PCAM: proteochemometric assay-aware modeling for robust bioactivity estimation and uncertainty quantification
Abstract
Public bioactivity databases aggregate measurements from heterogeneous experimental assays, introducing systematic variability that can degrade model performance and distort uncertainty estimates. We present proteochemometric assay-aware modeling (PCAM), an extension of proteochemometrics that integrates assay descriptors alongside compound and protein features to capture assay context. Using a curated ChEMBL-derived benchmark spanning 167 G protein-coupled receptor targets, 77,081 compounds, and 5,161 assays, we compare models that omit assay information to models using either categorical assay identifiers or text-derived embeddings from assay descriptions. We evaluate generalization under three regimes of increasing distribution shift (random, scaffold-based, and assay-held-out splits) and assess both point accuracy and probabilistic quality. Overall difficulty is dominated by the split strategy, with assay-held-out evaluation inducing the strongest distribution shift. When assay identifiers are shared between training and evaluation, categorical assay descriptors provide the most consistent gains in accuracy and probabilistic scoring relative to models without assay context. Under assay-held-out evaluation, differences between assay representations contract, while text-derived embeddings show a consistent but typically small advantage. This regime corresponds to practical assay-transfer scenarios, such as protocol or vendor changes, new readouts, or sparse early measurements for a newly introduced assay, where leveraging assay text/metadata can help transfer signal from related assays. In addition, assay representations can reduce reliance on brittle cross-assay normalization in heterogeneous public datasets by allowing the model to learn assay-dependent shifts directly; however, we observed optimization instability for one configuration (a probabilistic neural network using BioBERT embeddings under the random split). We introduce an assay-aware extension of proteochemometrics that explicitly models assay context and evaluate it under increasing distribution shift. We clarify when simple assay identifiers suffice and when text-derived assay representations better support transfer to unseen assay protocols while maintaining uncertainty-aware evaluation.
Authors 6
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Leiden University · Janssen (Belgium)
Affiliation as printed
Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2300 RA, Leiden, The Netherlands
In Silico Discovery (ISD), Janssen Research and Development, LLC, a Johnson & Johnson company, Beerse, 2340, Belgium
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David Alencar Araripe Aachen Department of Human Genetics Division of Medicinal Chemistry Leiden Academic Centre for Drug Research
Leiden University · Leiden University Medical Center
Affiliation as printed
Department of Human Genetics, Leiden University Medical Centre (LUMC), Leiden, 2300 RC, The Netherlands
Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2300 RA, Leiden, The Netherlands
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Affiliation as printed
Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2300 RA, Leiden, The Netherlands
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Affiliation as printed
In Silico Discovery (ISD), Janssen Research and Development, LLC, a Johnson & Johnson company, Beerse, 2340, Belgium
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Leiden University · Janssen (Belgium)
Affiliation as printed
Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2300 RA, Leiden, The Netherlands
In Silico Discovery (ISD), Janssen Research and Development, LLC, a Johnson & Johnson company, Beerse, 2340, Belgium
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Gerard J. P. van Westen corresponding Aachen Division of Medicinal Chemistry Leiden Academic Centre for Drug Research
Affiliation as printed
Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2300 RA, Leiden, The Netherlands