Yujie Zhou, Jiangmeng Li, Anyi Rao, Hao Sun, Zhiwu Lu, and Ji-Rong Wen. A molecular multimodal foundation model associating molecule graphs with natural language. arXiv preprint arXiv:2209.05488 2022.
[36] Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani. Molecular contrastive learning of representations via graph neural networks. Nature Machine Intelligence, 4(3):279- 287, 2022.
[37] Zhenqin WU, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. Moleculenet: a benchmark for molecular machine learning. Chemical science, 9():513-530, 2018.
12

---

[38] Haiyang Xu, Qinghao Ye, Ming Yan, Yaya Shi, Jiabo Ye, Yuanhong Xu, Chenliang Li, Bin Bi. Qi Qian, Wei Wang, al. mplug-2: A modularized multi-modal foundation model across text, image and video. In International Conference on Machine Learning, pages 38728-38748. PMIR, 2023.
[39] Zheni Zeng, Yuan Yao, Zhiyuan Liu, and Maosong Sun. A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals. Nature communications, 13(1):862, 2022.
[40] Renrui Zhang, Ziyu Guo, Wei Zhang, Kunchang Li. Xupeng Miao, Bin Cui, Yu Qiao, Peng Gao, and Hongsheng Li. Pointelip: Point cloud understanding by clip. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8552-8562, 2022.
[41] Yikun Zhang, Geyan Ye, Chaohao Yuan, Bo Han, Long- Kai Huang, Jianhua Yao, Wei Liu, and Yu Rong. Atomas: Hierarchical adaptive alignment on molecule-text for unified molecule understanding and generation. In The Thirteenth International Conference on Learning Repre- sentations, 2025.
[42] Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke. Uni-mol: A universal 3d molecular representation learning framework. 2023.
[43] Bin Zhu, Bin Lin, Munan Ning, Yang Yan, Jiaxi Cui, HongFa Wang. Yatian Pang, Wenhao Jiang, Junwu Zhang, Zongwei Li, et al. Languagebind: Extending video-language pretraining to n-modality by language-based semantic alignment, arXiv preprint arXiv:2310.01852 2023,
13

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Appendix
A
Data Collection and Processing
We obtain a dataset containing 320,000 molecule-text pairs from the PubChem database and prepro- cess the text descriptions following the MolecularSTM method. Specifically, molecule names are replaced with "this molecule is..." or "these molecules are..." to prevent the model from recognizing molecules based solely on their names. Additionally, to create unique SMILES-text pairs, we merge molecules with the same CID (chemical identifier) and filter out text descriptions with fewer than 18 characters.
Moreover, we use PubChem's classification system to obtain up to 32 classification descriptions for each molecule, as illustrated in Algorithm I Ultimately, we generate 47,269 <SMILES, Text, Hierarchical Taxonomic Annotation> triplets. shown in Figure 4 to further optimize and summa- rize the classification annotations, we use GPT-4 to generate summarized descriptions, resulting in high-quality HTA descriptions.
You are a chemical classification assistant. Your task to interpret complex chemical classification data and SMILES representations to provide concise, accurate summaries that explain what the compound is and its key artributes.
Based the following classification information, provide concise summary what this compound its key characteristics, Taxonomic Annotation. Provide summary 3-7 sentences, focusing what this compound key attributes.
Classification information: "MeSH Tree": "ppath: "root Chemicals and Drugs Category > Organic Chemicals Hydrocarbons > Terpenes > Monoterpenes > Acyclic
Monoterpenes > Sapplementary Records > citronellol", "description" " "alcohol form of citronellul; found in rose oil; RN given refers parent epd without isomeric designation; structure" "LOTUS Tree": "path": "root > Riological Tree Eukaryota Archaeplastida Streptophytu Magnoliopsida Myrtales Myrtaceae Eucalypteae Fucalyptus Eucalyptus astringens", "description" " .......
Citronellol is an acyclic monoterpene alcohol derived from citronellal, commonly found in rose oil. It is characterized by its chemical structure as oct-6-ene with a hydroxy group at position and methyl groups al positions 3 and 7. Citronellol is widely used fragrance various products and has been identified as part of the blood exposome. It also noted for causing skin irritation. terms of biological taxonomy, associated with the plant Eucalyptus astringens. The compound actively used in commercial applications and regulated by environmental protection agencies.
Figure The workflow for summarizing Hierarchical Taxonomic Annotations (HTA). Using GPT-4o, detailed classification annotations are processed and summarized, resulting in high-quality HTA text descriptions for molecular data.
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Algorithm 1: Hierarchical Taxonomic Annotation: retrieval of molecule classification from PubChem.
Input preparation
Load CID list
(from CIDs.txt file) [batch_ size 20)
Configure batch size
Batch processing setup
Thread pool executor
max workers 5
Retry mechanism
max retries = 3, backoff factor 2] (0.5s between API calls, 3s batches]
Rate limiting
API interaction
Classification headers retrieval
Endpoint (PubChem /pug http://ccooooooooodddd////////aa
Output
(list of classification systems with HIDs]
12
Classification path retrieval
Endpoint (PubChem /classification trcpaattioon__ffe 2.fcgi/hid- (hid)& Path construction Output
14
(recursive traversal of parent-child nodes)
15
hierarchical path string, description)
16
Result processing
17
Data structure
(CID - (classification __ssttmsssmm___ - (path, description)) )
Intermediate saving
[save after each batch for resumability) (log warnings and errors, continue processing)
19
Error handling
20
Output
JSON file
complete taxonomy annotation for all CIDs)
B Experimental Setup
B.1 Model Architecture
As shown in Algorithm our multimodal contrastive learning framework consists of the following key components:
1. Three-modal encoding:
Mol.Former processes SMILES structures, while SciBERT en- codes molecular text descriptions and category information, outputting di8-dimensional features.
2. Feature projection: Multi-layer MLPs project features from each modality into a 512- dimensional shared space with L2 normalization.
3. Two-level contrastive learning:
Global contrast: Applies GRAMBModal method to calculate volume loss and InfoNCE loss across three modalities.
Local contrast: Aligns SMILES and text representations at the functional group level
4. Dynamic loss integration: Employs a momentum update mechanism (5 = 0.9) adap- tively adjust weights between global and local losses, with total loss L = a Lgioball + (1 a:)
Licul
The model is implemented in distributed training environment, freezing pre-trained encoders and optimizing only projection layer parameters.
B.2 Training Configuration
Our multimodal contrastive learning model trained on two NVIDIA H100 GPUs with the following configuration, as shown in Table
Each training epoch takes approximately 5 minutes. During training, we used DistributedSamplr to ensure consistent data distribution across different GPUs and shuffled the data by setting different random seeds the beginning of each epoch. Due to the large size of MoLFormer and SciBERT
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Algorithm 2: MultiModal Contrastive Learning: three-modal alignment with momentum integra-
tion.
Input modality encoders
SMILES encoder
(Momerrmer (768-dim))
3 Text description encoder
[SciBERT (768-dim) (shared with text encoder (768-dim))
Category encoder
Projection layers
SMILES projection
(512-dim))
7
Text projection
(512-dim))
Global contrastive loss (GRAM3Modal) (determinant of Gram matrix)
Volume computation
10
Temperature scaling
(T= 1 0.07]
Volume-based alignment
(cross entropy negative volumes)
12
InfoNCE alignment
standard contrastive across modalities)
13
Local functional group alignment
Functional group detection
(RDKit Fragments]
15
FG representation
weighted pooling of fragment embeddings local InfoNCE between SMILES and text)
FG contrastive loss
Momentum- based loss integration
18
Momentum coefficient
13 0.9)
Initial alpha
{a 0.5)
20
Dynamic update
(a = B arre - 3) - (global Llsssl_ossssttoo llosllssssssstt total _oss]]
21
Training configuration
Optimization
AAdm (Ir=le-5) (both text and SMILES encoders) (DDP, NCCL backend)
23
Encoder freezing
Distributed training
Batch size 25
(40 per GPU, multi-GPU)
models, we adopted strategy of freezing pre-trained encoder parameters and only training projection layer parameters, which significantly reduced computation and memory requirements while main- taining model expressiveness. We observe that the dynamic integration of global and local losses (dynamic adjustment of a value) demonstrates good adaptability during the training process, enabling reasonable balancing of the contributions from the two losses different training stages.
