ENTERPRISE AI ANALYSIS
An equivariant pretrained transformer for unified 3D molecular representation learning
This paper introduces Equivariant Pretrained Transformer (EPT), an all-atom foundation model for molecular representation learning. EPT is pretrained on a diverse dataset of 3D molecules (small molecules, proteins, and complexes) using an E(3)-equivariant transformer. It learns both atom-level interactions and graph-level structural features via a block-level denoising pretraining strategy. EPT achieves state-of-the-art or competitive results in ligand binding affinity prediction, mutation stability prediction, and molecular property prediction. It also successfully identifies potential anti-COVID-19 compounds through virtual screening and experimental validation.
Executive Impact Summary
Molecular modeling is crucial for drug discovery and material design. EPT's unified approach can accelerate these processes by providing a generalizable framework across diverse molecular systems, reducing the need for domain-specific models and extensive labeled data.
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Predicted Kd for Ac-Leu-Leu-Nle-CHO
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Predicted Kd for Saquinavir
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MAE for QM9 MPP Task (EPT-10)
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AUROC on Atom3D for MSP Task (EPT-MultiDomain)
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Model Architecture
Pretraining Strategy
Performance & Applications
Insights on Model Architecture
EPT is an all-atom foundation model for multiple domains including small molecules, proteins, and complexes (Fig. 1a). EPT creates a unified molecular representation across domains by defining 'blocks' of atoms.
To effectively capture the geometry of molecular structures, EPT employs an improved Transformer architecture that integrates E(3) symmetry as its backbone model (Fig.1c).
The self-attention layer plays a crucial role in modeling interatomic interactions. For each layer, query Qs, key Ks, and value Vs matrices of the s-th head are computed.
The model is constructed in this way: [H(0), V(0)] = Embedding(A, B, P, Z), [H(1-0.5), V(1-0.5)] = [H(1-1), V(1-1)] + Self-Attn(LN(H(1-1)), V(1-1)), [H(1), V(1)] = [H(1-0.5), V(1-0.5)] + FFN(LN(H(1-0.5)), V(1-0.5)).
EPT's Unified Molecular Representation Process
Input Diverse 3D Molecules
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Define 'Blocks' (Heavy Atoms/Amino Acids)
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E(3)-Equivariant Transformer Processing
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Unified Molecular Representation
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Apply to Downstream Tasks
Insights on Pretraining Strategy
To encode hierarchical molecular information, we introduce a block-level denoising pretraining strategy that integrates both translational and rotational perturbations applied to molecular blocks. This approach considers blocks as rigid bodies.
The pretraining objective incorporates two components. First, a translation objective minimizes the discrepancy between predicted resultant forces and translational noises averaged over blocks.
Second, a rotation objective aligns angular accelerations derived from predicted torques with gradients of the rotational noise distribution.
We first pretrain the backbone model on the collected multi-domain dataset based on the objective Lblock-C, leveraging 8 NVIDIA Tesla A800 GPUs. The pretraining process spans 50 epochs.
5.89M+
Molecular Entries in Pretraining Dataset
Insights on Performance & Applications
For Ligand Binding Affinity (LBA) prediction, EPT outperforms existing methods on benchmarks with varying protein sequence similarities (30% and 60%).
For the Mutation Stability Prediction (MSP) task, EPT showcases robust generalizability, significantly outperforming existing domain-specific models.
In Table 1, our EPT outperforms or matches the performance of existing denoising-based methods, underscoring the effectiveness of multi-domain block-level pretraining.
The model successfully identifies known anti-COVID-19 drugs as top candidates, with further t-SNE analysis revealing that these drugs clustered closely with other high-affinity candidates predicted by EPT.
| Task | EPT Performance | Previous SOTA |
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| Ligand Binding Affinity Prediction (LBA) |
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| Mutation Stability Prediction (MSP) |
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| Molecular Property Prediction (MPP) |
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Anti-COVID-19 Drug Discovery with EPT
EPT was applied to virtual screening of FDA-approved drugs for binding potential to the 3CL protease (SARS-CoV-2 target).
- Known Anti-COVID-19 Drugs Identified: EPT successfully ranked known anti-COVID-19 drugs as top candidates (within top 200).
- Discovery of Promising Candidates: Two strong hits, Ac-Leu-Leu-Nle-CHO and Saquinavir, were identified with high predicted binding affinities (Kd 87.4 nM and 24.5 nM respectively).
- Experimental Validation: Both Ac-Leu-Leu-Nle-CHO (IC50 5.47 μM) and Saquinavir (IC50 9.92 μM) showed inhibitory potency, validating EPT's predictive power.
- Acceleration of Drug Discovery: This demonstrates EPT's potential to accelerate the identification of promising drug candidates.
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Phased Implementation Roadmap
A structured approach to integrating EPT into your R&D, ensuring a smooth transition and maximizing impact.
Phase 1: Discovery & Integration
Initial assessment of your current molecular modeling pipelines and integration of EPT for baseline tasks. (Weeks 1-4)
Phase 2: Customization & Fine-tuning
Tailoring EPT's pretraining and downstream task heads to your specific research needs and datasets. (Weeks 5-12)
Phase 3: Advanced Deployment & Optimization
Full-scale deployment of EPT for novel drug discovery or material design, with continuous performance monitoring and optimization. (Months 3+)
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