MEGA-CL：通过图外部注意力和对比学习实现可泛化ADMET预测的分子基础模型
MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning
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中文总结 AI 辅助
该研究针对小分子ADMET预测难题，提出MEGA-CL框架，集成自监督对比学习等技术，能同时建模局部与全局关系并减轻过平滑。它在多数据集和任务中优于基线模型，在回归任务及外部验证中表现出色，还通过实验验证了其在药物研发中的潜力。
中文摘要 AI 辅助
预测小分子的吸收、分布、代谢、排泄和毒性（ADMET）特性仍然是药物发现中的一项重大挑战。在此，我们提出了MEGA-CL，这是一个用于通用分子ADMET预测的基础图神经网络框架。MEGA-CL将自监督对比学习与多头外部注意力机制和增强的消息传递架构相结合，能够同时对局部化学子结构和全局图间关系进行建模，同时减轻深度图网络中常见的过平滑效应。在13个基准数据集和21个下游ADMET任务中，MEGA-CL始终优于当前最先进的基线模型。该框架在具有挑战性的回归任务上表现出强大性能，在独立外部验证中保持了较强的泛化能力。临床相关的预测准确性得以实现，超过75%的预测误差在3倍以内。在对18种源自近期获批FDA药物的新型化合物的外部评估中，超过50%的人肝微粒体清除率（HLMC）预测误差在2倍以内。为进一步评估其实际适用性，使用体外肝微粒体代谢试验和模型预测指导的CYP450抑制试验对三种临床前候选药物进行了前瞻性评估。所有候选药物的预测HLMC值在实验测量值的2.5倍以内，73.（3%）的CYP450抑制终点（11/15）被正确分类。这些结果证明了MEGA-CL作为加速计算机辅助ADMET评估和早期候选药物优化的可泛化框架的潜力。
英文摘要
Predicting the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small molecules remains a major challenge in drug discovery. Here, we present MEGA-CL, a foundation graph neural network framework for universal molecular ADMET prediction. MEGA-CL integrates self-supervised contrastive learning with a multi-head external attention mechanism and an enhanced message-passing architecture, enabling simultaneous modeling of local chemical substructures and global inter-graph relationships while mitigating over-smoothing effects commonly observed in deep graph networks. Across 13 benchmark datasets and 21 downstream ADMET tasks, MEGA-CL consistently outperforms state-of-the-art baseline models. In particular, the framework demonstrates robust performance on challenging regression tasks, including clearance (CL) and steady-state volume of distribution (VDss), while maintaining strong generalization ability in independent external validation. Clinically relevant predictive accuracy was achieved, with more than 75% of predictions falling within a 3-fold error range. In an external evaluation on 18 novel compounds derived from recently approved FDA drugs, over 50% of human liver microsome clearance (HLMC) predictions were within a 2-fold error range. To further assess its practical applicability, MEGA-CL was prospectively evaluated on three preclinical drug candidates using in vitro hepatic microsomal metabolism assays and CYP450 inhibition assays guided by model predictions. The predicted HLMC values for all candidates were within 2.5-fold of the experimentally measured values, and 73.3% of CYP450 inhibition endpoints (11/15) were correctly classified. These results demonstrate the potential of MEGA-CL as a generalizable framework for accelerating in silico ADMET evaluation and early-stage drug candidate optimization.
