An Open Quantum Chemistry Property Database of 120 Kilo Molecules with 20 Million Conformers

## Abstract

Abstract Artificial intelligence is revolutionizing computational chemistry, bringing unprecedented innovation and efficiency to the field. To further advance research and expedite progress, we introduce the Quantum Open Organic Molecular (QO2Mol) database — a large-scale quantum chemistry dataset designed for professional and transformative research in organic molecular sciences under an open-source license.
The database comprises 120,000 organic molecules and approximately 20 million conformers, encompassing 10 different elements (C, H, O, N, S, P, F, Cl, Br, I), with heavy atom counts exceeding 40. Utilizing the high-precision B3LYP/def2-SVP quantum mechanical level, each conformation was meticulously computed for quantum mechanical properties, including potential energy and forces. These molecules are derived from fragments of compounds in ChEMBL, ensuring their structural relevance to real-world compounds . Its extensive coverage of molecular structures and diverse elemental composition enables comprehensive studies of structure-property relationships, enhancing the accuracy and applicability of machine learning models in predicting molecular behaviors.
The QO2Mol database and benchmark codes are available at https://github.com/saiscn/QO2Mol/ .

## 1 Introduction

The advent of artificial intelligence (AI) has heralded a new era of innovation and efficiency in computational chemistry.
Among the various areas of focus within computational chemistry, the study of small organic molecules holds a particularly prominent position due to their fundamental importance in diverse scientific disciplines, including drug discovery (Mayr et al., ; Chen et al., ; Agüero-Chapin et al., ; Stokes et al., ; Zeng et al., ), reaction prediction (Żurański et al., ; Wang et al., ; Pereira and Trofymchuk, ; Lin et al., ; Ding et al., ), and materials science (Yang et al., ; Cheng et al., ; Dai et al., ; Bu et al., , ).
For instance, in drug discovery, computer-aided drug design (CADD) technologies, including ligand-protein docking and rapid binding free energy estimation, depend on modeling small organic molecules. This facilitates the identification and optimization of lead compounds, ultimately accelerating the delivery of new drug candidates at reduced costs.
In addition, physiology and pathology medical experts are concerned with the various behaviors of small organic molecules in the human body environment, including the prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) and molecular metabolism, while materials and chemistry experts focus on the physicochemical properties of small organic molecules to develop polymer composites, identify catalysts, and discover new chemical reactions.

However, there is currently still a lack of a publicly available large-scale quantum chemistry dataset to support the increasingly extensive research on small organic molecules by AI and computational chemistry experts in the field.
Existing public quantum chemistry datasets are either constrained by limited elemental diversity and molecular variety, or by a small sample size predominantly focused on small molecules with low heavy atom counts, thereby lacking the necessary breadth and comprehensiveness for robust research applications. Figure 1 illustrates that other commonly used datasets are restricted in both their coverage of element types and the number of conformers they encompass. We provide a more detailed comparison and description of the shortcomings of existing datasets in Section 3.

Figure: Figure 1: Main characteristics of commonly used datasets regarding elemental coverage and the number of molecular structures. The left panel illustrates the coverage of elements; The right panel presents the number of conformations.
Refer to caption: extracted/5952216/figs/figure1_comparation.png

To address these challenges and to promote deeper development in the field, we release Quantum Open Organic Molecular (QO2Mol) database, the large-scale quantum chemistry dataset with 20 million conformers, designed for the research in molecular sciences under an open-source license.
We provide a comprehensive set of molecular property labels, encompassing potential energy, forces, and formal charge, and additional relevant attributes.
In Figure 1, compared to other well-known datasets, QO2Mol covers the widest variety of 10 elements and includes the largest number of conformers. Additionally, QO2Mol employs high-precision quantum mechanical calculations, which are computationally intensive and costly(^1^11Approximately 10 million core-hours of CPU resources in total.).
By offering this high-quality data to the global scientific community, we aim to accelerate advancements in computational chemistry, material science, and drug discovery. In summary, our key contributions are threefold:

- •
Firstly, we introduce the QO2Mol dataset, which comprises 120,000 organic molecules and over 20 million conformers. This database covers 10 different elements with heavy atom counts exceeding 40, closely mirroring the distribution of chemical structures found in widely used real compound libraries.
- •
Secondly, we employ high-precision methods and the B3LYP/def2-SVP basis set to obtain reliable molecular property labels, including potential energy and forces, providing a valuable database for future research and model development.
- •
Finally, we provide scripts for loading and processing the dataset, along with benchmark code and comparative results, enabling researchers to quickly get started and easily integrate the dataset into their projects.

We hope these contributions would effectively advance the field of computational chemistry and provide essential resources and methodologies for accurate molecular modeling.

## 2 Background Information

### 2.1 Basic concepts of computational chemistry

We introduce the necessary preliminaries of computational chemistry that will be used later.

- •
Density Functional Theory (DFT) (Thomas, ) is a popular computational method used to solve Schrödinger equation which offers property labels of molecules.
- •
Force fields can be applied in various areas of computational chemistry, such as Free Energy Perturbation (FEP) calculations (Jiang and Roux, ; Wang et al., ).
- •
InChI (Heller et al., ) (The International Chemical Identifier) is a unique representation of a chemical substance.
InChIKey (Pletnev et al., ) is a compacted version of InChI with 27-character fixed-length. InChIKey is intended for identifying a unique molecule in database searching/indexing (Wikipedia contributors, ).
- •
SMILES (Weininger, ) (Simplified Molecular Input Line Entry System) is a ASCII string that represents a chemical structure in a way that can be friendly used by the computer.
- •
Heavy atom is any atom other than hydrogen, typically used in molecular studies to focus on more complex atomic interactions.

