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Interactive Drug Screening in Your Browser

Hacker News

Interactive Drug Screening in Your Browser

Today, the team at Bindwell (YC W25) is launching a free, unified platform for finding candidate drugs (or pesticides, or herbicides, or anything else that needs to bind to a protein), and visualizing their structure. Unlike AlphaFold 3 and others, we've worked hard to make the experience as interactive as possible by designing and training our own accelerated models, significantly speeding up the candidate screening workflow. Q: Why are we building this? A: To design better, safer pesticides. The therapeutics industry has seen huge improvements by integrating machine learning models into its drug discovery pipelines; on the other hand, the agrochemical pesticide discovery space has seen almost no improvement, despite the high degree of similarity between the two processes. This is because the agrochemical industry is largely stagnant; fewer than 40 new pesticide ingredients have been developed in the last decade, with most "new" pesticides being just tweaks of existing ones. To solve this, we're using our SOTA machine learning models to find radically better pesticides. Because this is a search problem, speed is key. This is why we've spent so much time optimizing our models to be as fast as possible, which also allows us to make them freely available without the long queue times or rate limits alternatives like AlphaFold 3 or Chai have. Q: Who are we? A: We’re Navvye Anand (Caltech) and Tyler Rose (Wolfram Research), a scrappy duo of engineers who met at the Wolfram Summer Research Program in June 2023. We’re Indo-Chinese founders who both grew up visiting farmlands in our countries. United by our passion for tackling hard problems, we dropped out of college and started Bindwell to transform the archaic agrochemical industry. We’re joined by Max, a prodigious hacker and open source contributor who dropped out of a math degree at Reed College. Max is relentlessly curious about everything from robotics to philosophy, and like us, his passion for solving challenging problems made him a perfect fit for the team. We'd love to hear from you, whether it's feedback about our app, questions about our mission, or anything else you think is interesting. You can reach us by commenting on this post, or by emailing us at founders@bindwell.ai.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
93%93% 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: mac, model, new · Missing: agents, macos, agent
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training, active · Missing: arr, mrr, revenue
12%12% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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