B.3 Evaluation Metrics
To comprehensively evaluate the performance of our multimodal contrastive learning model molec- ular property prediction tasks, we adopt appropriate evaluation metrics based on the characteristics of different datasets.
B.3.1 MoleculeNet Datasets
For binary classification tasks in MoleculeNet datasets, we employ ROC-AUC (Receiver Operating Characteristic Area Under Curve) and standard deviation as the primary evaluation metric. The ROC-AUC calculated follows:
AUC = TPR (t)) dt
(9)
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Table 5: Training Configuration Details
Parameter
Configuration
Hardware environment
x NVIDIA HI00 GPU
Training framework
PyTorch DistributedDataaarrallll (DDP)
Communication backend
NCCL
Optimizer
Adam
Learning rate
le-5
Batch size
40 per GPU (total batch size = 80)
Weight decay
le-4
Training epochs
60 epochs
Training dataset size
47,269 molecule-text-HTA pairs
Gradient accumulation steps
Learning rate schedule
Fixed learning rate, no decay
Early stopping
Stop after 5 epochs without validation loss improvement
Loss Function Configuration
Contrastive temperature
0.07
Momentum coefficient
B = 0.9
Initial loss weight
a 0.3
Global loss composition
GRAM3Modal volume loss + InfoNCE loss
Local loss composition
Functional group level InfoNCE loss
Label smoothing parameter
0.1
Model Configuration
Modality encoders
Frozen (feature extraction only) Fully fine-tuned (768-dim 512-dim)
Projection layers
Dropout rate
0.1
Gradient clipping
Max norm 1.0
Mixed precision training
FP16
Checkpoint saving frequency
Every epochs
where the True Positive Rate (TPR) and False Positive Rate (FPR) are defined as:
TP
TP + FN TPR
(10)
FPR
(11)
FP + TN
ROC-AUC values range from 0 to with values closer indicating better model performance. This metric demonstrates good robustness to class imbalance issues, making it particularly suitable molecular property prediction tasks the pharmaceutical domain where positive and negative samples often unevenly distributed.
STD
[; -)
(12)
where n is the number of experiments, 4, result of the i-th experiment, and T is the mean of experiments. The standard deviation reflects the stability and reliability of model performance, with smaller standard deviations indicating more stable performance across different data splits and random seeds.
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Table 6: MoleculeNet Datasets Details
Dataset
Sample Size
Prodiction Task
Task Deseription
BUBP
2,500
Blood Beain Barrier Penetration
Predicts whether
bloed briin burler
Tox21
Fualutes componnd activity 12 different pathways compound jusicity
ToxCast
Prediction
biological asays effects
SIDER
1,427
Side Predaction
Prodicts adverse drug reactions covering types
Clin
1,433
Clinical Toxicity
Evuluases clinical toxicity und FDV approval status of compounds
MUY
93.087
Riological Activity
Molocular activity prodiction with 17 highly imhalanced biological tagets Prodicts compound inhibriion of IIIV replication
ITV
41,127
Amivinal Activily
BACE
1,513
Enzyme Inhibition
Prodicts 3 s-serrttase inhibitor octivity for Altheimer's is discase drug discovery
ESOL
1,127
Water Solubility
Prodicts solbbility dog S), fundamental for drug formulation and delivery
FreeSalv
Solvation Free Energy
Profiets hydlution free energy. important for understanding molocular intenctions octanol water purttion coefficient (log D), for drug abscrption distribution
Lipophilicity
Lipophilicity
Table 7: TDC Datasets Details
Sample
Prediction
Description
DII
iver fnjury Prodiction
Puslics drg--indecl of liver injury, criiical convidenaionn developmert
Cancirogens
Carcinngenicctyy Prediction
Pralices componnd cacinogaticity reactions, important topical drug development
drgg chemical
Skia
Prediction
whether compounés
7,255
Mittaenicity
compound muttgenicity
genetic tedicity
Curdionodictty Prediction
Prodices componnd blocking activity against potusium
majer cardiosoniiity
2C19
12,665
Drug Mesabolism Prediction
Predices inhibotion CYP2C19 enzyme. essental asessing drug drug inseractions
Caco-2
Penncabdiit Prediction
Prodiess intestiaal permesbility using Caco-2 cell line, enttial ceal drug bioovallabliit
B.3.2 TDC Datasets
For TDC (Therapeutics Data Commons) datasets, we employ both ROC-AUC and Accuracy as evaluation metrics:
1. ROC-AUC: Same definition as in MoleculeNet datasets, used to measure the model's classification performance and discriminative ability.
2. Accuracy: The accuracy is calculated as:
TP + TN
Accuracy TP + IN + FP + FN
(13)
where TP, TN, FP, and FN represent the number of true positives, true negatives, false positives, and false negatives, respectively.
Accuracy intuitively reflects the proportion of correctly predicted samples by the model. When used in combination with ROC-AUC, it provides more comprehensive evaluation of model performance. ROC-AUC primarily focuses on the model's 's ranking ability and threshold-indepenee performance, while accuracy directly reflects the model's classification effectiveness under specific thresholds.
C Downstream Tasks Datasets
To comprchensively evaluate the performance of our proposed multimodal contrastive learning framework molecular property prediction tasks, conduct extensive experiments on two major benchmark dataset collections: MoleculeNet and TDC (Therapeutics Data Commons).
C.1 MoleculeNet Datasets
MoleculeNet is one of the most authoritative benchmark dataset collections in the field of molecular machine learning, specifically designed evaluate the performance molecular property prediction methods. Table @ summarizes the detailed information of the 8 MoleculeNet datasets we used.
C.2
TDC Datasets
TDC (Therapeutics Data Commons) is a large-scale dataset collection specifically designed for thera- peutics research, providing more challenging and practically valuable molecular property prediction tasks. Table 7 presents the detailed information of the 7 TDC datasets selected.
These datasets cover key property prediction tasks in the drug discovery process, including pharma- cokinetics (ADME), toxicity, and biological activity across multiple aspects. Both dataset collections are characterized by diversity, challenging nature, standardization, and authority, and are widely recognized and used by both academia and industry.
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D Baselines
In this section, provide descriptions of the baseline methods used for comparison in periments. These baselines represent current approaches molecular representation learning and multimodal molecular modeling.
D.I Single-Modal Baselines
MOLFORMER: A transformer-basee based model that processes SMILES string representations using masked language modeling. The model employs linear attention with rotary positional embeddings and is pre-trained 1.1 billion molecules from PubChem and ZINC databases in an unsupervised fashion. MegaMolBART: BART-based encoder-decoder model adapted for molecular data. processes SMILES representations and applies bidirectional and auto-regressive transformers for molecular understanding and generation tasks.
