Papers for

pharmaceutical screening teams

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.

Automated pipeline improves DNA damage measurement in comet assay images

COMPASS: Comet Object Measurement Pipeline with Automated Selection and Scoring

Abstract: Summary: The single-cell gel electrophoresis ('comet') assay is a widely used technique for quantifying DNA damage at the individual cell level. However, image analysis often relies on manual inspection or semi-automated software, which can be labor-intensive, difficult to reproduce, and sensitive to image quality and comet morphology. COMPASS automates comet assay image analysis by combining deep learning-based comet segmentation with damage measurement and automated comet selection. The pipeline produces standardized DNA damage measurements while offering robust detection, reducing manual effort and improving reproducibility through transparent selection and optional manual review. Availability and implementation: COMPASS is implemented in Python and is freely available at https://github.com/ rsinghlab/COMPASS. Installation instructions, pretrained weights, and example usage are provided in the repository. Contact: jack_roberts2@brown.edu, ritsingh@illinois.edu Supplementary information: Available onlime upon publication.

Sat 3 OctComputer Vision and Pattern Recognition
The gist
Measuring DNA damage inside individual cells can be tricky because scientists usually look at images by hand or use partly automated tools that can take a lot of time and might miss some details. The authors created COMPASS, a computer program that uses artificial intelligence to automatically find and measure damaged DNA in these images. This makes the results more reliable, saves time, and lets scientists check or change results if they want. COMPASS is freely available for anyone to use.
Open → 2610.04680v1

Algorithm picks best mix of top ranking and diverse chemical compounds

OPDiv: Optimal Selection of Top-K High-Scoring, Diverse Compounds

Abstract: A virtual screening campaign may produce thousands of promising candidates, but only a small number can be purchased, synthesized, or tested. The practical question is how to select a set of compounds that both rank well and are diverse enough: this poses a genuine tradeoff, where selecting the highest-scoring molecules yields limited diversity, while diversity selection sacrifices some well-scoring molecules. We introduce OPDiv, a diversity selection and evaluation algorithm solving this tradeoff by finding an optimal subset of molecules using integer optimization. We demonstrate the selection algorithm in practice with fingerprint distance, shape and electrostatic diversity and compare the resulting diversity spectra. We argue that virtual screening is not merely a ranking problem, but also an implicit constrained optimization task: when redundant chemotypes are undesirable, pipelines should be compared based on the top-k compound selections satisfying the desired diversity constraints. OPDiv makes it possible to find the optimal compound set under a given diversity threshold efficiently and serves as a fair benchmark of the best diverse selection achievable by a given structure-based or ligand-based virtual screening pipeline, molecular search or generative model.

Wed 23 SeptMachine Learning
The gist
When searching for new chemicals, scientists find many good candidates but can only test a few. Picking the very best often means many similar compounds, while choosing diverse ones might miss the top scorers. The authors present OPDiv, an algorithm that finds the best balance by choosing a small group of chemicals that score high and are diverse. OPDiv uses a smart math approach called integer optimization to do this efficiently. This helps compare and improve virtual screening methods by focusing on diverse top picks, not just top scores.
Open → 2609.28665v1