Papers for

chemical safety assessors

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

EnviGraph generates synthetic data to improve biodegradation prediction

Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction

Abstract: The scarcity of observed biodegradation reactions constrains the training of product and pathway predictors. We present enviGraph, a framework that generates and curates synthetic reaction data, uses these data to pretrain a graph-to-sequence predictor, and fine-tunes the predictor on observed environmental reactions for product and recursive pathway prediction. Applying inherited environmental transformation rules to structures from PubChem, NORMAN SusDat and EPA DSSTox, followed by curation, produces 776,851 canonical parent-product pairs for pretraining. We compare this dataset with three United States Patent and Trademark Office (USPTO) reaction datasets: USPTO-50K, USPTO-MIT and USPTO-Stereo. We evaluate enviGraph and the sequence-based enviFormer using ten folds split by pathway on the EAWAG Biocatalysis/Biodegradation Database (BBD), regulatory pesticide biodegradation data (Soil) and activated-sludge biodegradation data (Sludge). For both predictors, synthetic pretraining gives the highest BBD mean precision-recall area under the curve (PR-AUC) among the evaluated pretraining datasets for single-generation and multi-generation prediction. enviGraph achieves BBD single-generation PR-AUC of $0.202 \pm 0.050$ with synthetic pretraining, compared with $0.169 \pm 0.065$ for the retrospectively selected strongest USPTO condition, USPTO-Stereo (mean $\pm$ sample standard deviation across folds). Its exact BBD top-1 and top-5 accuracies with synthetic pretraining are 0.355 and 0.594. The strongest USPTO conditions give higher PR-AUC means than synthetic pretraining in most Soil and Sludge comparisons. enviGraph has higher Soil multi-generation PR-AUC means than enviFormer under each of the four main pretraining datasets, and with synthetic pretraining it also leads enviFormer on BBD single-generation PR-AUC and exact top-1 and top-5 accuracy.

Wed 14 OctMachine Learning
The gist
Predicting how chemicals break down in the environment requires lots of reaction data, but real examples are often scarce. The authors created enviGraph, a tool that makes large amounts of synthetic reaction data based on known transformation rules. This synthetic data helps train computer models to better predict chemical breakdown pathways. Their method improved prediction accuracy compared to using existing reaction datasets alone, especially for specific biodegradation scenarios.
Open → 2610.19640v1

Molecular encoders do not outperform simple descriptors in predicting cell traits

Can phenotypic activity be predicted without experimental readouts?

Abstract: Molecular encoders contrastively pretrained on paired molecule-morphology data, such as CLOOME and CellCLIP, have been proposed as cheap surrogates for phenotypic prediction, avoiding the need to run a Cell Painting assay. We evaluate this idea for these molecular encoders under a protocol designed to control for two confounds that can inflate apparent performance: leakage across an encoder's own pretraining boundary, and the correlation between phenotypic activity and cytotoxicity. Testing six representations, including a non-pretrained MLP control matching CLOOME's input and layer count, on two distinct Cell Painting screens, we find that once these confounds are controlled for, the pretrained molecular encoders show no clear advantage over plain physicochemical descriptors, and that toxicity is generally easier to predict than phenotypic activity across representations. Our results suggest leakage-aware, confound-controlled evaluation should be standard practice before phenotype-pretrained encoders are trusted as surrogates for phenotypic drug discovery.

Tue 6 OctMachine Learning
The gist
Scientists wanted to find out if computer models trained on images of cells could predict how molecules affect cell behavior without doing lab experiments. They checked several models carefully, controlling for tricky issues that can make results seem better than they are. They found that these fancy models didn't predict cell traits any better than simple chemical features of the molecules. Predicting if a molecule is toxic was easier than predicting more complex changes in cells. The authors suggest being careful when trusting these models to replace lab tests in drug discovery.
Open → 2610.07997v1