D.2 Multimodal Baselines
MoleculeSTM: A bi-modal model with separate encoders for molecular structures (gapps)/ and textual descriptions. It uses contrastive learning to align structure-text pairs and is trained on over 280,000 molecule-text pairs from PubChem. MoMu: A multimodal foundation model that uses separate encoders for molecular graphs and natural language text. The model employs contrastive learning to bridge molecular structures with textual descriptions using paired molecule-text datasets. MolFM: tri-modal model that integrates molecular structures (2D graphs), biomedical texts, and knowledge graphs. It uses cross-modal attention mechanisms and is pre-trained with four objectives: structure-text contrastive learning. cross-modal matching, masked language modeling, and knowledge graph embedding. KV-PLM: A BERT-based unified framework that processes both SMILES-encoder molecular structures and natural language text through masked language modeling pre-training. The system enables cross-modal understanding between molecular structures and biomedical text. MoICA-SMILES: A molecular cross-modal projector to bridge graph encoders and language models. The approach employs LoRA adapters and follows a three-stage training pipeline for efficient fine-tuning. Atomas: A hierarchical alignment framework that introduces Adaptive Polymerization Module (APM) and Weighted Alignment Module (WAM) to learn fine-grained correspondences between SMILES and text at atom, fragment, and molecule levels. uses a unified encoder and end-to-end training for joint alignment and generation.
model that uses a Q-Former
D.3 Comparison with TRIDENT
Our proposed TRIDENT framework differs from these baselines in several key aspects:
1. Hierarchical Taxonomic Annotations: Unlike existing methods that rely on generic textual
descriptions, TRIDENT incorporates structured, multi-level functional annotations across 32 taxonomic classification systems, providing richer semantic understanding.
2. Tri-modal Architecture: While most baselines focus on bi-modal alignment (structure- text). TRIDENT introduces novel tri tri-modal approach that jointly models SMILES, textual descriptions, and hierarchical taxonomic annotations.
3. Volume-based Global Alignment: Instead of traditional pairwise contrastive learning, TRIDENT employs a geometry-awand volume-basee based alignment objective that captures higher-order relationships across all three modalities simultaneously.
4. Local-Global Integration: TRIDENT uniquely combines global tri-modal alignment with fine-gra fine-grained local alignment between molecular substructures and their corresponding textual descriptions, balanced through a momentum-basee mechanism.
5. Dynamic Alignment Strategy: The momentum- based integration of global and local objectives allows TRIDENT adaptively focus on different alignment components during training, leading to more robust representation learning.
These innovations enable TRIDENT to achieve state-of-the-arr performance across 11 downstream molecular property prediction tasks, demonstrating the effectiveness our comprehensive multimodal approach.
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Table 8: Regression performance (RMSE) on MoleculeNet benchmark. Lower values indicate better performance. The best results are marked in bold, and the second-best are underlined.
Dataset
Uni-Mol
Bio'S
BioTI+
MolXPT
MolFormer
MolIs
TRIDENT(M-M)
ESOL
0.79 + 0.03
0.80 = 0.02
0.79 = 0.01
0.73 + 0.01
0.78 a 0.12
0.82 + 0.02
0.72 = 0.07
FreeSolv
1.48 0.05
1.63 a: 0.02
1.98 = 0.13
1.60 0.03
1.67 # 0.06
1.55 + 0.14
1.42 2 0.03
Lipophilicity
0.60 0.02
0.74 + 0.74 0.06
0.69 0.01
0.63 0.02
0.65 + 0.04
0.60
Table 9: Performance of different methods on AMES and hERG tasks, reporting AUC and Accuracy. The best results are marked in bold, and the second-bes best underlined.
Method
AMES (7,255 drugs)
hERG (648 drugs)
AUC
ACC
AUC
ACC
MOLFORMER
83.20+0.32
78.05+0.76
79.65=1.19
81,82+3.03
KV-PLM
78.23+0.90
71.70+0.94
75.87+2.76
75.30+3.08
MolTS
76.93+0.84
70.87+2.22
76.25=1.22
77.04+4.90
MoMu
77.20=0.85
70.78+0.36
75.68=1.89
73.27+3.55
MOICA-SMILES
77.62+1.49
71.74+1.07
78.40=1.84
73.94+4.38
MoleculeSTM-SMMLL
83.60+1.00
77.6820.64
79.4624.63
79.1914.94
MolXPT
76.93+0.84
70.87+2.22
82.44+2.14
81.31+2.31
BioT5
77.57=0.69
13.25+1.67
77.48+1.59
80.30+3.03
BioTS+
78. 78.1841.48
73.30#1.15
82.27+3.29
80.31
Atomas
82.63+0.72
77.32+0.83
83.34+1.79
78.0242.00
TRIDENT (M-S)
85.3740.30
78.7440.55
87.60#1.20
81.11+2.64
TRIDENT (M-M)
86.87=0.60
80.20=1.44
83.31
83.33+2.62
E
Additional Results
In this section, we present additional experimental results that complement the main findings reported in the paper. These include performance evaluations on MoleculeNet regression tasks, larger-scale datasets from the TDC benchmark, more extensive ablation experiments, and additional analyses that provide deeper insights into TRIDENT's capabilities.
E.1 Performance MoleculeNet Regression Tasks
To further demonstrate TRIDENT's effectiveness across different task types, we evaluate our method on three regression benchmarks from MoleculeNet: ESOL (water solubility). FreeSolv (solvation free energy), and Lipophilicity (octanol-water partition coefficient). As shown in Table TRIDENT (M-M) achieves the best performance on ESOL and FreeSolv, and matches the state-of-the-art performance on Lipophilicity. These results demonstrate that TRIDENT's multimodal learning framework effective not only for classification tasks but also for continuous property prediction.
E.2 Performance Larger TDC Datasets
While the main paper focused smaller TDC datasets to demonstrate TRIDENT's s data efficiency, we also evaluated our method on larger-scale molecular property prediction tasks. Table presents the results on the AMES mutagenicity dataset (7,255 molecules) and the hERG cardiotoxicity dataset (648 molecules). Additionally, Table I0 reports performance on the CYP P450 2C19 inhibition dataset (12,665 molecules) and the Caco-2 permeability regression dataset (906 molecules).
In summary, TRIDENT's superior performance across these diverse large-scale datasets from the moderately-sized hERG and Caco-2 to the large-scale AMES and CYP P450 2C19, demonstrates the versatility and scalability of our approach. The consistent improvements across different dataset sizes, ranging from hundreds to over ten thousand molecules, and across both classification and regression tasks, validate that the tri-modal alignment strategy and hierarchical taxonomic annotations provide robust molecular representations that generalize well. These results complement our findings on larger datasets and further establish TRIDENT as powerful framework for molecular property prediction across the full spectrum of practical applications in drug discovery.
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Table 10: Performance on larger-scale TDC datasets. For CYP P450 2C19, we report accuracy (ACC) and ROC-AUC. For Caco-2 regression task, we report RMSE (lower better).
Dataset
Metric
MolTs
MolXPT
BioT5
BioT5+
TRIDENT(M-M)
ACC
77.86 = 0.44
77.92 0.99
76.40 1.14
76.43 0.94
80.08 0.17
CYP P450 2C19
ROC-AUC
87.32 + 0.34
86.87 0.61
84.34
84.78 + 0.32
87,50 0.26
Caco-2
RMSE
0.44 = 0.01
0.48 = 0.03 0.57 = 0.03
0.60 0.05
0.41
Table 11: Ablation study on the impact of LLM-based summarization in HTA generation. Comparison between using raw JSON taxonomic annotations versus LLM-synthesized HTA descriptions across molecular property prediction datasets (ROC-AUC%). Best results bold.