### 2.2 Calculation Precision

In quantum chemistry, computational precision is closely tied to the choice of calculation methods and basis sets. Advanced methods offer higher precision but demand substantial computational resources.
Among DFT calculation functionals, B3LYP (Becke, ; Lee et al., ; Becke, ; Stephens et al., ) is the one of most popular choices in quantum mechanical calculations of organic molecular systems due to its balance between computational efficiency and precision.
We enumerate the computational precision levels of commonly used datasets, as illustrated in Figure 2.
It can be observed from the figure that B3LYP is employed by the majority of previous datasets.
In QO2Mol, we employ the B3LYP/def2-SVP calculation method, one of the highest precision levels achievable within an acceptable computational cost range for large-scale calculations of organic molecular systems.

Figure: Figure 2: Main characteristics of commonly used datasets in terms of precision level and number of heavy atoms. Size of each circle corresponds to the number of elements covered in each dataset.The precision level of QM9 (Ramakrishnan et al., ), AN1-1 (Smith et al., ), AlChemy (Chen et al., ) are directly obtained from their original paper. PCQM4Mv2 dataset is secondarily derived from the PubChemQC Project, we employ the precision level outlined in PubChemQC Project (Nakata and Shimazaki, ).
Refer to caption: extracted/5952216/figs/figure2_precision_level.png

### 2.3 The Potential Impact of Our Dataset on Data-Driven Methods

This section explores the potential impacts of our dataset on three specific areas: Potential Energy Surfaces, Force Field Parameters, and Conformation Generation. However, it is crucial to recognize that the scope of influence may reach well beyond these identified domains.

#### Potential Energy Surface

The potential energy surface (PES) of atomistic systems is the core of several aspects of physical chemistry, such as transition states, vibrational frequencies and electronic properties. Many of current methods based on deep learning mechanism focus on deploying neural networks to predict QM computed properties (Qiao et al., ; Atz et al., ; Walters and Barzilay, ; Chen et al., ; Wang et al., ). These methods directly predict the QM properties instead of solving the many-body Schrodinger equation numerically. All these methods require high-precision QM data for training reliable models without exception.

#### Force Field Parameters

Force fields are mathematical models that describe the potential energy of a molecular system as a function of the positions of all atoms within it. These models are essential for molecular dynamics simulations and other computational studies that predict molecular behavior (Joshi and Deshmukh, ; Shub et al., ; Suzuki et al., ; Souza et al., ; Bejagam et al., ). The success of such optimizations not only improves the accuracy of simulations but also extends the applicability of the force fields to a broader range of chemical and biological systems.

#### Conformation generation

High-quality data ensures accurate molecular descriptions and physically plausible conformations, crucial for applications like drug design. It enables the training of robust models that can generalize across diverse molecular structures, enhances predictive accuracy, reduces computational waste, and supports rigorous model validation. Thus, maintaining high data quality is essential for advancing research and development in computational chemistry and related fields.

## 3 Previous Datasets

Table: Table 1: Summary of main characteristics among commonly used QM datasets.

We provides a comparative overview of several commonly used quantum mechanical datasets in Table 1, highlighting their respective methodologies, molecular coverage, and elemental diversity.
QM9 (Ramakrishnan et al., ), employing the B3LYP/6-31G(2df,p) method, contains 134,000 molecules with a maximum of 9 heavy atoms, limited to the elements H, C, N, O, and F. The AN1-1 dataset (Smith et al., ), released in 2017, using the $\omega$B97x/6-31G(d) method, features 22 million molecules but is restricted to only 8 heavy atoms and 4 elements (H, C, N, O). Alchemy (Chen et al., ), released in 2019, also uses the B3LYP/6-31G(2df,p) method but includes 119,000 molecules, expanding the elemental range to H, C, N, O, F, S, and Cl, and accommodating up to 14 heavy atoms. PCQM4Mv2 (Hu et al., ), utilizing data from the PubChemQC Project (Nakata and Shimazaki, ) which employs the B3LYP/6-31G(d) level of precision, comprises 3.7 million molecules and includes elements H, C, N, O, F, S, and Cl.

Overall, the QO2Mol dataset encompasses the widest variety of elements. Employing the high-precision B3LYP/def2-SVP method, QO2Mol encompasses an impressive 20M molecules, supporting more than 40 heavy atoms, and extends its elemental range to H, C, N, O, F, P, S, Cl, Br, and I.
We present in Figure 2 a comparative analysis of these datasets in terms of computational accuracy, the number of heavy atoms, and the variety of elements.

Most earlier released datasets like QM9 are severely limited in the number of molecular structures, making them grossly inadequate for training large-scale models. Furthermore, although ANI-1 boasts a considerable sample size, its restriction to only 4 elements (H, C, N, O) imposes a limitation for studying small organic molecules with diverse spectral properties. In addition, PCQM4v2 only provides HOMO-LUMO gap labels, which are insufficient for supporting more complex molecular tasks and studies.

Figure: Figure 3: Distribution of the number of conformations with different heavy atom counts among commonly used datasets. We omitted Alchemy because of its small scale.
Refer to caption: extracted/5952216/figs/figure3_dist_heavynum.png

In Figure 3, QO2Mol exhibits the broadest distribution of heavy atom counts and the richest number of conformations overall. In contrast, while ANI-1 offers a substantial number of conformations for smaller heavy atom counts, its limitation to a maximum of 8 heavy atoms severely impacts the diversity and realism of the structures it covers. For example, organic molecular structures with high occurrence rates such as naphthalene (10 heavy atoms) and biphenyl (12 heavy atoms) cannot be incorporated.
QO2Mol’s extensive molecular and elemental coverage, combined with advanced computational methodology, underscores its superior capacity for comprehensive quantum mechanical studies, particularly for larger organic molecules and a broader spectrum of elements.

