AIMNET-X2D: Universal and Scalable Framework for Chemical Property Transfer Learning

<p dir="ltr">Accurate molecular property prediction is fundamental to drug discovery and materials science, where rapidly expanding datasets now challenge existing computational approaches. We present AIMNet-X2D, a highly scalable Graph Neural Network framework that processes molecular datasets of unprecedented size while maintaining state-of-the-art accuracy. By overcoming critical bottlenecks that have limited previous architectures, AIMNet-X2D achieves exceptional scalability through three key innovations: (1) optimized multi-hop edge precomputation, (2) an explicit shell convolution mechanism that processes multiple structural contexts simultaneously, and (3) dynamic attention based weighting that captures both local and long-range molecular interactions. Benchmarked on datasets of unprecedented size—up to 20 million molecules—AIMNet-X2D demonstrates near-linear scalability (R2 ¿ 0.99) that enables efficient processing of chemical datasets orders of magnitude larger than previously feasible. The framework achieves excellent predictive performance across diverse tasks, including quantum mechanical properties (QM9, R2 = 0.97), physical properties (Tetko melting points, R2 = 0.77), and pharmacological properties (LogP, R2 = 0.93). Our framework transforms high-throughput virtual screening capabilities and accelerates AI-guided discovery across pharmaceuticals, materials science, and beyond by enabling chemical AI to scale in ways previously thought unattainable.</p>