Open-source protein and ligand viewer

Hacker News

I just published an open-source protein and ligand viewer. Goal: fast and easy-to-use. Long-term: It's a platform for docking and molecular dynamics. The basic functionality for viewing and inspecting work. I'm currently working on Coot-like electron-density viewing. I'm gradually splitting it into standalone libs, published to crates.io. Some are up already, others are integrated, and will be split off when sufficiently mature and decoupled. Uses parallel execution when able, through thread pools (Rayon), CPU SIMD and Cuda. The default control scheme is non-standard, unless you play video games... Any feedback and critique appreciated!

Visit product View on Hacker NewsMay 23, 2025

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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

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

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

61%61% 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: video, para · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

16%16% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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