Input
Datusets
Method
SMILES
Text
HTA
BBBP
Tox21
CaxCast
Sider
Bace
TRIDENT (M-M) w/o I.I. TRIDENT (M-M)
71.89+0.56
79.01#0.33
66.86.00.75 67.80=0.37
62.78+0.45
81.12+0.69
73.95&1001
79.36+0.13
63.6420.56
82.39+0.56
E.3 Performance without LLM Summary
To evaluate the contribution of LLM-based summarization in our HTA generation process, we conduct an ablation study comparing the performance of TRIDENT when using raw JSON taxo- nomic annotations versus LLM- LLM--yythesized HTA descriptions. In this experiment, directly input the structured JSON files containing hierarchical taxonomic paths and descriptions from the 32 classification systems, bypassing the GPT-4o summarization step described in Section 3.1.
The results in Table TT demonstrate the effectiveness of LLLM-based synthesis in our HTA generation pipeline. When using raw ISON taxonomic annotations without LLM summarization (TRIDENT w/o LLM), the model achieves competitive performance but consistently underperforms compared to the full TRIDENT framework across all datasets.
This performance gap highlights several key advantages of LLM-based summarization: (I) Infor- mation Integration: The LLM synthesis process effectively combines information from multiple taxonomic systems into coherent, contextually rich descriptions that capture cross-domain knowledge spanning chemistry, biology, and pharmacology. (2) Semantic Coherence: Raw JSON annotations often contain fragmented or inconsistent terminology across different classification systems, while LLM synthesis produces semantically coherent descriptions that are more amenable to natural lan- guage processing. (3) Contextual Enrichment: The synthesis process adds relevant contextual information and relationships between different taxonomic levels that may not be explicitly present in individual classification paths.
While the raw taxonomic annotations still provide valuable structural information that outperforms traditional text-only approaches. the LLM synthesis step proves crucial for maximizing the utility of hierarchical taxonomic knowledge in molecular representation learning. This finding validates our design choice incorporate GPT-4o the HTA generation pipeline and demonstrates that the additional computational cost of LLM synthesis is justified by the consistent performance improvements across all molecular property prediction tasks.
E.4 Impact of Tri-modal vs. Concatenated Text Architecture
To validate the necessity of our tri-modal architecture, conduct ablation study comparing approach with a simpler alternative that concatenates HTA and traditional text descriptions into a single textual input. This experiment evaluates whether treating HTA and text as separate modalities provides advantages over straightforward concatenation approach.
The results in Table [2] demonstrate the effectiveness of our tri-modal architecture over the concatena- tion approach. The concatenated version (TRIDENT Concatenated) combines HTA and traditional molecular descriptions into a single text input using simple string concatenation with separator tokens, then processes this unified text through the same text encoder used in our tri-modal framework. While this approach still benefits from the rich semantic information in HTA, it consistently underperforms
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Table 12: Ablation study comparing tri-modal architecture (SMILES + Text + HTA as separate modalities) versus concatenated text approach (SMILES + concatenated HTA@Tee Text as single text modality). The concatenated approach combines HTA and traditional molecular descriptions using string concatenation with separator tokens, while the tri-modal approach processes each information source through separate encoders with volume-basee alignment. Performance reported across
molecular property prediction datasets using ROC-AUC(()). Best results in bold.
Method
Datasess
Modalities
Text Processing
BRAP
Tox21
ToxCast
Sider
Race
TRIDENT (Concatenated) TRIDENT (M-M)
Tex Separate
70.918=0.82
76.67+0.59
SMILES + Text + HTA
73.95=1.01
79.36+0013
67 07.80.0.37
63.6420.56
82.39+0.56
the tri-modal architecture across all datasets. These consistent improvements highlight several key advantages of treating HTA and text as separate modalities:
Modality-Speciffe Representation Learning: The tri-modal architecture allows the model to learn distinct representation spaces for hierarchical taxonomic information and functional descriptions. This separation enables the capture of different semantic poscctts-xxooomice relationships in HTA versus direct functional properties in traditional text-tha may require different representational strategies.
Enhanced Alignment Flexibility: The volume-based tri-modal alignment objective can capture com- plex geometric relationships between SMILES, text, and HTA that are not accessible when HTA and text are merged into single modality. This geometric awareness enables more nuanced understanding of how molecular structure relates to both functional properties and taxonomic classifications.
Reduced Information Interference: Concatenation may lead to interference between the structured, multi-level taxonomic information and the more direct functional descriptions, potentially diluting the distinct contributions of each information source. Separate processing preserves the unique characteristics of each modality.
Dynamic Weighting Capabilities: The tri-modal framework allows for dynamic balancing of different information sources during training through momentum-based mechanism, whereas concatenation fixes the relative importance of HTA and text information at the input level.
These findings validate our design choice maintain HTA and traditional text as separate modali- ties, demonstrating that the additional architectural complexity tri-modal learning is justified by consistent performance gains across all molecular property prediction tasks.
29


Reviews:
[{"review_id": "NawEUT3Q8N", "reviewer": "Reviewer_TSPM", "summary": "TRIDENT introduces a novel trimodal molecular representation learning framework that integrates SMILES structures, textual descriptions and Hierarchical Taxonomic Annotations (HTA) to overcome limitations in existing methods. The framework employs a geometry aware volume based contrastive loss for global trimodal alignment and a local alignment module linking RDKit identified substructures to text spans, dynamically balanced via a momentum mechanism. The paper achieves state-of-the-art performance across 11 molecular property prediction benchmarks from MoleculeNet, establishing a new paradigm for structured and biologically meaningful molecular embeddings while releasing a comprehensive taxonomy annotated dataset for community use.", "questions": "- What is the computational overhead introduced by the volume-based contrastive loss and local alignment module? How does training time and memory usage compare to pairwise contrastive models like MoMu or MolFM?\n- Could you provide more detailed information about the prompting strategy, filtering criteria, and validation process used to synthesize the HTA texts with GPT-4o? Are the prompts and outputs deterministic or do they vary across runs? How were factual hallucinations and taxonomic inconsistencies addressed?", "limitations": "- The paper does not provide sufficient error analysis. Although results are strong, the paper lacks insight into when TRIDENT fails.\n- Including generalizability experiments will be helpful. Performance on non-taxonomically rich or non-biomedical datasets would test the universality of the method.