(Our startup) DistilBio, a search engine for life sciences data

Hacker News

Link: http://www.distilbio.com DistilBio lets you look up detailed information on drugs, diseases, proteins etc. You can ask questions like "Find drugs which interact with Tylenol, and proteins which are targeted by these drugs", or build more complex queries if you like. While it is targeted at scientists in the life sciences field, we've found that many people like finding out more about drugs they're using, diseases they know about etc. We're continuing to add more data and make it more useful for the layman too. We thought we'd like to show our app to HN users and solicit feedback before we embark on a big PR push. We rely on this forum heavily for info on all things startup related :) Thanks

Visit product View on Hacker NewsMar 9, 2012

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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