#### Remark

We also acknowledge the existence of several other notable datasets in the field, such as OC20/22 (Chanussot et al., ; Tran et al., ), which is frequently used for crystalline material tasks. However, these datasets focus on different domains and are not directly designed for the study of small organic molecules. Our dataset specifically addresses the unique challenges and requirements of high-precision quantum mechanical calculations for organic molecules, filling a gap that existing datasets do not cover. This distinction ensures that our contributions are both complementary to and distinct from the current resources available in the field.

## 4 Dataset Generation

In this section, we outline the rigorous process of data selection, processing, and preparation in QO2Mol.
To ensure the quality and reliablity of quantum mechanical data, the following considerations need to be taken into account :

- •
The selected molecules should represent a chemical space that closely aligns with the distribution of chemical structures found in widely used compound library, such as ZINC (Irwin et al., ), PubChem (Wang et al., ), and ChEMBL (Gaulton et al., ).
- •
Identify as many key conformations as possible on the potential energy surface, as these play a critical role in determining the properties of the molecules.
- •
Calculate properties using high-level quantum mechanical methods to ensure accuracy and reliability.

By adhering to these guidelines, we release the QO2Mol dataset, which comprises 120,000 organic molecules and their corresponding 20 million conformations.

### 4.1 Molecule Fragmentation

We first derive a set of source compounds from ChEMBL, a widely used virtual screening compound database for drug design (Sadybekov and Katritch, ).
Performing quantum mechanical calculations directly on these compounds is quite challenging due to the large size of these molecules. To overcome the computational difficulties of quantum mechanical calculations, we employed a Compound Fragmentation Process, dividing the source compounds into smaller fragments containing fewer heavy atoms, as shown in Figure 4. In this way, we ensured that the basic fragment structures can be found in real-world molecules and are therefore chemically meaningful. Then a total of 120,000 fragmented molecules were selected based on three rules: 1) with top 90% occurrence frequency over the database; 2) labeled as important phosphate groups by our chemistry expert; 3) encompassing 10 different elements(C, H, O, N, S, P, F, Cl, Br, I).
We also ensured that there was no fragment duplication during the generation procedure by utilizing InChIKey and canonical SMILES identifiers.

Our selection criteria did not impose restrictions on the number of heavy atoms. This approach enables us to capture a diverse range of significant and complex chemical space that might not be adequately represented in existing databases, such as QM9 and ANI-1.

Figure: Figure 4: An example of molecule fragmentation process. The molecule (a) is decomposed into four fragments: F1, F2, F3, and F4r.
Refer to caption: extracted/5952216/figs/fig_fragment_process.jpg

### 4.2 Conformation Generation

The constituent atoms of a molecule exhibit dynamic motion in three-dimensional space, generating the molecule’s conformational space. Each conformation has its own unique energy, collectively forming the molecular potential energy surface in 3N-dimensional space. The macroscopic properties of a molecule are effectively described by the ensemble average of the various conformational properties existing on this PES. Thus, the contributions of key conformations, such as local minima or transition state structures, are considerably important, while the significance of other conformations is also noteworthy. Given that, we sampled multiple conformations for each molecule within the QO2Mol database.

#### Structure Optimization

For each selected fragment molecule, an initial 3D structure is generated using the RDKit package (Landrum et al., ) based on its SMILES (Weininger, ) representation.
Then each initial structure is optimized to a local minimum at the B3LYP/def2-SVP precision level. To ensure the structure reliability, during the structure optimization process, we employ four convergence criteria to ensure the resulting structures are reasonable: 1) Maximum force
<0.00045; 2) root-mean-square force <0.00030; 3) maximum displacement <0.00180; 4) root-mean-square displacement <0.00120.
Following each structural optimization, we perform a validation step to ensure that all bond lengths fall within a defined range relative to their empirical values. For example, the empirical length of C–C single bond is approximately 1.54 Å as widely observed (Allen et al., ).
We provide a statistic distribution of C-C bond length over the whole dataset in Figure 5.

#### Conformation Search

Conformation search is performed on optimized structures obtained in the previous step. At room temperature, the flexible dihedral angles of molecules are likely to rotate. Therefore, rotation is the most influential factor in constructing potential energy surfaces. Based on this intuition, we perform rotational search in 30-degree increments each step on all rotatable bonds of each molecule.
By systematically rotating the flexible bonds of molecule to specific degrees, a series of new structures are generated. These structures are then optimized at the B3LYP/def2-SVP level with fixed torsions.
Additionally, for specific molecules, we also perform stretching and bending operations on bond lengths and bond angles, generating corresponding conformations.
We ensure that all bond types, such as C=C and C=O, are included in these manipulations.
Moreover, the database includes a collection of nearby unstable conformations for each stable conformation, further enhancing the representation of the overall molecular potential energy landscape. We provide a scan curve showing the potential energy changes during the flexible bond rotation in Figure 5.

Based on the mentioned conformation generation procedure, we finally obtained a total of 20 million conformers for the 120,000 molecules in our database.

Figure: Figure 5: Results of data generation. (left) The distribution statistics of C-C single bond lengths in the dataset. (right) An example of the scan curve illustrating change of potential energies associated with torsion rotation over a flexible bond during conformation search procedure.
Refer to caption: extracted/5952216/figs/figure_dist_cc_bondlength_and_torsion_energy.png

### 4.3 Properties

All conformations were analyzed to compute energy and forces at the B3LYP/def2-SVP level of theory. The forces, representing the first-order derivatives of energy with respect to coordinates, were calculated for each atom in the three Cartesian directions (x, y, z). Among the 20 million conformations, we also provide additional properties for approximately 210,000 stable conformations, although this is not the main focus of our contribution. For these stable conformations, we conducted frequency and charge population calculations. Vibrational frequencies were derived through diagonalization of the Hessian matrix, yielding 3N - 6 frequency values after excluding the three translational and three rotational modes. The Hessian matrix represents the second-order derivatives of energy with respect to coordinates. These frequency calculations allow for the determination of thermodynamic properties, including zero-point energy, entropy, enthalpy, heat capacity, and free energy, utilizing both harmonic and ideal gas approximations.. The charge population analysis includes the calculation of electron density-derived charges such as ESP (Electrostatic Potential) charges and Mülliken charges. More details are provided in Appendix B.

