From Target Idea to Synthesizable Molecule

Denovo technologies integrate molecular design, evaluation, and synthetic route generation to shorten discovery timelines and increase candidate success rates.

Denovo Platform

Generative Engine

The Denovo Platform is a streamlined portal for access to the state-of-the-art molecular design, powered by generative and RL engines. The platform provides an integrated environment where users can generate, evaluate, and optimize novel small-molecule drug candidates with unprecedented efficiency. With its intuitive interface and fully automated end-to-end workflows, the Denovo Platform offers researchers of all backgrounds a unified entry point to explore chemical space, uncover new drug-like molecules, and accelerate the path from concept to candidate.

Generative Models

Denovo’s advanced generative AI models outperform other generative AI models for small-molecule design, producing highly novel, synthesizable, and drug-like compounds from first principles and enabling rapid exploration of chemical space far beyond existing ones.

Minimum Data Input

The platform is designed to operate effectively even in data-scarce environments, requiring only minimal target information to initiate high-quality molecule generation and optimization. By leveraging physics-based simulations, the platform overcomes the limitations of experimental datasets and delivers reliable results from the very beginning of a project.

Modularity

Integrated modules enhance research efficiency at every step, from selecting the optimal target conformation to prioritizing designed molecules with superior drug-like properties. The modular architecture also enables researchers to add new modules upon request, ensuring each workflow is specifically aligned with the project's needs.

Easy-to-Use Interface

An intuitive, user-friendly interface makes advanced computational and AI-driven discovery accessible to users at any expertise level, enabling rapid onboarding and highly efficient day-to-day workflows.

Key Performance Metrics

50K

Compounds / Week

50%

Synthesizability

100%

Chemical Validity

30%

Hit Rate

HADES

Drug-Likeness Engine

The holistic AI-based drug-likeness estimation system (HADES) is Denovo’s proprietary engine for rapid, reliable, and context-aware evaluation of small-molecule drug-likeness quality. Built on a combination of different ML models trained on the most comprehensive small molecule oral drugs dataset, HADES provides a unified, quantitative assessment of a compound’s overall drug potential—far beyond traditional filters. It integrates structural, physicochemical, ADMET, and medicinal chemistry heuristics into a single predictive score, ensuring that only the most viable compounds progress through the design pipeline.

100+ Features

For comprehensive analysis, HADES integrates major features, including physicochemical properties, chemical alerts, bioavailability, ADMET factors, and off-target risks into a single metric optimized for rapid decision-making.

Interpretability

Rather than offering a simple pass/fail evaluation, HADES provides detailed rationales for its predictions. Researchers gain a clear breakdown of each molecule's strengths and weaknesses, guiding targeted optimization and eliminating unnecessary design cycles.

Discriminatory Power

HADES performs fast and accurate prioritization of millions of compounds by identifying which molecules are drug-like and optimization-friendly. It helps researchers as a real-time guide to plan hit/lead optimization campaigns.

Key Performance Metrics

molecules per second

Speed of assessment

71%

Score Increase in Hit/Lead Optimization Studies

89.1%

Accuracy on external dataset

21.47%

1% Enrichment Factor in Virtual Screening Process

Synthony™

Synthesis Engine

Synthony is Denovo Sciences’ proprietary, synthon-driven retrosynthesis platform designed for accurate, synthesis-ready route generation for small molecules. Unlike classical reaction-center–first approaches, Synthony operates in a building-block-first paradigm, where an AI model directly predicts the most viable sets of synthons required to construct a target molecule, without relying on explicit rule-based disconnection steps.
The platform integrates: AI-driven synthon set prediction from target structures , Forward synthesis tree construction from purchasable building blocks corresponding to predicted synthons, Multi-objective route scoring based on feasibility, availability, cost, and commercial viability, Automated pruning of low-viability pathways to focus on experimentally relevant chemistry
By separating synthon selection from route construction, Synthony bypasses traditional disconnection logic and enables parallel exploration of chemically valid and commercially actionable synthesis routes.

AI-Powered Synthon Prediction

AI-Powered Synthon Prediction
Our AI models predict the most promising sets of hypothetical synthons for any target molecule, enabling accurate and efficient retrosynthetic planning without relying on traditional disconnection rules.

Preference for Shorter and Wider Paths

Prioritizes routes that are shorter and offer multiple viable options, increasing the likelihood of successful synthesis and providing flexible pathways for chemists.

Early Pruning of Unsolvable Routes

Automatically removes low-viability or infeasible routes early in the planning process, saving time and focusing efforts on pathways likely to succeed.

Feasibility and Commercial Viability Scoring

All routes are automatically scored for chemical plausibility, cost-efficiency, and commercial viability, helping chemists prioritize the most promising pathways and focus on routes likely to succeed in real-world lab settings.

Key Performance Metrics

74%

Top-1 Accuracy (USPTO50k)

50–200

Routes / Molecule

Avg Synthon Sets / Molecule

10+

Molecules / Second

Q-Pocket

Target Analyzing Engine

Q-Pocket is a toolkit designed for the automated analysis and selection of optimal binding pocket conformations. Moving beyond the analysis of structures as discrete entities, Q-Pocket uses a continuous representation of binding sites to systematically capture the experimentally evidenced conformational space. Q-Pocket provides a benchmarking platform for these states against specific functional criteria and identifies the single most effective conformational state for downstream applications. It serves as an important solution for the selection, processing, and interpretation of structural data for actionable drug discovery, ensuring that virtual screenings and de novo small molecule generation are built upon the most geometrically and energetically favorable basis.

Quantum-Informed Representation

Q-Pocket transcends traditional geometric analysis by utilizing quantum-annotated hotspot clouds. This allows for a high-fidelity representation of the binding pocket that incorporates electronic and energetic properties alongside spatial coordinates.

Automated Benchmarking

Eliminating the bias of manual structure selection, Q-Pocket automatically evaluates the entire evidenced conformational space of a target. It runs a systematic benchmarking process to rank states based on tractability and druggability, isolating the conformation most likely to yield high-quality hits.

Generative Readiness

Designed specifically to empower Denovo Sciences’ drug discovery workflows, Q-Pocket ensures that the selected binding site is fully optimized for RL-driven small molecule generation. The toolkit delivers a standardized, high-quality input that significantly increases the success rate of subsequent design cycles.

Key Performance Metrics

< 1 min

Extraction, featurization & distance matrix (10 structures)

~ 10%

Early enrichment boost in virtual screening (EF 1%)

Discover how our technologies can accelerate your discovery.

Partner with us to discover new drug candidates.