Equivariant Pretrained Transformer for Unified Geometric Learning on Multi-Domain 3D Molecules
(2402.12714)Abstract
Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications. However, prior efforts typically focus on pretraining models on a specific domain, either proteins or small molecules, missing the opportunity to leverage the cross-domain knowledge. To mitigate this gap, we introduce Equivariant Pretrained Transformer (EPT), a novel pretraining framework designed to harmonize the geometric learning of small molecules and proteins. To be specific, EPT unifies the geometric modeling of multi-domain molecules via the block-enhanced representation that can attend a broader context of each atom. Upon transformer framework, EPT is further enhanced with E(3) equivariance to facilitate the accurate representation of 3D structures. Another key innovation of EPT is its block-level pretraining task, which allows for joint pretraining on datasets comprising both small molecules and proteins. Experimental evaluations on a diverse group of benchmarks, including ligand binding affinity prediction, molecular property prediction, and protein property prediction, show that EPT significantly outperforms previous SOTA methods for affinity prediction, and achieves the best or comparable performance with existing domain-specific pretraining models for other tasks.
Overview
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The Equivariant Pretrained Transformer (EPT) offers a unified geometric learning framework for different 3D molecular structures, aiming to enhance molecular representation in scientific fields like drug discovery.
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EPT utilizes a transformer architecture with E(3) equivariance and a block-enhanced representation technique to enrich atom and residue-level features for a better understanding of 3D molecular geometry.
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Experimental tests show EPT outperforms existing methods in ligand binding affinity prediction and performs well in molecular and protein property prediction tasks.
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The development of EPT marks a stride toward creating models that accurately represent molecular structures, encouraging future exploration into cross-domain knowledge transfer and scalability to larger systems.
Equivariant Pretrained Transformer for Multi-Domain 3D Molecular Learning
In the recent surge of advancements in deep learning applications for scientific research, the accurate representation and understanding of molecular structures have emerged as a pivotal area, particularly due to its relevance in drug discovery, materials science, and biochemistry. Traditional approaches often constrain their focus to specific domains, either proteins or small molecules, neglecting the enriching potential of cross-domain knowledge sharing. Addressing this research gap, the newly introduced Equivariant Pretrained Transformer (EPT) proposes a novel framework designed for unified geometric learning across different domains of 3D molecular structures.
Introduction to EPT
EPT stands out by its innovative employment of a block-enhanced representation technique that enriches each atom’s context by aligning atom-level and residue-level features. Built upon a transformer architecture, EPT integrates E(3) equivariance, enabling it to capture the 3D structure more accurately than traditional methods. A notable breakthrough in this research is the block-level denoising pretraining task, which allows for a more nuanced understanding of the complex hierarchical geometry inherent in 3D molecules.
Experimental Evaluation
EPT's performance was rigorously tested against a variety of benchmarks in the fields of ligand binding affinity prediction, molecular property prediction, and protein property prediction. The results affirm EPT’s capacity to significantly outperform state-of-the-art methods in affinity prediction, while achieving comparable or superior performance in other tasks, evidencing its robust applicability across different molecular domains.
Technical Insights and Analysis
The study offers profound technical insights into the components contributing to EPT's performance. Key findings include:
- Enhancing atom features with block-level information slightly elevates performance, suggesting the effectiveness of incorporating broader atom context.
- The integration of distance matrices and edge features into the attention mechanism underpins the model's ability to encapsulate diverse interatomic relations effectively.
- The block-level denoising strategy adopted by EPT positively impacts its ability to capture broader molecular dynamics, as illustrated by its superior performance across various molecular benchmarks.
Implications and Future Directions
The development of EPT initiates a promising direction towards the creation of generalizable and accurate models for molecular representation learning. It paves the way for further exploration into the benefits of cross-domain knowledge transfer and the development of unified models capable of understanding the universal principles governing molecular structures. Future research could focus on enhancing the scalability of EPT to larger molecular systems and extending its applicability to encompass more diverse scientific domains.
In conclusion, the Equivariant Pretrained Transformer (EPT) sets a new benchmark in the field of 3D molecular learning, demonstrating an unprecedented level of performance across multiple domains. Its innovative approach to pretraining and geometric representation holds promising potential for revolutionizing how molecular systems are modeled and understood in computational research.
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