### 4.4 Data Segmentation

In order to support various learning tasks in this field, we divided the data into three subsets, with each subset exhibiting a different data distribution pattern serving distinct learning tasks, as depicted in Figure 6.

The main subset, referred to as subset A, encompassing the most extensive conformation data, containing 20 million conformations from more than 110,000 molecules. Unlike previous datasets that only sample equilibrium conformations at local minima, our subset A consists of equilibrium conformations at local minima and near-equilibrium conformations additionally sampled around local minima. These near-equilibrium conformations aid in training models and reconstructing high-precision potential energy surfaces. Due to its more comprehensive conformation sampling method and broad distribution of heavy atoms, subset A can be used for various learning tasks, such as neural network potential (NNP) regression tasks (Kocer et al., ), machine learning force field (MLFF) tasks (Fu et al., ), or denoising-like pretraining tasks (Zaidi et al., ).

Figure: Figure 6: Distribution of the number of heavy atoms over sub-datasets
Refer to caption: extracted/5952216/figs/figure_data_split_heavy_atoms.png

To introduce a higher level of complexity and challenge, we present the second subset, referred to as subset B, which includes 2.4 million conformers generated from approximately 1,400 molecules. This subset consists of carefully selected representative drug molecules, based on domain expert annotations, with a large number of heavy atoms ranging from 30 to 34, as shown in Figure 6. This subset facilitates multiple tasks, such as testing the model’s extrapolative and generalization capabilities and assessing its performance in real drug design workflows.

The third part, referred to as subset C, includes molecules that are non-analogous to those in subsets A and B. Subset C can be used for potential-related tasks either as a supplementary data source combined with the training set or as a validation set. Since the three subsets contain molecules that occupy distinct and separate regions in the chemical representation space, researchers have the flexibility to combine them in various ways.

## 5 Benchmark Results

Potential energy prediction is one of the most important benchmark tasks in the field of computational chemistry, as it serves as the foundation for numerous downstream tasks such as reaction simulations (Manzhos and Carrington, ), protein dynamics (Majewski et al., ), and crystal structure screening (Chen and Ong, ).
Additionally, the potential energy prediction task is typically employed to evaluate whether the model has successfully learned robust representations of molecular geometries (Gasteiger et al., , ; Liao and Smidt, ; Liu et al., ). Potential energy prediction task leverages the 3D structure of molecules as input to predict the potential energy of each conformation. In this section, we will discuss the results of benchmark models on the potential energy prediction task using the QO2Mol dataset.

### 5.1 Data Preprocess Pipeline

It has been successfully demonstrated that utilizing predefined atomic reference energies to optimize the model’s prediction target enables the neural network to focus on fitting conformational energies. This approach can be represented by the following formula:

$$ $E_{f}=E_{m}-\sum_{e}N_{e}\epsilon_{e}$ (1) $$

where $E_{f}$ denotes formation energy, $E_{m}$ denotes molecule energy. $N_{e}$ corresponds to the number of atoms of element $e$ and $\epsilon_{e}$ corresponds to the reference energy of single atom of element $e$. Such strategy has been demonstrated to effectively reduce the variance in energy fitting, enhancing the stability of training and the performance of the model on large-scale dataset. Notably, the top-ranked teams in the CFFF Prize all employed this approach.

### 5.2 Benchmark Models

In this section, we mainly consider two types of benchmark models: invariant models and equivariant models.
Invariant models, such as SchNet (Schütt et al., ), SphereNet (Zhao et al., ), DimeNet++ (Gasteiger et al., ), GemNet (Gasteiger et al., ), leverage features that remain unchanged under rotations and translations. These features include interatomic distances, bond angles, and torsion angles. By focusing on invariant features, these models can effectively capture the essential geometric relationships within molecular structures without being affected by their spatial orientation.
Equivariant models or approximately Equivariant model, such as Equiformer (Liao and Smidt, ), EquiformerV2 (Liao et al., ), and eSCN (Passaro and Zitnick, ), utilize features that transform predictably under rotations and translations. These features include the irreducible representations of the SO(3) group and higher-order interactions. Equivariant models are designed to handle the inherent symmetries of molecular systems, allowing them to better capture the directional dependencies and interactions between atoms.
Notably, most of these benchmark models were adopted by participants in the CFFF Prize.
By employing both invariant and equivariant models as benchmarks, we can comprehensively evaluate the performance and robustness of various approaches in capturing the complexities of molecular structures and dynamics.

### 5.3 Potential Prediction Benchmark

We first evaluate the interpolation performance of potential prediction task over a series of benchmark models on subset A , which is aforementioned in Section 4.4.
Subsequently, we undertook a more challenging task of employing these trained models to predict potential energies on the subset B, in order to evaluate the extrapolation capability of benchmark models.
The results are presented in Table 2.
We employ Mean Absolute Error (MAE) as the evaluation metric, measured in units of kcal·mol^-1.
Detailed experimental settings are provided in the Appendix D.

**Table 2: MAE results on potential prediction task in units of kcal·mol^-1.**
| Model | Params | Interpolation | Extrapolation |
| --- | --- | --- | --- |
| Spherenet | 2.7M | 0.10522 | 3.29613 |
| Equiformer | 3.5M | 0.07743 | 2.22257 |
| DimeNet++ | 5.0M | 0.07681 | 4.40856 |
| SchNet | 5.7M | 0.12974 | 8.73877 |
| GemNet | 5.7M | 0.02357 | 2.85464 |
| eSCN | 17.1M | 0.06417 | 3.60763 |
| EquiformerV2 | 38.0M | 0.04757 | 2.88512 |

Table 2 presents that GemNet stands out with the lowest MAE on test set A and relatively high generalization capability on test set B, indicating exceptional performance with a moderate number of parameters. Spherenet and SchNet, show higher MAE, reflecting limited expressive power. Equiformer and eSCN demonstrate good performance with lower MAE, balancing parameter count and accuracy effectively.

