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Accelerating drug discovery through physics-informed machine learning and quantum-accurate molecular simulation.

Entos is a pioneer in the 'TechBio' sector, leveraging its proprietary OrbNet technology to bridge the gap between high-fidelity quantum mechanics and the speed of machine learning. Unlike traditional drug discovery platforms that rely on heuristic-based models, Entos utilizes physics-informed AI architecture to predict molecular properties with the accuracy of Density Functional Theory (DFT) but at 1,000x to 10,000x the speed. This allows for the screening of billions of compounds in a fraction of the time. By 2026, Entos has positioned itself as the backbone of hybrid therapeutic pipelines, integrating its EnSieve high-throughput screening with active learning loops that refine lead candidates in real-time. The platform is designed for medicinal chemists and computational biologists who require high-confidence predictions for ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles before physical synthesis. Its architecture is built to handle massive datasets while maintaining thermodynamic consistency, making it a critical tool for tackling 'undruggable' targets and optimizing lead compounds in the hit-to-lead phase of pharmaceutical R&D.
Entos is a pioneer in the 'TechBio' sector, leveraging its proprietary OrbNet technology to bridge the gap between high-fidelity quantum mechanics and the speed of machine learning.
Explore all tools that specialize in predict molecular properties. This domain focus ensures Entos delivers optimized results for this specific requirement.
Explore all tools that specialize in optimize drug leads. This domain focus ensures Entos delivers optimized results for this specific requirement.
Explore all tools that specialize in simulate molecular dynamics. This domain focus ensures Entos delivers optimized results for this specific requirement.
Explore all tools that specialize in design novel molecules. This domain focus ensures Entos delivers optimized results for this specific requirement.
Explore all tools that specialize in design new molecules for drug candidates. This domain focus ensures Entos delivers optimized results for this specific requirement.
A graph neural network architecture that incorporates symmetry-adapted atomic orbital information to achieve DFT-level accuracy.
An automated workflow for filtering billions of molecules based on physics-based constraints.
A Bayesian optimization framework that selects the most informative molecules for subsequent simulation steps.
Ensures all predicted molecular properties obey fundamental physical laws (conservation of energy, etc.).
Fine-tuned models for predicting metabolic stability, solubility, and toxicity based on specific chemical series.
Predicts the synthetic accessibility of AI-generated molecules.
Allows for heavy computational workloads to be distributed across Entos's cloud or secure on-prem clusters.
Secure Enterprise account provisioning and SSO integration.
Initialize project workspace and define therapeutic target parameters.
Import proprietary chemical libraries or utilize Entos built-in virtual libraries.
Configure OrbNet simulation parameters for desired quantum accuracy levels.
Run EnSieve high-throughput screening to identify initial hits.
Deploy Active Learning loops to iteratively refine lead candidates.
Execute ADMET property prediction simulations on top-tier candidates.
Perform multi-parameter optimization (MPO) to balance potency and safety.
Export high-confidence candidates for laboratory synthesis and validation.
Feedback wet-lab results into the Entos platform for model retraining.
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Verified feedback from other users.
"Users praise the platform for its rare combination of quantum-level accuracy and production-level speed. Pharmaceutical partners highlight the significant reduction in 'dry' cycles before moving to 'wet' lab validation."
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A computational platform transforming therapeutics and materials discovery through physics-based modeling and AI.