\n- While the model performs well, there’s no qualitative or visualization-based analysis of the embedding space, cross-modal attention, or interpretability of predictions", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths\n- The framework unifies SMILES, text and hierarchical taxonomies (HTA) addressing a critical gap in molecular representation. The HTA’s multi-level annotations capture nuanced functional semantics beyond flat descriptions\n- The alignment mechanisms are novel. Volume-based contrastive loss enables geometry-aware trimodal alignment outperforming pairwise methods, local substructure-text alignment resolves granular correspondence and the momentum based balancing dynamically optimizes global vs. local objectives. \n- Ablation studies are thorough and clearly show performance drop when removing HTA, local alignment, or the volume loss\n\nWeaknesses\n- It's unclear how deterministic or reproducible this GPT-based synthesis is. GPT-generated text could introduce hallucinations or inconsistent phrasing, possibly leading to poor generalization or overfitting\n- The volume-based contrastive loss and functional group alignment introduce non-trivial computational overhead\n- The paper does not clearly explain the architecture of the encoders and projectors (e.g., how SciBERT or MolT5 is fine-tuned or frozen, the tokenization process for HTA).", "was_revised": true, "final_justification": "The paper is well detailed and the authors have well addressed the concerns raised in the initial review.", "rebuttal": "We thank the reviewer for their positive feedback and appreciating the novelty of our method. We address the valuable comments and questions raised by the reviewer point-by-point and will include them at appropriate places in camera-ready version.\n\n**1. Training time and memory usage:** We use pre-trained frozen encoders for SMILES, HTA, and functional descriptions, with only lightweight trainable projection layers (2.5 M parameters vs. 223 M in MoMu). This parameter-efficient design significantly reduces computational requirements for training and deployment. Under the same experimental settings, MoMu processes 32.5 samples per second while our method processes 16.4 samples per second on a single NVIDIA H100 GPU, with the difference primarily attributed to processing additional modalities (functional groups and HTA information). The total GPU memory usage is ~45 GB versus ~35 GB for MoMu, with the modest increase due to loading multiple frozen encoders and handling additional data modalities, but still easily affordable by most researchers.\n\n**2. Details on prompting strategy and validation process to synthesize HTA texts with GPT-4o:**\n\n*Prompting strategy:* The exact prompt template used is shown in Appendix A, Figure 4. This template explicitly instructs the model to summarize taxonomic annotations from PubChem without introducing additional information. All HTA summaries were generated using the same fixed prompt and model parameters (temperature=0.2, frequency_penalty = 0.0).\n\n*Filtering and validation:* Because GPT-4o was summarizing well-defined, scientifically validated PubChem Taxonomic Annotations, the risk of introducing extraneous information was minimized. Additionally, we conducted a validation step where domain experts independently reviewed a random sample of 1,000 HTA summaries, comparing them against the corresponding PubChem annotations. No factual hallucinations or taxonomic inconsistencies were observed in this review.\n\n**3. Error analysis (failure cases):** We thank the reviewer for highlighting this important point. While TRIDENT demonstrates strong overall performance, we note a performance decline on specific downstream tasks. For example, in the SIDER dataset, it achieves a ROC-AUC of 0.5904 on the *Product issues task*, which is notably lower than the 0.7383 ROC-AUC on the *Blood and Lymphatic System Disorders task*. A key factor is class imbalance in the former dataset, for which TRIDENT does not currently employ explicit rebalancing mechanisms. Addressing such imbalance-aware training strategies (e.g., loss reweighting or data augmentation) is a promising direction for future work to combine with TRIDENT.\n\n**4. Analysis of the embedding space:** We thank the reviewer for this suggestion. To analyze the embedding space, we calculated the average Euclidean distance between positive pairs across all modalities during pretraining. The distances between positive pairs across all modality combinations consistently decrease throughout training: SMILES-Text distances reduce from 0.7930 (Epoch 1) to 0.4805 (Epoch 40), SMILES-HTA distances decrease from 0.8269 to 0.4708, and Text-HTA distances drop from 0.9511 to 0.8698, with the overall average distance declining from 0.8570 to 0.6070. This systematic reduction in intra-sample cross-modal distances indicates that TRIDENT successfully learns to align semantically related representations across different modalities. We will include this analysis in camera-ready version. Given the rebuttal policy, we cannot include figures in this response but we will also include tSNE projections of embeddings and crossmodal attention heatmaps in the camera-ready version to further illustrate interpretability. \n\n**5. Performance on non-biomedical or non-taxonomic datasets:** We thank the reviewer for this valuable suggestion. Our work is primarily focused on molecular property prediction for drug discovery applications. Exploring non-biomedical datasets is an interesting future direction but is beyond the current scope. Notably, taxonomy information is used only during pre-training; all downstream molecular property prediction tasks rely solely on the SMILES encoder and projector, demonstrating that TRIDENT performs effectively even when taxonomic information is unavailable at inference time. \n\n**6. Architecture of encoders and projectors:** We thank the reviewer for the suggestion. Our model architecture and training configuration are described in Appendix B.2 and Algorithm 2 (Appendix page 20). Briefly, we use pre-trained MoLFormer for SMILES and MolT5/SciBERT for text (including HTA descriptions), both of which are frozen during training. We train only lightweight modality-specific projection heads (three linear layers with GELU, LayerNorm, and dropout) to map encoder outputs to a shared 512-dimensional space. For HTA and Text tokenization, we adopt the default SciBERT WordPiece vocabulary (scivocab) tokenizer, ensuring consistency with its pre-training. When using MolT5 as the HTA and Text encoder, we employ its pre-trained T5Tokenizer. This design minimizes computational overhead while retaining rich semantic representations."}, {"review_id": "NDP4ava1LH", "reviewer": "Reviewer_tPkn", "summary": "The paper presents TRIDENT, a novel framework for molecular property prediction that integrates three modalities: molecular SMILES, textual descriptions, and hierarchical taxonomic annotations (HTA). The authors introduce a geometry-aware volume-based contrastive loss for global alignment and a local alignment module that links molecular substructures to corresponding textual descriptions.", "questions": "- There are so many datasets in TDC; why did you choose these three?\n\n- A considerable portion of the paper is dedicated to explaining global tri-modal alignment, but there is less discussion on local alignment. Is there a detailed ablation study on local alignment?", "limitations": "see weakness", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n- TRIDENT's tri-modal approach is novel, combining SMILES, textual descriptions, and HTA for richer molecular representations. The use of HTA adds a structured, multi-level understanding that previous methods lacked.