## 6 Conclusion

In this paper, we present the QO2Mol database, a open-source large-scale data resource designed for organic molecular researchs. This database stands out for its distinctive composition, as it comprises 120,000 organic molecules meticulously curated from real compound libraries. This vast collection spans across approximately 20 million conformers, showcasing both structural diversity and complexity. With representation from 10 different elements and heavy atom counts exceeding 40, the QO2Mol database offers an extensive and diverse molecular landscape for research exploration.By utilizing these data, researchers can simulate and predict molecular behaviors across different chemical environments, thereby advancing organic chemistry models and theories.

Despite the richness and diversity of the dataset, it may not cover all possible molecular configurations or adequately represent certain chemical environments. Future research endeavors could involve leveraging the diverse and extensive molecular data within the QO2Mol database to refine and optimize machine learning applications in the field of computational chemistry. New algorithms could be developed to better predict the chemical properties of complex molecules, or existing models could be improved to enhance their generalization capabilities and accuracy. Over time, we hope to see the QO2Mol database continually expand, incorporating new data to support a broader range of scientific research needs.

## Appendix A Key Information

#### Dataset documentation

All the documentation for our datasets, along with usage demo scripts via Python, are provided at [https://github.com/saiscn/QO2Mol](https://github.com/saiscn/QO2Mol).

#### Author statement

We bear all responsibility in case of violation of rights, etc., and confirmation of the data license.

#### License

This work uses CC BY-NC-SA 4.0. See details at [https://creativecommons.org/licenses/by-nc-sa/4.0/](https://creativecommons.org/licenses/by-nc-sa/4.0/).

#### Maintaining Plan

We utilize persistent cloud storage servers to provide accessing and downloading of the dataset. Further version will be updated upon research demands and the latest available links will be provided on the official Github repository.

## Appendix B Data File Format

The QO2Mol database comprises several chunk files, each containing a list of molecular data objects.
The description of the fields in each molecule object is provided in Table S1. We also provide a supplementary bunch of thermochemical properties at local minima to facilitate further research, with field names depicted in Table S2. Given the same data formats across all sets, researchers retain the flexibility to conduct data preprocessing or resplitting utilizing alternative methodologies.

**Table S1: Data File Format**
| Field | Description |
| --- | --- |
| inchikey | String, the identity of the conformer. |
| confid | String, the identity of the conformer. |
| atom_count | Integer, the number of atoms in the molecule. |
| bond_count | Integer, the number of bonds in the molecule. |
| elements | List, length equal to the number of atoms. Each value indicates the atomic number in the periodic table. |
| coordinates | List, length equal to the number of atoms. Each element is a 3-tuple representing the 3D coordinates (x, y, z) of the corresponding atom. |
| edge_list | List, length equal to the number of bonds multiplied by 2. Each element (i, j) represents an edge from atom i to atom j. |
| edge_attr | List, length equal to the number of bonds multiplied by 2. Each value represents a bond type. ’1’: single bond, ’2’: double bond, ’3’: triple bond. |
| energy | Float, the calculated potential energy of the molecule. |
| force | List, length equal to the number of atoms multiplied by 3. Each element represents the force component (x, y, z) of an atom. |
| net_charge | Float, the overall charge of a molecule. |
| formal_charge | List, length equal to the number of atoms. Each element represents the formal charge of the corresponding atom. |

**Table S2: Supplementary Thermochemical Properties**
| Field | Description |
| --- | --- |
| inchikey | String, the identity of the conformer. |
| confid | String, the identity of the conformer. |
| dipole | List, length equals 3 corresponding to Cartesian coordinate components. |
| esp_charge | List, length equals number of atoms. |
| mulliken_charge | List, length equals number of atoms. |
| freq | List, length equals 3N-6. N denotes number of atoms. |
| hessian | List, the upper triangular version of hessian matrix. Length equals 3N(3N+1)/2. |
| thermochem | Dict, containing 7 items: capacity, enthalpy, entropy, free_energy, thermal_e, total_e. |

## Appendix C Chemical Space

Relative to the QM9 database, which is limited to the elements C, H, O, N, and F, QO2Mol dataset encompasses a broader range of elements commonly found in organic molecules. These include C, H, O, N, S, P, F, Cl, Br, and I, which depicts the number of molecules in our dataset and QM9 containing for each element. QO2Mol dataset comprises a signiffcantly larger number of molecules that contain the element F, totaling 10,345 compounds, in contrast to the mere 310 F-containing molecules in QM9. Additionally, our dataset includes a substantial number of molecules containing S (29,702), P (2,464), Cl (9,829), Br (2,549), and I (647) elements, all of which are absent from the QM9 database. This expanded elemental coverage in our dataset enables a more comprehensive exploration of the chemical space, encompassing a wider array of important and diverse molecular structures.

Table S3 summarizes the presence of ring structures in the molecules. Rings are essential components of organic molecules, and the majority of drug molecules contain ring structures. Due to the inffuence of ring strain, 5-membered and 6-membered rings are more stable compared to 3-membered and 4-membered rings. It is evident from the results of the QO2Mol databases that molecules containing 5-membered and 6-membered rings are more prevalent. However, due to the limitations on heavy atom counts, the QM9 database includes a greater number of molecules with 3-membered and 4-membered rings. Aromatic rings represent a distinct category of ring structures, contrasting with aliphatic rings. Aromatic rings can be 5-membered, such as pyrrole and furan, or 6-membered, such as benzene and pyridine. Due to their high stability, aromatic rings are commonly encountered in organic molecules. In the ChemBL library, the majority of molecules contain aromatic rings, and a signiffcant proportion of molecules in the QO2Mol database also feature aromatic ring. However, the QM9 database exhibits a relatively lower percentage of molecules with aromatic rings, particularly 6-membered aromatic rings.

