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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2202.05146/</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/2202.05146/feed.xml" rel="self" type="application/rss+xml" /><item><title>EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction — mentioned by MAXPOOL</title><link>https://news.ycombinator.com/item?id=32088706</link><guid isPermaLink="false">urn:hackerpapers:mention:2202.05146:32088706</guid><pubDate>Wed, 13 Jul 2022 21:01:20 GMT</pubDate><description>&lt;p&gt;What do you think after read the abstract? https://arxiv.org/abs/2202.05146 &amp;gt;... Existing methods are computationally expensive as they rely on heavy candidate sampling coupled with scoring, ranking, and fine-tuning steps. We challenge this paradigm with EquiBind, an SE(3)-equivariant geometric deep learning model performing direct-shot prediction of both i) the receptor binding location (blind docking) and ii) the ligand&amp;#x27;s bound pose and orientation. ...&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2202.05146/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>q-bio.BM</category></item></channel></rss>
