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<p>Experimental profiling of drug–cell transcriptomic responses over dose and time is sparse and irregular, complicating discovery. We present ExPO, an exposure-conditioned neural operator that predicts full L1000 (978-gene) z-score signatures for a given compound–cell context as a continuous function of exposure. ExPO ifanstantiates a DeepONet, fusing ChemBERTa-2 molecular embeddings (with LoRA adaptation) and a trunk over sinusoidal Fourier features, so signatures can be evaluated at arbitrary dose–time pairs without regridding. Training is discovery-aligned, combining robust regression with a two-list listwise objective to optimize early-rank gene ordering, plus lightweight priors (Sobolev smoothness and an optional dose-monotonicity penalty) for pharmacologic plausibility. On a scaffold-held-out CMap/L1000 benchmark, ExPO improves over strong baselines in both accuracy and ranking (MAE 0.83 vs 0.89, Spearman 0.52 vs 0.48 vs DeepCE; NDCG@50↑/↓ 0.74/0.72 vs 0.71/0.69 for CIGER), tolerates withheld exposures (ΔMAE + 0.03 for interior interpolation; + 0.07 at edges, reduced by − 0.02 with the monotonicity prior), and transfers across cell lines (LCL-O MAE 0.90 vs 0.95; ρ 0.44 vs 0.40). Quantile heads with conformal calibration yield reliable uncertainty (test PICP 0.80/0.90), and filtering the least-confident 20% reduces MAE by ~ 18%. By modeling exposure as a field rather than discrete buckets, ExPO delivers accurate, rank-faithful, and calibrated signatures for in-silico exploration of dose–time surfaces. ExPO as a standalone predictor, along with its source code and benchmark dataset, is available at <a href="https://github.com/MLBC-lab/ExPO">https://github.com/MLBC-lab/ExPO</a>.&#xa0;</p>

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ExPO: an exposure-conditioned neural operator for L1000 signature prediction

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  • 发表日期：

    2026年5月21日

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  • Austin Spadaro,
  • Alok Sharma,
  • Iman Dehzangi

摘要

Experimental profiling of drug–cell transcriptomic responses over dose and time is sparse and irregular, complicating discovery. We present ExPO, an exposure-conditioned neural operator that predicts full L1000 (978-gene) z-score signatures for a given compound–cell context as a continuous function of exposure. ExPO ifanstantiates a DeepONet, fusing ChemBERTa-2 molecular embeddings (with LoRA adaptation) and a trunk over sinusoidal Fourier features, so signatures can be evaluated at arbitrary dose–time pairs without regridding. Training is discovery-aligned, combining robust regression with a two-list listwise objective to optimize early-rank gene ordering, plus lightweight priors (Sobolev smoothness and an optional dose-monotonicity penalty) for pharmacologic plausibility. On a scaffold-held-out CMap/L1000 benchmark, ExPO improves over strong baselines in both accuracy and ranking (MAE 0.83 vs 0.89, Spearman 0.52 vs 0.48 vs DeepCE; NDCG@50↑/↓ 0.74/0.72 vs 0.71/0.69 for CIGER), tolerates withheld exposures (ΔMAE + 0.03 for interior interpolation; + 0.07 at edges, reduced by − 0.02 with the monotonicity prior), and transfers across cell lines (LCL-O MAE 0.90 vs 0.95; ρ 0.44 vs 0.40). Quantile heads with conformal calibration yield reliable uncertainty (test PICP 0.80/0.90), and filtering the least-confident 20% reduces MAE by ~ 18%. By modeling exposure as a field rather than discrete buckets, ExPO delivers accurate, rank-faithful, and calibrated signatures for in-silico exploration of dose–time surfaces. ExPO as a standalone predictor, along with its source code and benchmark dataset, is available at https://github.com/MLBC-lab/ExPO.