臨床開発の成功率を分析する創薬向けAIプラットフォーム

Lavo is an AI-accelerated crystal structure prediction platform for small-molecule drug development, built by Lavo Life Sciences. Its Crystal Console web application helps research teams import and visualize crystal structures, run virtual polymorph screening, and analyze and compare PXRD data against predicted structures. The goal is to de-risk the solid-state pipeline, optimize formulations and catch unexpected crystal forms before they disrupt late-stage development.

編集部による 5 点満点の評価です。ユーザー評価の平均ではありません。 このページの各視点は、製品情報をもとに AI がまとめたものです。実際の利用者のレビューではありません。
OpenAITools.app のテレメトリ(2026-11-11 検証)によると、Lavo Life Sciences(lavo.ai)は AI 予測に分類される AI プラットフォームで、月間 0 の訪問を生み出し、前月比のトラフィック増減は +0(0%)です。主要な地域市場はグローバル(トラフィックシェア —)で、料金は要問い合わせの料金モデルで運営されています。月間ボリューム別の主な競合代替ツールには Intelligencia AI(月間 3872 訪問)、Unlearn.AI(月間 1.71万訪問)、Citrine Informatics(月間 7770 訪問)が含まれます。
結晶予測が早い。
製薬R&Dに便利。
サイト検証・料金体系・同カテゴリ比較に基づき AI が多角的な論点を要約した参考視点であり、実在ユーザーの個別投稿ではありません。
Import, visualize and analyze crystal structures through an intuitive browser-based interface.
Identify and analyze potential polymorphs computationally to flag solid-state risks early in development.
Upload, analyze and compare PXRD data against predicted structures to confirm forms faster.
Surface hidden or more desirable polymorphs that traditional experimental methods might miss.
Use predictions to support stability and manufacturability decisions that reduce development time and cost.
Lavo is an AI-accelerated crystal structure prediction platform for small-molecule drug development, built by Lavo Life Sciences. Its Crystal Console web application helps research teams import and visualize crystal structures, run virtual polymorph screening, and analyze and compare PXRD data against predicted structures. The goal is to de-risk the solid-state pipeline, optimize formulations and catch unexpected crystal forms before they disrupt late-stage development.
Screen for potential polymorphs computationally to flag solid-state risks before committing to costly experiments.
Compare experimental PXRD patterns against predicted structures to identify and confirm crystal forms faster.
Use crystal structure insights to inform decisions about stability and manufacturability of drug candidates.
Explore predicted structures to spot novel or more desirable polymorphs that experimental methods may overlook.
同じ主要カテゴリの競合 AI ツールをテレメトリで並列比較
| 順位 | AI ツール | 月間訪問数 | 前月比成長率 | 直接流入の割合 | 料金 | 直接比較 |
|---|---|---|---|---|---|---|
| ● | Lavo Life Sciences | 0 | 0% | — | 料金は要問い合わせ | 表示中 |
| 1 | Intelligencia AI | 3872 | -31.9% | 直接流入 27% | 料金は要問い合わせ | vs Intelligencia AI |
| 2 | Unlearn.AI | 1.71万 | -32.5% | 直接流入 44% | 料金は要問い合わせ | vs Unlearn.AI |
| 3 | Citrine Informatics | 7770 | -1.2% | 直接流入 40% | 料金は要問い合わせ | vs Citrine Informatics |
| 4 | Cradle | 1.7万 | -22.3% | 直接流入 37% | 料金は要問い合わせ | vs Cradle |
| 5 | Lotto Chart | 1.73万 | +89.4% | 直接流入 75% | 無料 | vs Lotto Chart |
| 6 | Floatz | 253 | -36.8% | 直接流入 37% | 料金は要問い合わせ | vs Floatz |
臨床開発の成功率を分析する創薬向けAIプラットフォーム

Unlearn.AI builds AI-generated digital twins of clinical trial participants — forecasts of how each individual patient's outcomes would evolve — and uses them to sharpen design, interim, and readout decisions in drug development.
Unlearn.AI builds AI-generated digital twins of clinical trial participants — forecasts of how each individual patient's outcomes would evolve — and uses them to sharpen design, interim, and readout decisions in drug development.
材料・化学品開発のためのAI。散らかった実験データを、より速い製品上市へ
無料・オープンソース7つのAI予測モデルで宝くじの数字を分析・チャート化

Synthace is a cloud software platform that helps life-science R&D teams design multivariate experiments, automate their execution across lab hardware, and capture the results as structured, contextualized data. It combines design-of-experiments (DOE) tooling with instrument and dispenser integrations so biologists can run richer experiments without writing code or rebuilding their automation stack.
Synthace is a cloud software platform that helps life-science R&D teams design multivariate experiments, automate their execution across lab hardware, and capture the results as structured, contextualized data. It combines design-of-experiments (DOE) tooling with instrument and dispenser integrations so biologists can run richer experiments without writing code or rebuilding their automation stack.
無料・オープンソース確率・日付・数値・意思決定を、根拠ある予測で

製薬・医療機器のトップチームが信頼するAI活用の文献レビュー・エビデンス管理プラットフォーム
無料・オープンソース約1,900銘柄を対象にした毎日のAI目標株価予測
無料・オープンソースシニアリビング向けAIケアプラットフォーム