AIDO.ModelGenerator and Expanded Foundation Model Suite

Hacker News

GenBio AI just released an update to AIDO.ModelGenerator, a software package for adapting, benchmarking, and applying biological foundation models across multiple tasks and data types. What it is AIDO.ModelGenerator supports both GenBio AI models and popular open-source models like Enformer, ESM2, and scFoundation. It enables workflows for structure prediction, protein modeling, single-cell analysis, and spatial transcriptomics. What’s new New models: • AIDO.StructurePrediction – biomolecular structure prediction across proteins, RNA, DNA, and ligands • AIDO.Protein-RAG – protein modeling with MSA and structure input • AIDO.Tissue – spatially aware cell modeling New benchmark datasets: • Cell age prediction • Cell-type classification • Cellular perturbation (Tahoe-100M) Utilities for distributed training and hyperparameter tuning Use cases Includes tutorials for: • mRNA vaccine design • Predicting protein complexes • Simulated gene knockouts for target discovery

Visit product View on Hacker NewsApr 17, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos

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

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TrustMRRFits verified-revenue profile · Strong signals: para · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: just released, io · Missing: https docs, excited, exist

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

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