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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>MolPILE -- large-scale, diverse dataset for molecular representation learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2509.18353/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2509.18353/feed.xml" rel="self" type="application/rss+xml" /><item><title>MolPILE -- large-scale, diverse dataset for molecular representation learning — mentioned by morgul</title><link>https://news.ycombinator.com/item?id=46733397</link><guid isPermaLink="false">urn:hackerpapers:mention:2509.18353:46733397</guid><pubDate>Fri, 23 Jan 2026 15:09:01 GMT</pubDate><description>&lt;p&gt;I did large-scale molecular data engineering with DuckDB and Polars (https://github.com/scikit-fingerprints/MolPILE_dataset, https://arxiv.org/abs/2509.18353 for those interested). Both were amazing, but for large-scale JOINs, only DuckDB didn&amp;#x27;t result in OOM (tried Dask, DuckDB, Polars). Really a pleasure to use too. I could also JOIN local CSV datasets, Postgres database, and even Excel files from chemists. All of this in Jupyter Notebook and really seamless Python integration. This also means that one can easily do heavy lifting in DuckDB, export to Polars variable, put into Plotly, and…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2509.18353/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>MolPILE -- large-scale, diverse dataset for molecular representation learning — mentioned by morgul</title><link>https://news.ycombinator.com/item?id=46733390</link><guid isPermaLink="false">urn:hackerpapers:mention:2509.18353:46733390</guid><pubDate>Fri, 23 Jan 2026 15:08:33 GMT</pubDate><description>&lt;p&gt;I did large-scale molecular data engineering with DuckDB and Polars (https://github.com/scikit-fingerprints/MolPILE_dataset, https://arxiv.org/abs/2509.18353 for those interested). Both were amazing, but for large-scale JOINs, only DuckDB didn&amp;#x27;t result in OOM. Really a pleasure to use too. I could also JOIN local CSV datasets, Postgres database, and even Excel files from chemists. All of this in Jupyter Notebook and really seamless Python integration. This also means that one can easily do heavy lifting in DuckDB, export to Polars variable, put into Plotly, and get an interactive plot. Neat…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2509.18353/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