\n\n- The model outperforms several strong baselines on multiple molecular property prediction tasks.\n\n- The introduction of both global and local alignment strategies, balanced via a momentum-based mechanism, addresses the alignment challenges across different modalities effectively. \n \nWeakness\n\n- While the framework shows impressive results on smaller datasets, there is limited discussion on the scalability of the approach when applied to much larger molecular datasets and other downstream tasks.", "was_revised": true, "final_justification": "The construction and application of Hierarchical Taxonomic Annotations (HTA) are straightforward and have been shown to be useful across multiple datasets. The other reviewers' comments are generally positive as well, and no significant flaws have been identified.", "rebuttal": "We thank the reviewer for appreciating the novelty and highlighting the state-of-the-art performance of our proposed model on molecular property prediction tasks. We also thank the reviewer for raising valuable questions. We address all questions raised by the reviewer point-by-point. \n\n**1. Why chose only 3 TDC datasets/more TDC datasets/other downstream tasks on larger datasets:** We actually evaluated TRIDENT on five TDC datasets: three in the main text (as noted by the reviewer) and two additional ones in Appendix Table 8. The appendix includes results on larger datasets (AMES with 7,255 drugs and hERG with 648 drugs), where TRIDENT also outperforms all baselines. Additionally, for the rebuttal, we conducted experiments on two other larger datasets (CYP P450 2C19 with 12,665 drugs and Caco-2 with 906 drugs ), bringing the total to seven TDC datasets. For completeness, we report all results below, which further confirm TRIDENT’s consistent performance across diverse TDC benchmarks.\n| Dataset | Metric | MolT5 | MolXPT | BioT5 | BioT5+ | TRIDENT (M-M) |\n|---------|--------|-------|--------|-------|--------|---------------|\n| DILI (475 drugs) | ROC-AUC | 77.37±1.15 | 91.67±0.76 | 82.45±1.81 | 82.58±1.65 | **94.56±0.88** |\n| | ACC | 69.44±1.20 | 84.03±3.19 | 76.39±3.18 | 80.56±1.20 | **86.80±3.18** |\n| Carcinogens (278 drugs) | ROC-AUC | 86.89±1.00 | 75.76±2.73 | 82.83±4.31 | 86.62±2.32 | **87.07±0.77** |\n| | ACC | 84.45±1.11 | 80.90±2.06 | 76.19±2.06 | 77.41±2.00 | **84.62±1.07** |\n| Skin Reaction (404 drugs) | ROC-AUC | 68.67±3.99 | 61.08±1.28 | 68.27±4.41 | 65.25±0.66 | **72.00±1.09** |\n| | ACC | 62.22±1.41 | **62.60±1.40** | 62.21±1.06 | 62.27±1.20 | **62.60±1.40** |\n| hERG (648 drugs) | ROC-AUC | 76.25±1.22 | 82.44±2.14 | 77.48±1.59 | 82.27±3.29 | **83.31±1.63** |\n| | ACC | 77.04±4.90 | 81.31±2.31 | 80.30±3.03 | 80.31±1.52 | **83.33±2.62** |\n| AMES (7,255 drugs) | ROC-AUC | 76.93±0.84 | 83.73±0.69 | 77.57±0.69 | 78.18±1.48 | **86.87±0.60** |\n| | ACC | 70.87±2.22 | 76.50±2.06 | 73.25±1.67 | 73.30±1.15 | **80.20±1.44** |\n| CYP P450 2C19 (12,665 drugs) | ROC-AUC | 87.32±0.34 | 86.87±0.61 | 84.34±0.15 | 84.78±0.32 | **87.50±0.26** |\n| | ACC | 77.86±0.44 | 77.92±0.99 | 76.40±1.14 | 76.43±0.94 | **80.08±0.17** |\n| Caco-2 (906 drugs) | RMSE $\\downarrow$ | **0.41±0.01** | 0.48±0.03 | 0.57±0.03 | 0.60±0.05 | **0.41±0.03** |\n\nIn case the reviewer referred to large molecules such as proteins by larger datasets, we emphasize that since the pre-training is done on small molecules data curated from PubChem, we have benchmarked the model on many tasks from commonly used small molecule property prediction benchmarks (MoleculeNet and TDC) relevant for small molecule drug-discovery applications. Extending TRIDENT to larger molecules (such as proteins) is an interesting future work! \n\n**2. Ablation and discussion on local alignment hyperparameters:** The local alignment module has only two main hyperparameters:(a) the relative weighting of local vs. global alignment terms, (b) the aggregation (pooling) strategy for consolidating sub-text and sub-structure representations. \n\n  a. In the main text (Table 4), we already presented ablation results for different weighting strategies for local vs. global alignment terms, showing that momentum-based dynamic weighting performs best.  \n\n  b. Following the reviewer’s suggestion, we additionally ablated the aggregation strategy, comparing multiple pooling operators. Note that the “weighted mean pooling” variant refers to assigning weights based on the frequency of functional groups present in the molecule. The results (reported below: three MoleculeNet regression tasks and one TDC dataset (Caco-2)) indicate that TRIDENT’s performance is similar irrespective of the choice of aggregation function.   \n| Method | Metric | ESOL | FreeSolv | Lipophilicity | Caco-2 |\n|--------|--------|------|----------|---------------|--------|\n| Max pooling | RMSE | 0.72 ± 0.07 | 1.42 ± 0.03 | 0.60 ± 0.01 | 0.41 ± 0.03 |\n| Mean pooling | RMSE | 0.73 ± 0.02 | 1.44 ± 0.02 | 0.62 ± 0.02 | 0.42 ± 0.04 |\n| Weighted mean pooling | RMSE | 0.71 ± 0.04 | 1.42 ± 0.03 | 0.61 ± 0.01 | 0.40 ± 0.02 |\n\nWe will include these results in the Appendix of camera-ready version. Given that we have addressed all your questions, we request if the reviewer may kindly consider raising their score!"}, {"review_id": "JoUoTSrxQC", "reviewer": "Reviewer_g9iG", "summary": "This paper introduce a molecular representation learning model that utilize three types of data, SMILES, text and hierarchical taxonomic annotation (HTA). HTA is a new datatype that is used in molecular representation learning, and the author curated a dataset with HTA as well as SMILES and text of 47,269 molecules. A new contrastive learning loss for multimodal alignment, Global Volume-based Contrastive Loss, is utilized to train train the model with three data types. In addition, fine-grained local alignment between molecular substructures and the relevant text description is explored in model training.  Experiments are conducted on two public datasets for molecular property learning and the proposed method outperform a series of baseline methods.", "questions": "please respond to the weakness points", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\n1.The curated dataset with many different hierarchical taxonomic annotation are valuable for the field.\n\n2.The motivation of using multimodal alignment to train a molecular representation learning model with three modality is reasonable.\n\n3.The idea of align molecular substructures with their function description is valuable.\n\n4.The performance of the proposed method is superior over baseline methods with the same available data.\n\n\n\nWeakness.\n1.With the MoleculeNet dataset, there are a series of regression tasks, which are also used for evaluating molecular property prediction models. But this proposed method is not evaluated on these tasks.\n\n2.There are approximately 380k SMILES-text pairs in the original dataset, from witch the authors curated 47,269 SMILES-Text-HTA triplets. The size of the triplet dataset is much smaller than the original dataset, which is a negative factor for representation learning. Two other kinds of ablation should be tried. First, adjust the proposed architecture to exclude HTA while utilize the whole data of 380k SMILES-text pairs. Second, on the base of the current proposed model, besides using the data of SMILES-Text-HTA triplets, adding different numbers of SMILES-text pairs data for model training.\n\n3.The overall performance of the proposed method is superior than the baselines with the same data available. However, there are some other studies using 3D molecular graph as input, which may also achieve high performance. This line of research should be discussed. A lower performance than the competing research line will also reduce the practical value of the proposed method.