Importance-Sampling Estimation of Gaussian Molecular Shape Overlap: Exact Union Volumes and Confidence-Bounded Virtual Screening

Abstract

Gaussian descriptions of molecular shape underpin 3D shape-based virtual screening, but existing methods evaluate Gaussian overlap analytically. The widely used first-order approximation is fast but systematically overestimates overlap, whereas the exact molecular volume requires a combinatorial inclusion-exclusion expansion. We introduce the first stochastic estimator of Gaussian shape overlap: an unbiased Monte Carlo method that importance-samples directly from a molecule's Gaussian mixture. The estimator reproduces analytic overlap without bias and extends to the exact union volume of all inclusion-exclusion orders with O(N) cost per sample. On drug-like molecules, the union estimator matches high-resolution grid quadrature with a mean relative error of 0.07 percent, while the first-order approximation overestimates the true union volume by 3.4x on average. The estimator provides analytic standard errors, enabling confidence-bounded screening that reduces sampling by 94 percent while preserving ranking. Implemented in JAX, it is fully differentiable and supports gradient-based rigid alignment on CPU, GPU, and TPU. On the DUD-E and LIT-PCBA benchmarks, the method achieves shape-only enrichment comparable to existing single-conformer approaches while additionally providing unbiased absolute volumes and uncertainty estimates.