Ablation study results on predictor modules.

Accurate prediction of molecular properties is a key component of Artificial Intelligence-driven Drug Design (AIDD). Despite significant progress in improving these predictive models, balancing accuracy with computational complexity remains a challenge. Molecular topological and geometric features provide rich spatial information, crucial for improving prediction accuracy, but their extraction typically increases model complexity. To address this, we propose TGF-M (Topology-augmented Geometric Features for Molecular Property Prediction), a novel predictive model that optimizes feature extraction to enhance information capture and improve model accuracy, and reduces model complexity to lower computational cost. This approach enhances the model’s ability to leverage both topological and geometric features without unnecessary complexity. On the re-segmented PCQM4Mv2 dataset, TGF-M performs remarkably, achieving a low mean absolute error (MAE) of 0.0647 in the HOMO-LUMO gap prediction task with only 6.4M parameters. Compared to two recent state-of-the-art models evaluated within a unified validation framework, TGF-M demonstrates comparable performance with less than one-tenth of the parameters. We conducted an in-depth analysis of TGF-M’s chemical interpretability. The results further validate the method’s effectiveness in leveraging complex molecular topology and geometry during model learning, underscoring its potential and advantages. The trained models and source code of TGF-M are publicly available at https://github.com/TiAW-Go/TGF-M.精准预测分子性质是人工智能驱动药物研发（Artificial Intelligence-driven Drug Design, AIDD）的核心组成部分。尽管在优化这类预测模型方面已取得显著进展，但在预测精度与计算复杂度之间取得平衡仍是一项挑战。分子拓扑与几何特征可提供丰富的空间信息，对提升预测精度至关重要，但其提取过程通常会增加模型的复杂度。针对这一问题，本文提出了TGF-M（分子性质预测的拓扑增强几何特征，Topology-augmented Geometric Features for Molecular Property Prediction）——一种新型预测模型，其通过优化特征提取流程以强化信息获取能力、提升模型精度，同时降低模型复杂度以减少计算开销。该方法在无需引入冗余复杂度的前提下，增强了模型对拓扑与几何特征的利用能力。在经过重新分割的PCQM4Mv2数据集上，TGF-M表现优异：仅需6.4M参数量，便可在HOMO-LUMO能隙预测任务中实现低至0.0647的平均绝对误差（Mean Absolute Error, MAE）。在统一验证框架下与两款近期的当前最优模型进行对比后，TGF-M仅用不到十分之一的参数量，便实现了相当的性能表现。本文还对TGF-M的化学可解释性展开了深入分析，实验结果进一步验证了该方法在模型学习过程中利用复杂分子拓扑与几何特征的有效性，凸显了其应用潜力与优势。TGF-M的训练完成模型与源代码已在https://github.com/TiAW-Go/TGF-M公开获取。