\n\n4. Comparison to more recent studies is needed. Such as the methods in the following works.\n[1]Pei Q, Wu L, Gao K, et al. BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning[C]//ACL (Findings). 2024.\n[2]Pei Q, Zhang W, Zhu J, et al. BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations[C]//The 2023 Conference on Empirical Methods in Natural Language Processing.\n[3]Liu Z, Zhang W, Xia Y, et al. MolXPT: Wrapping Molecules with Text for Generative Pre-training[C]//The 61st Annual Meeting Of The Association For Computational Linguistics. 2023.", "was_revised": true, "final_justification": "I raise my score from 4 to 5 after the response with additional latest baseline methods, experiments on new tasks and more ablation studies.", "rebuttal": "We thank the reviewer for their positive feedback and highlighting the value of our hierarchical taxonomic annotations for the field as well as for our substructure-subtext alignment approach which obtains SOTA performance on molecular property prediction benchmarks. We address the valuable questions and comments raised by the reviewer grouping similar questions together: \n\n**1. Results on MoleculeNet regression tasks and discussion/comparison with 3D models:** In addition to the MoleculeNet classification tasks reported in the main text, we have now also evaluated TRIDENT on three MoleculeNet regression tasks, and we report the results below (metric is RMSE). For a more comprehensive comparison, we extend the baseline set beyond those in the main text to include: (a) 2D topological graph models (GCN, GIN, GINE, SR-GINE) and 3D molecular graph models (Uni-Mol). These additional results demonstrate TRIDENT’s strong performance over existing and recent baselines (including recent 2D and 3D graph models ). We will cite and include the discussion of these models in the camera-ready version along with the new results on MoleculeNet regression tasks shown here. \n| Method | ESOL | FreeSolv | Lipophilicity |\n|--------|------|----------|---------------|\n| GCN | 1.43 ± 0.05 | 2.87 ± 0.14 | 0.85 ± 0.08 |\n| GIN | 1.45 ± 0.02 | 2.76 ± 0.18 | 0.85 ± 0.07 |\n| GINE | 0.98 ± 0.10 | 2.92 ± 0.31 | 0.81 ± 0.01 |\n| SR-GINE | 0.73 ± 0.17 | 2.52 ± 0.45 | 0.76 ± 0.02 |\n| Uni-Mol | 0.79 ± 0.03 | 1.48 ± 0.05 | **0.60 ± 0.02** |\n| BioT5 | 0.80 ± 0.02 | 1.63 ± 0.02 | 0.74 ± 0.07 |\n| BioT5+ | 0.79 ± 0.01 | 1.98 ± 0.13 | 0.74 ± 0.06 |\n| MolXPT | 0.75 ± 0.01 | 1.60 ± 0.03 | 0.69 ± 0.01 |\n| MolFormer | 0.78 ± 0.12 | 1.67 ± 0.06 | 0.63 ± 0.02 |\n| MolT5 | 0.82 ± 0.02 | 1.55 ± 0.14 | 0.65 ± 0.04 |\n| TRIDENT (M-M) | **0.72 ± 0.07** | **1.42 ± 0.03** | **0.60 ± 0.01** |\n\n**2. Comparison with BioT5, BioT5+, MolXPT:** We also compared TRIDENT against models BioT5, BioT5+, and MolXPT as suggested by the reviewer. We reported the comparison in the table above for three MoleculeNet regression tasks. We  are also including the results for 7 TDC datasets in the table below. TRIDENT outperforms the baselines here as well. We will also cite BioT5, BioT5+, and MolXPT in the camera-ready version and will include these results.\n| Dataset | Metric | MolT5 | MolXPT | BioT5 | BioT5+ | TRIDENT (M-M) |\n|---------|--------|-------|--------|-------|--------|---------------|\n| DILI (475 drugs) | ROC-AUC | 77.37±1.15 | 91.67±0.76 | 82.45±1.81 | 82.58±1.65 | **94.56±0.88** |\n| | ACC | 69.44±1.20 | 84.03±3.19 | 76.39±3.18 | 80.56±1.20 | **86.80±3.18** |\n| Carcinogens (278 drugs) | ROC-AUC | 86.89±1.00 | 75.76±2.73 | 82.83±4.31 | 86.62±2.32 | **87.07±0.77** |\n| | ACC | 84.45±1.11 | 80.90±2.06 | 76.19±2.06 | 77.41±2.00 | **84.62±1.07** |\n| Skin Reaction (404 drugs) | ROC-AUC | 68.67±3.99 | 61.08±1.28 | 68.27±4.41 | 65.25±0.66 | **72.00±1.09** |\n| | ACC | 62.22±1.41 | **62.60±1.40** | 62.21±1.06 | 62.27±1.20 | **62.60±1.40** |\n| hERG (648 drugs) | ROC-AUC | 76.25±1.22 | 82.44±2.14 | 77.48±1.59 | 82.27±3.29 | **83.31±1.63** |\n| | ACC | 77.04±4.90 | 81.31±2.31 | 80.30±3.03 | 80.31±1.52 | **83.33±2.62** |\n| AMES (7,255 drugs) | ROC-AUC | 76.93±0.84 | 83.73±0.69 | 77.57±0.69 | 78.18±1.48 | **86.87±0.60** |\n| | ACC | 70.87±2.22 | 76.50±2.06 | 73.25±1.67 | 73.30±1.15 | **80.20±1.44** |\n| CYP P450 2C19 (12,665 drugs) | ROC-AUC | 87.32±0.34 | 86.87±0.61 | 84.34±0.15 | 84.78±0.32 | **87.50±0.26** |\n| | ACC | 77.86±0.44 | 77.92±0.99 | 76.40±1.14 | 76.43±0.94 | **80.08±0.17** |\n| Caco-2 (906 drugs) | RMSE $\\downarrow$ | **0.41±0.01** | 0.48±0.03 | 0.57±0.03 | 0.60±0.05 | **0.41±0.03** |\n\n**3. Ablations (a) with 380k SMILES-text original dataset, (b) using further fine-tuning with larger SMILES-text dataset starting with pre-trained TRIDENT weights:** We appreciate the reviewer’s suggestion to explore this. For this ablation, we pre-trained TRIDENT on the full 380k SMILES–Text pairs without HTA and evaluated it on three MoleculeNet regression tasks and one TDC dataset (Caco-2). Training solely on the full 380k SMILES–Text pairs degrades performance due to known dataset issues (artificially templated text, limited semantic richness (too short descriptions), and duplicates) [1]. For the second ablation, we used the model weights from triplet-based trained TRIDENT model and fine-tuned it with remaining (380k-47,269) SMILES-text pairs. The performance improved slightly but still lagged behind TRIDENT trained solely on curated triplets as fine-tuning on potentially low quality data is detrimental for learning. This confirms the value of our data curation contribution and the inclusion of hierarchical taxonomic annotations (HTA) for representation learning. \n| Method | Metric | ESOL | FreeSolv | Lipophilicity | Caco-2 |\n|--------|--------|------|----------|---------------|--------|\n| 380k SMILES-text | RMSE | 0.78 ± 0.13 | 1.68 ± 0.07 | 0.67 ± 0.05 | 0.53 ± 0.06 |\n| Finetune | RMSE | 0.75 ± 0.06 | 1.61 ± 0.15 | 0.65 ± 0.13 | 0.51 ± 0.05 |\n| Ours | RMSE | **0.72 ± 0.07** | **1.42 ± 0.03** | **0.60 ± 0.01** | **0.41 ± 0.03** |\n\n\nWe believe we have addressed all your questions and request the reviewer if they may consider raising their score! \n\n[1]: Ross, Jerret, et al. \"Large-scale chemical language representations capture molecular structure and properties.\" Nature Machine Intelligence 4.12 (2022): 1256-1264."}, {"review_id": "AGeraN3MyR", "reviewer": "Reviewer_C5UF", "summary": "This study argues that previous models overlook fine-grained annotations and suffer from alignment limitations among modalities, as well as ignoring local fine-grained relationships. To address this issue, this work combines SMILES, textual descriptions, and annotations together to create a high-quality dataset with 47,269 molecules from PubChem. It trains the TRIDENT model using a designed volume-based contrastive loss. Moreover, a novel local alignment module is proposed to link substructures to descriptions. The model achieves SOTA performance across 11 molecular property prediction tasks from TDC and MoleculeNet.", "questions": "1. The ablation study evaluates the contribution of each module, but why not provide sensitivity analysis on different hyperparameter settings, such as learning rate or temperature for contrastive loss?\n2. Why not compare with stronger and more recent baselines such as Uni-Mol or HiGNN, which are commonly used in recent molecular representation works?\n3. This paper mentions multimodal fusion, but why not include comparisons with other recent multimodal models like 3D-MolT5, GIMLET, or MolFM, which are also designed for aligning structure and language?", "limitations": "1. The model lacks sensitivity analysis on hyperparameter settings, and we cannot evaluate its robustness or stability under different training conditions.