Conformer datasets for "Equivariant Graph Neural Networks for Toxicity Prediction"

Predictive modeling of toxicity is a crucial step in the drug discovery pipeline. It can help filter out molecules with a high probability of failing in the early stages of de novo drug design. Thus, several machine learning (ML) models have been developed to predict the toxicity of molecules by combining classical ML techniques or deep neural networks with well-known molecular representations such as fingerprints or 2D graphs. But the more natural, accurate representation of molecules is expected to be defined in physical 3D space like in ab initio methods. Recent studies successfully used equivariant graph neural networks (EGNNs) for representation learning based on 3D structures to predict quantum-mechanical properties of molecules. Inspired by this, we investigated the performance of EGNNs to construct reliable ML models for toxicity prediction. We used the equivariant transformer (ET) model in TorchMD-NET for this. Eleven toxicity data sets taken from MoleculeNet, TDCommons, and ToxBenchmark have been considered to evaluate the capability of ET for toxicity prediction. Our results show that ET adequately learns 3D representations of molecules that can successfully correlate with toxicity activity, achieving good accuracies on most data sets comparable to state-of-the-art models. We also test a physicochemical property, namely, the total energy of a molecule, to inform the toxicity prediction with a physical prior. However, our work suggests that these two properties can not be related. We also provide an attention weight analysis for helping to understand the toxicity prediction in 3D space and thus increase the explainability of the ML model. In summary, our findings offer promising insights considering 3D geometry information via EGNNs and provide a straightforward way to integrate molecular conformers into ML-based pipelines for predicting and investigating toxicity prediction in physical space. We expect that in the future, especially for larger, more diverse data sets, EGNNs will be an essential tool in this domain. PAPER https://pubs.acs.org/doi/full/10.1021/acs.chemrestox.3c00032 CODE and MODELS: The conformer data sets and trained toxicity models will be published upon acceptance of this work. The code has been made available at https://github.com/jule-c/ET-Tox, and the processed data as well as pretrained models for training and testing can be downloaded from https://zenodo.org/record/7942946. We can provide the full list of conformers as XYZ files upon request.毒性预测建模是药物研发管线中的关键环节，可帮助筛选出在从头药物设计早期阶段极有可能失败的分子。为此，已有诸多机器学习（ML）模型被开发出来，用于预测分子毒性：这类模型将经典机器学习技术或深度神经网络，与分子指纹、二维图等主流分子表征方式相结合。但更为自然且精准的分子表征，理应如从头算（ab initio）方法那般，基于物理层面的三维空间进行定义。近期已有研究成功利用等变图神经网络（EGNNs）开展基于三维结构的表征学习，以预测分子的量子力学性质。受此启发，本研究探索了利用EGNNs构建可靠的机器学习毒性预测模型的性能，具体采用了TorchMD-NET中的等变Transformer（ET）模型。本研究选用了来自MoleculeNet、TDCommons及ToxBenchmark的11个毒性数据集，以评估ET在毒性预测任务中的性能表现。研究结果表明，ET能够有效学习与毒性活性显著相关的分子三维表征，在多数数据集上均取得了可与当前最先进模型媲美的优异精度。此外，本研究还测试了一项物理化学性质——分子总能量，尝试通过物理先验辅助毒性预测，但结果显示这两类性质之间并无关联。本研究还开展了注意力权重分析，以帮助理解三维空间下的毒性预测机制，进而提升该机器学习模型的可解释性。综上，本研究通过EGNNs引入三维几何信息，所得结果为毒性预测任务提供了极具前景的研究思路，并提供了一种简便的方式，可将分子构象整合至基于机器学习的研发管线中，以在物理空间中开展毒性预测与相关研究。我们预期，未来尤其是在处理规模更大、多样性更强的数据集时，EGNNs将成为该领域的核心工具。 论文 https://pubs.acs.org/doi/full/10.1021/acs.chemrestox.3c00032 代码与模型： 本研究的构象数据集与训练完成的毒性预测模型将在论文录用后公开。相关代码已上传至https://github.com/jule-c/ET-Tox，用于训练与测试的预处理数据及预训练模型可从https://zenodo.org/record/7942946下载。如有需要，我们可提供以XYZ格式存储的全部构象列表。