**Table S3: Summary of the presence of ring structures in the molecules**
|  |  | QO2Mol | QM9 |
| --- | --- | --- | --- |
| Ring Size | 3 | 3304 | 54489 |
|  | 4 | 3335 | 50720 |
|  | 5 | 53476 | 50951 |
|  | 6 | 72420 | 19527 |
|  | 7 | 4819 | 4465 |
|  | $>7$ | 1453 | 750 |
| Ring property | Aromatic (5) | 28264 | 12209 |
|  | Aromatic (6) | 45645 | 3239 |
|  | Non-aromatic | 46094 | 114552 |

## Appendix D Experiment Details

We conducted all experiment on A100 GPU cluster. For the interpolation task, we employ a 72%/18%/10% split for training, validation and testing on subset A. For the extrapolation task, we use the entire subset B. In our experiments, we established the basic parameter settings as follows. The cutoff radius is set to 5.0 angstrom for all models. The training process was conducted using the AdamW optimizer with a cosine annealing learning rate scheduler. For hyper-parameter optimization, we employed a grid search strategy. Target hyper-parameters include learning rate, batch size, and the weight decay, with the following ranges: learning rate {1e-3, 4e-4, 8e-4}, batch size {32, 64, 128, 256}, weight decay {0, 1e-5, 1e-4}. Each combination of hyper-parameters was evaluated on the valid set, and the configuration yielding the highest validation accuracy was selected for the final model. Convenient data loading scripts and relative codes are available at our Github repository.

## Peer Reviews

confidence: 4
contribution: 1
initial_rating: 3
presentation: 1
questions: For example, Figure 2, the precision levels seem to be some linear scale of various basis sets. While I am not an expert in computation chemistry, I believe this figure paints a deceptively simple measure of precision in DFT. Are the choice of level of theory and basis sets not dependent on the system being looked at; and are they appropriately chosen? Are there any dispersion corrections (as done in GEOM, and QCDGE)? Is the choice of DFT level of theory appropriate for properties that are used as targets (ie. entropy, enthalpy, heat capacity, etc.)? 

Is the fragmentation method deployed to generate the structures common used? There are no details given on the fragmentation algorithm used. The authors claim that this approach would give "a diverse range of significant and complex chemical space," with no evidence of this.

The conformation generation through cheminformatics software like RDKit seems low-precision, given the computational cost required for DFT at B3LYP/def2-SVP. GEOM for example uses CREST, which is based on semi-empirical tight-binding model. Is RDKit sufficient? What are convergence criteria typically used for optimization process of these structures?

Note that most of these questions would likely not require an explanation if reviewed by an expert in quantum computational chemistry.
soundness: 2
strengths: This paper demonstrates several strengths. It addresses a recognized need for a large and diverse quantum chemistry dataset for training and evaluating machine learning models in molecular sciences. The inclusion of benchmark results using established models provides a baseline for future research. The open-source availability of the dataset and code further encourages community engagement and broader application.

However, the paper's strong focus on quantum chemistry methodologies and detailed discussions of molecular properties, conformations, and potential energy surfaces might be more suitable for a computational chemistry or materials science audience. While leveraging machine learning for benchmarking, the core contribution and primary impact lie within the domain of chemistry, potentially making it less central to the interests of a machine learning conference focused on algorithmic advancements or theoretical foundations of AI.
summary: QO2Mol, an open-source quantum chemistry dataset, is presented for organic molecular science research. It contains 120,000 molecules and 20 million conformers (10 elements, >40 heavy atoms) with potential energy and forces calculated at the B3LYP/def2-SVP level. Derived from ChEMBL fragments, the dataset is divided into three subsets: A (20 million conformations, >110,000 molecules, equilibrium and near-equilibrium), B (2.4 million conformers, ~1,400 drug molecules, numerous heavy atoms), and C (dissimilar molecules). Benchmark results for potential energy prediction using various models are included. The dataset and code are available on GitHub.
weaknesses: This manuscript and the accompanying database should be submitted to a chemistry journal. The work here is predominantly chemistry focused, primarily quantum physical chemistry. Details of DFT calcuations, the construction of dataset, and the utility of the dataset requires peer-review from experts in quantum chemistry. To highlight this, ANI-1 and QM9 are both published in Scientific Data. PCQM4v2 is published in NeurIPS, however the work is part of a larger body of benchmarks for OGB-LSC, and the DFT datasets were curated from a peer-reviewed chemistry work by Nakata et al. (2017) in Journal of Chemical Information and Modeling. 

The paper provides a lot of details about how the dataset was assembled, but it is unlikely that the anonymous reviewers of this venue would be able to give proper feedback on this; it would be irresponsible to publish this work without proper peer-review. Only the final section 5.3, with Table 2, are there relevant machine learning benchmarking results on some deep models on the dataset. There is also a lot of importance placed on the size of the dataset (Figures 1, 2, 3), but it is not clear whether the additional conformations/fragmentation methods are physically sound or useful for chemical modelling.

Additional issues:
- Table 2 should have caption before the table.
- Figure 6 caption should be below the figure.
- There are gramatical errors that need to be addressed: for example, line 359, "The main subset, referred to as subset A, encompassing the most extensive conformation data, containing 20 million conformations from more than 110,000 molecules."
- A double period in line 350.

Additionally there are other works, not mentioned, that have explored something similar; with conformations, higher precision calculations, property annotations etc. (also published in chemistry related venues): GEOM set by Axelrod et al. (2022), QCDGE by Zhu et al. (2024), and Nabla2DFT and NablaDFT by Khrabrov et al. (2024), VQM24 by Khan et al. (2024).

