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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Massively Multitask Networks for Drug Discovery · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1502.02072/</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/1502.02072/feed.xml" rel="self" type="application/rss+xml" /><item><title>Massively Multitask Networks for Drug Discovery — mentioned by dekhn</title><link>https://news.ycombinator.com/item?id=29541129</link><guid isPermaLink="false">urn:hackerpapers:mention:1502.02072:29541129</guid><pubDate>Mon, 13 Dec 2021 16:33:04 GMT</pubDate><description>&lt;p&gt;the field is the application of machine learning to biology (specifically drug design, but also the wider area of computational biology in all its forms). Here&amp;#x27;s some of my prior work: https://arxiv.org/abs/1502.02072 however that doesn&amp;#x27;t use transformers. The DeepMind work on protein structure prediction does, and it represents one of the greatest technical achievements of the modern era. Transformers basically took anything that was &amp;quot;sequence learning&amp;quot; (large fractions of computational biology are) and made it work 100X better over night.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1502.02072/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
