GEOM, energy-annotated molecular conformations for property prediction and molecular generation

Axelrod, Simon; Gómez‐Bombarelli, Rafael

doi:10.1038/s41597-022-01288-4

“…Datasets and setup Following the settings in previous works [43,52], we use GEOM-QM9 and GEOM-Drugs [53] dataset to perform conformation generation experiments. As described in Sec.…”

Section: Molecular Conformation Generationmentioning

confidence: 99%

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

Zhou

¹

,

Gao

²

,

Ding

³

et al. 2022

Preprint

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Molecular representation learning (MRL) has gained tremendous attention due to its critical role in learning from limited supervised data for applications like drug design. In most MRL methods, molecules are treated as 1D sequential tokens or 2D topology graphs, limiting their ability to incorporate 3D information for downstream tasks and, in particular, making it almost impossible for 3D geometry prediction or generation. Herein, we propose Uni-Mol, a universal MRL framework that significantly enlarges the representation ability and application scope of MRL schemes. Uni-Mol is composed of two models with the same SE(3)-equivariant transformer architecture: a molecular pretraining model trained by 209M molecular conformations; a pocket pretraining model trained by 3M candidate protein pocket data. The two models are used independently for separate tasks, and are combined when used in protein-ligand binding tasks. By properly incorporating 3D information, Uni-Mol outperforms SOTA in 14/15 molecular property prediction tasks. Moreover, Uni-Mol achieves superior performance in 3D spatial tasks, including protein-ligand binding pose prediction, molecular conformation generation, etc. Finally, we show that Uni-Mol can be successfully applied to the tasks with few-shot data like pocket druggability prediction. The model and data will be made publicly available at \url{https://github.com/dptech-corp/Uni-Mol}

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“…Datasets and setup Following the settings in previous works [44,53], we use GEOM-QM9 and GEOM-Drugs [54] dataset to perform conformation generation experiments. As described in Sec.…”

Section: Molecular Conformation Generationmentioning

confidence: 99%

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

Zhou

¹

,

Gao

²

,

Ding

³

et al. 2022

Preprint

View full text Add to dashboard Cite

Molecular representation learning (MRL) has gained tremendous attention due to its critical role in learning from limited supervised data for applications like drug design. In most MRL methods, molecules are treated as 1D sequential tokens or 2D topology graphs, limiting their ability to incorporate 3D information for downstream tasks and, in particular, making it almost impossible for 3D geometry prediction or generation. Herein, we propose Uni-Mol, a universal MRL framework that significantly enlarges the representation ability and application scope of MRL schemes. Uni-Mol is composed of two models with the same SE(3)-equivariant transformer architecture: a molecular pretraining model trained by 209M molecular conformations; a pocket pretraining model trained by 3M candidate protein pocket data. The two models are used independently for separate tasks, and are combined when used in protein-ligand binding tasks. By properly incorporating 3D information, Uni-Mol outperforms SOTA in 14/15 molecular property prediction tasks. Moreover, Uni-Mol achieves superior performance in 3D spatial tasks, including protein-ligand binding pose prediction, molecular conformation generation, etc. Finally, we show that Uni-Mol can be successfully applied to the tasks with few-shot data like pocket druggability prediction. The model and data will be made publicly available at \url{https://github.com/dptech-corp/Uni-Mol}

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“…2.2 play a crucial role in identifying some important molecular structures, such as conformers. In nature, a real molecule exits as an ensemble of interconventing 3D structures, known as conformers [5,4,18]. Different conformers posses the same 2D molecular graph, but differ in 3D structures.…”

Section: Rotation Angles For Conformer Identificationmentioning

confidence: 99%

ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs

Wang¹,

Liu²,

Lin³

et al. 2022

Preprint

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Many real-world data can be modeled as 3D graphs, but learning representations that incorporates 3D information completely and efficiently is challenging. Existing methods either use partial 3D information, or suffer from excessive computational cost. To incorporate 3D information completely and efficiently, we propose a novel message passing scheme that operates within 1-hop neighborhood. Our method guarantees full completeness of 3D information on 3D graphs by achieving global and local completeness. Notably, we propose the important rotation angles to fulfill global completeness. Additionally, we show that our method is orders of magnitude faster than prior methods. We provide rigorous proof of completeness and analysis of time complexity for our methods. As molecules are in essence quantum systems, we build the complete and efficient graph neural network (ComENet) by combing quantum inspired basis functions and the proposed message passing scheme. Experimental results demonstrate the capability and efficiency of ComENet, especially on real-world datasets that are large in both numbers and sizes of graphs. Our code is publicly available as part of the DIG library (https://github.com/divelab/DIG). * Equal contribution Preprint. Under review.

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GEOM, energy-annotated molecular conformations for property prediction and molecular generation

Cited by 139 publications

References 83 publications

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs

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