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confidence: 4
contribution: 2
initial_rating: 3
presentation: 1
questions: 1. The paper provides insufficient detail on the fragmentation / recombination strategy to construct the molecule library. What constitutes a fragment? How were the fragments generated and selected? 
2. Similarly, the rationales and workflows of conformation generation and selection of “stable” conformations should be explained in more detail (at minimum, in the Appendix). 
3. A notable portion of the paper blend descriptions of the dataset and comparisons to existing datasets, with overlapping content in section 2.2 and section 3. A clearer separation of these aspects would improve clarity and aid reader comprehension. 
4. A better distinction between *molecules* and *conformations* would be beneficial for readability of the paper, and for understanding the structure of the dataset. E.g. the analyses in Fig. 5a and Fig. 6 would be more informative on the molecule level (rather than on the conformation level). A plot of average conformation number against heavy atom count, for example, would help contextualize the dataset structure. Similarly, plots of further simple-to-compute topological molecular descriptors (molecular weight, ring counts, …) would add further information. 
5. The purpose of Fig. 5b, showing energy as a function of dihedral angle without specifying the molecule(s) is unclear. From my perspective, this plot, as it stands, is elementary, and provides no insights into the dataset. 
6. An estimate of the computational resources required for dataset generation (in terms of CPU hours), would provide readers with a realistic perspective on the cost, and feasibility of expanding such datasets.
soundness: 2
strengths: 1. High-level reference data on molecular energies and forces are necessary for creating more generalizable ML potential energy surfaces and force fields, with applications e.g. in drug discovery. 
2. The fragmentation–recombination strategy is chemically sensible, ensuring that the included molecules are realistic and feasible.
summary: This paper introduces a computational dataset of small to medium-sized molecules. For 120k molecules, energies and atomic forces were computed for ~170 conformers per molecule. The molecules were generated through fragmentation and recombination of drug-like compounds in the CHEMBL library. The process of database construction (molecule library enumeration, conformer generation, electronic structure calculations) is described. Eventually, the study includes a comparison of established molecular machine learning models applied to in-domain and out-of-domain prediction tasks within this dataset.
weaknesses: The main weaknesses of the paper are significant misinterpretations of core chemistry concepts and a lack of clarity around the dataset’s intended applications, leading to technical ambiguities and misleading claims. 

1. The paper does not clearly articulate the goal of developing the dataset, or potential use cases. Although the data is well-suited for potential energy surface modeling, the introduction implies applications outside of this scope (e.g., structure–activity relationships), which is strongly misleading. Section 2.2 should be re-written to clarify these use cases, and clarify the unclear “conformer generation” task. Moreover, the paper would strongly benefit from repositioning the use cases earlier in the paper to align the introduction with actual practical applications. A number of vague statements regarding applicability should be removed. 

2. The discussion (and application) of quantum chemistry methodologies is severely flawed. 
  * The high-level introduction to DFT misses a number of key points: The main goal is to approximate solutions to the electronic Schrödinger equation for obtaining energies and, possibly, estimate further molecular property labels from the computed solution. 
  * Figure 2 and the accompanying discussion makes strong statements about computation accuracy using different basis sets. However, both the Pople basis sets (6-31G(d) / 6-31G(2df,p)) and the Karlsruhe basis sets (def2-SVP) are double-zeta basis sets with additional polarization functions. To the best of my knowledge, for the molecules studied, the accuracy differences between these basis sets should be negligible. Any claims of significant accuracy differences must be rigorously supported with citations. 
  * Statements like “B3LYP/def2-SVP as one of the highest precision levels achievable“ are strongly misleading. In fact, in computational chemistry, double-zeta basis sets are considered a rather low-level method. Established workflows, such as optimizing geometries with cheap semi-empirical methods, followed by single-point DFT for energies and gradients, are entirely overlooked in generating the dataset. 
3. The discussion of chemistry concepts contains a number of inaccuracies and misinterpretations which weaken the perceived credibility of the study. Examples (non-comprehensive selection) include:
  * Force fields and potential energy surface are often discussed as two disjoint entities – rather than explaining that force fields are formally the derivative of the potential energy surface. 
* SMILES and InChI representations are introduced without clarity on their primary function as molecular graph notations. 
* “Heavy atom is any atom other than hydrogen, typically used in molecular studies to focus on more complex atomic interactions” (p. 3) This statement fails to address why distinguishing heavy atoms is relevant to real-world applications.
* “Labeled as important phosphate groups by our chemistry expert” (p. 5) What is the rationale behind labeling phosphate groups (which are one specific out of many relevant functional groups)? 
* “while the significance of other conformations is also noteworthy” (p. 6) This statement is highly ambiguous. What is the relevance of further (non-minimum) conformations? 

4. The choice of MAE as the error metric for model evaluation overlooks an essential consideration. Since energy scales with molecular size (i.e., number of heavy atoms), the MAE will naturally increase in an out-of-domain prediction task that includes larger molecules. The (in)ability to generalize across chemical space is not reflected in this metric. 

5. The paper uses overly promotional language, particularly in the abstract and introduction. Examples include “unprecedented innovation and efficiency”, “professional and transformative research”, “high-precision […] quantum mechanical level”, “meticulously computed”, “comprehensive studies of structure–property relationships”. These sections should be thoroughly revised.

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confidence: 4
contribution: 1
initial_rating: 3
presentation: 2
questions: - Listed in Cons.
- Provide a comparison with other datasets.
- Provide a detailed comparison between subsets A and B. 
- Add the description of the molecular fragmentation method.
- Typo in line 185: We provides.