\n2. The baselines used in experiments are not the most recent or strongest, and the improvements on most datasets are small, which makes it hard to claim SOTA performance.\n3. There is no comparison with other recent multimodal fusion models such as 3D-MolT5, GIMLET, or MolFM, so the advantage of this method in multimodal fusion is not clearly demonstrated.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\n1. This work introduces annotation data into the training process, which improves the model’s understanding of molecules and substructures. This is a very promising and innovative direction.\n2. To verify the effectiveness of the TRIDENT method, this paper evaluates on 11 datasets from two benchmarks and compares with multiple baselines using sequence inputs, demonstrating the performance of the proposed model.\n\nWeakness:\n1. The ablation experiments evaluate the contribution of each module to the overall model and highlight the role of HTA. However, there is no sensitivity analysis regarding model-related settings, such as different hyperparameter choices.\n2. In the experimental part, the paper does not compare with newer and stronger baselines such as Uni-Mol and HiGNN. Moreover, the improvements on most datasets are small, which makes it difficult to prove that the model achieves over SOTA performance significantly. It also does not compare with recent multimodal fusion models such as 3D-MolT5 and GIMLET, or MolFM which is mentioned in the related work. The advantage of this paper’s multimodal fusion is not clearly demonstrated.", "was_revised": true, "final_justification": "The authors have addressed my comments so I increased the score to 5.", "rebuttal": "We thank the reviewer for their feedback and highlighting the innovative model design and SOTA performance of our TRIDENT model. We address the valuable questions and comments raised by the reviewer point-by-point:\n\n**1. Sensitivity analysis on hyperparameter settings:** Following the reviewer’s suggestion, we performed sensitivity analysis on key hyperparameters: learning rate, contrastive loss temperature, and the momentum parameter β (used for weighting global and local alignment losses). We conducted this analysis on three MoleculeNet datasets and one TDC dataset (Caco-2). The results show that within a reasonable range of hyperparameters (learning rate 1e−4 to 1e−5, β between 0.5–0.9, and temperature 0.07–0.03), TRIDENT consistently achieves similar performance. This indicates that TRIDENT is robust to hyperparameter choices and does not require tuning for each individual dataset.\n| lr, beta, temperature | Metric | ESOL | FreeSolv | Lipophilicity | Caco-2 |\n|----------------------|--------|------|----------|---------------|--------|\n| 1e-5 & 0.9 & 0.07 (Ours) | RMSE | 0.718±0.072 | 1.424±0.031 | 0.603±0.009 | 0.413±0.03 |\n| 1e-5 & 0.9 & 0.03 | RMSE | 0.723±0.035 | 1.485±0.052 | 0.617±0.004 | 0.419±0.07 |\n| 1e-5 & 0.5 & 0.07 | RMSE | 0.739±0.024 | 1.478±0.076 | 0.619±0.008 | 0.433±0.04 |\n| 1e-4 & 0.5 & 0.07 | RMSE | 0.743±0.040 | 1.482±0.035 | 0.623±0.008 | 0.431±0.05 |\n| 1e-4 & 0.9 & 0.07 | RMSE | 0.731±0.004 | 1.443±0.043 | 0.602±0.005 | 0.418±0.02 |\n\n\n**2. Additional recent or multimodal baselines:** In response to the reviewer’s comment (and also reviewer g9iG), we have extended our comparisons to include recent state-of-the-art baselines, namely Uni-Mol (3D molecular modeling) and multimodal large-scale models such as BioT5, BioT5+, and MolXPT. (The suggested comparison with MolFM is already reported in Table 1 of the paper.) We have now evaluated these new models on MoleculeNet regression tasks and seven TDC datasets, where TRIDENT consistently outperforms these strong baselines. These additional results and corresponding citations to these baselines will be incorporated into the camera-ready version of the paper. For completeness, we also report the results below. \n\n\n*MoleculeNet regression tasks:*\n| Method | ESOL | FreeSolv | Lipophilicity |\n|--------|------|----------|---------------|\n| GCN | 1.43 ± 0.05 | 2.87 ± 0.14 | 0.85 ± 0.08 |\n| GIN | 1.45 ± 0.02 | 2.76 ± 0.18 | 0.85 ± 0.07 |\n| GINE | 0.98 ± 0.10 | 2.92 ± 0.31 | 0.81 ± 0.01 |\n| SR-GINE | 0.73 ± 0.17 | 2.52 ± 0.45 | 0.76 ± 0.02 |\n| Uni-Mol | 0.79 ± 0.03 | 1.48 ± 0.05 | **0.60 ± 0.02** |\n| BioT5 | 0.80 ± 0.02 | 1.63 ± 0.02 | 0.74 ± 0.07 |\n| BioT5+ | 0.79 ± 0.01 | 1.98 ± 0.13 | 0.74 ± 0.06 |\n| MolXPT | 0.75 ± 0.01 | 1.60 ± 0.03 | 0.69 ± 0.01 |\n| MolFormer | 0.78 ± 0.12 | 1.67 ± 0.06 | 0.63 ± 0.02 |\n| MolT5 | 0.82 ± 0.02 | 1.55 ± 0.14 | 0.65 ± 0.04 |\n| TRIDENT (M-M) | **0.72 ± 0.07** | **1.42 ± 0.03** | **0.60 ± 0.01** |\n\n\n*7 TDC datasets:*\n| Dataset | Metric | MolT5 | MolXPT | BioT5 | BioT5+ | TRIDENT (M-M) |\n|---------|--------|-------|--------|-------|--------|---------------|\n| DILI (475 drugs) | ROC-AUC | 77.37±1.15 | 91.67±0.76 | 82.45±1.81 | 82.58±1.65 | **94.56±0.88** |\n| | ACC | 69.44±1.20 | 84.03±3.19 | 76.39±3.18 | 80.56±1.20 | **86.80±3.18** |\n| Carcinogens (278 drugs) | ROC-AUC | 86.89±1.00 | 75.76±2.73 | 82.83±4.31 | 86.62±2.32 | **87.07±0.77** |\n| | ACC | 84.45±1.11 | 80.90±2.06 | 76.19±2.06 | 77.41±2.00 | **84.62±1.07** |\n| Skin Reaction (404 drugs) | ROC-AUC | 68.67±3.99 | 61.08±1.28 | 68.27±4.41 | 65.25±0.66 | **72.00±1.09** |\n| | ACC | 62.22±1.41 | **62.60±1.40** | 62.21±1.06 | 62.27±1.20 | **62.60±1.40** |\n| hERG (648 drugs) | ROC-AUC | 76.25±1.22 | 82.44±2.14 | 77.48±1.59 | 82.27±3.29 | **83.31±1.63** |\n| | ACC | 77.04±4.90 | 81.31±2.31 | 80.30±3.03 | 80.31±1.52 | **83.33±2.62** |\n| AMES (7,255 drugs) | ROC-AUC | 76.93±0.84 | 83.73±0.69 | 77.57±0.69 | 78.18±1.48 | **86.87±0.60** |\n| | ACC | 70.87±2.22 | 76.50±2.06 | 73.25±1.67 | 73.30±1.15 | **80.20±1.44** |\n| CYP P450 2C19 (12,665 drugs) | ROC-AUC | 87.32±0.34 | 86.87±0.61 | 84.34±0.15 | 84.78±0.32 | **87.50±0.26** |\n| | ACC | 77.86±0.44 | 77.92±0.99 | 76.40±1.14 | 76.43±0.94 | **80.08±0.17** |\n| Caco-2 (906 drugs) | RMSE $\\downarrow$ | **0.41±0.01** | 0.48±0.03 | 0.57±0.03 | 0.60±0.05 | **0.41±0.03** |\n\nWe thank the reviewer again for their questions! Given that we have addressed all your questions, we request you to kindly consider raising your score!"}]

Author rebuttal:

Meta-review: This paper proposes a molecular representation learning framework, TRIDENT, which leverages three modalities: SMILES (molecular structure), Molecular Functional Description (text), and Hierarchical Taxonomic Annotation (text). To this end, the authors introduce a newly curated, high-quality multi-modal molecular dataset and propose two learning objectives: (1) geometry-based global alignment across the three modalities, and (2) fine-grained local alignment based on functional group structures.

The authors successfully addressed the reviewers' concerns during the discussion phase, and as a result, all reviewers unanimously support acceptance. AC also agrees with the strengths highlighted by the reviewers:
- A novel and effective multi-modal molecular representation learning approach that integrates SMILES, textual descriptions, and HTA for richer representations,
- Strong experimental results across a wide range of benchmarks and recent baselines,
- A newly curated multi-modal dataset that includes informative hierarchical annotations.

Therefore, AC recommends acceptance of this submission. The authors are strongly encouraged to incorporate the reviewers' feedback, especially additional experimental results on new benchmarks and baselines, as doing so would further strengthen and clarify the contributions of this work.
Decision: Accept (spotlight)