[1] Isert, C., Atz, K., Jiménez-Luna, J., & Schneider, G. (2022). QMugs, quantum mechanical properties of drug-like molecules. Scientific Data, 9(1), 273.
[2] Eastman, P., Pritchard, B. P., Chodera, J. D., & Markland, T. E. (2024). Nutmeg and SPICE: Models and data for biomolecular machine learning. Journal of Chemical Theory and Computation.
[3] Khrabrov, K., Ber, A., Tsypin, A., Ushenin, K., Rumiantsev, E., Telepov, A., ... & Kadurin, A. (2024). ∇ DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials. CoRR.
[4] Mathiasen, A., Helal, H., Klaser, K., Balanca, P., Dean, J., Luschi, C., ... & Masters, D. (2023). Generating QM1B with PySCF $ _ {\text {IPU}} $. Advances in Neural Information Processing Systems, 36, 55036-55050.
[5] Nakata, M., Shimazaki, T., Hashimoto, M., & Maeda, T. (2020). PubChemQC PM6: Data sets of 221 million molecules with optimized molecular geometries and electronic properties. Journal of Chemical Information and Modeling, 60(12), 5891-5899.
[6] Hu, W., Fey, M., Ren, H., Nakata, M., Dong, Y., & Leskovec, J. OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
soundness: 1
strengths: - A new dataset with QC properties and a new scheme for conformer generation. 
- A new benchmark for NNPs.
summary: The paper proposes a new dataset and benchmark with quantum chemical (QC) properties of 120,000 organic molecules and over 20 million conformers. The dataset uses moderately accurate physical theory B3LYP/def2-SVP. Moreover, the paper demonstrates the performance of 7 modern Neural Network Potentials on the proposed benchmark.
weaknesses: - Most recent datasets with the same or higher level of theory are not cited and compared with, for example, QMugs [1], SPICE [2], nablaDFT [3], and QM1B [4]. Moreover, the cited PubchemQC [5] dataset contains 86 million structures, while PCQM4Mv2 [6] is its subset.
- The comparison between theory levels is unclear: for example, the level of theory in ANI-1 has more complex exchange correlation potential than in other listed datasets.
- The conformer generation procedure seems undermotivated. Why were 30-degree increments for the angles chosen instead of random or more physically based?
- The dataset composition scheme that involves fragmentation may not be optimal since the molecules that are included in the dataset are the most frequent fragments of bigger molecules. Such dataset may not cover the compound space well enough as it does not contain molecules that are not fragments of larger molecules. This may explain the performance drop between the subset A test set and the subset B.
- Finally, it is not clear how the subset B was generated.  Were these molecules chosen from the initial dataset or taken from another source? Atom and scaffold analysis may reveal severe divergence between A and B.

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confidence: 4
contribution: 1
initial_rating: 3
presentation: 1
questions: Major Issues:
1. The authors need to explain the absence of citation to the nabla^2DFT dataset, which is a significant oversight given its relevance and prior availability.
2. The authors should clearly articulate the unique advantages of the QO2Mol database compared to the nabla^2DFT dataset
3. The limited scope of benchmark results requires further explanation. The focus solely on potential prediction experiments in the main manuscript seems insufficient for a benchmark dataset paper of this nature.
4. Although the authors emphasize the high precision of B3LYP, the choice still requires justification. B3LYP's performance may degrade with non-covalent and long-range interactions, which can be present in organic molecules. The authors should explain why B3LYP was chosen over ωB97X-D, which potentially offers better precision.

Minor Editorial Issues:
1. In the appendix section, there are typos such as 'inffuence' (should be 'influence') and 'signiffcant' (should be 'significant'), which diminishes the professionalism of the paper.
2. The spelling of ChEMBL is inconsistent throughout the paper, appearing as both "ChemBL" (appendix page 16) and "ChEMBL" (elsewhere). This inconsistency should be corrected for clarity.
soundness: 2
strengths: The strength of this paper may come from the large number of organic molecules included in the QO2Mol database and its diverse coverage of elements. Specifically, the database includes molecules with heavy atom counts exceeding 40, representing an improvement over existing datasets like QM9 and ANI-1. Another positive aspect is the inclusion of tasks related to conformer generation.
summary: This paper presents a benchmark dataset, named the Quantum Open Organic Molecular (QO2Mol) database, which contains 120,000 organic molecules and approximately 20 million conformers. The authors highlight that this dataset covers 10 different elements, (C, H, O, N, S, P, F, Cl, Br, I) with heavy atom counts exceeding 40. The quantum mechanical properties of the organic molecules were computed using the B3LYP/def2-SVP calculation method, which is claimed to provide accurate computations of properties such as potential energy and forces. The paper presents benchmark results for potential energy prediction, evaluating the performance of both invariant and equivariant models.
weaknesses: The most significant weakness of this paper is its fundamental lack of novelty when compared to the existing dataset in this chemistry field. The existing nabla^2DFT dataset [1] contains 2 million molecules, which is more than the 120,000 molecules in this QO2Mol database, and includes 16 million conformers, a number comparable to this work. The comparison becomes even more unfavorable when considering the depth of information provided. The nabla^2DFT dataset offers 17 different molecular properties, along with Hamiltonian and overlap matrices, and a wavefunction object - features that are not provided in this QO2Mol database. Additionally, the nabla^2DFT dataset includes relaxation trajectories for a significant number of organic molecules, another feature not present in this paper. Given that the nabla^2DFT dataset was made available online in June 2024, well before the ICLR submission deadline, it is unclear what distinct advantages the QO2Mol database offers over the existing nabla^2DFT dataset. Furthermore, this paper presents a significant lack of benchmark results, only providing potential energy prediction benchmarks in the main manuscript, which limits its scope and impact.

[1] Khrabrov, Kuzma, et al. "$\nabla^ 2$ DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials." arXiv preprint arXiv:2406.14347 (2024).

Meta-review: This submission to the datasets and benchmarks track introduces QO2Mol, a computational dataset of small to medium-sized molecules. Reviewers expressed concerns on the presentation and the significance of the presented work. 

The authors may also need to seriously consider how the presented data may be organized and accompanied with machine learning methods that may help advance molecular forward property prediction or generative design research.

Decision: Reject