
Kallyope
Deciphering the gut-brain axis through AI-driven drug discovery for transformative therapeutics.

AI-driven de novo drug design and automated retrosynthesis for accelerated medicinal chemistry.

Iktos is a leading French AI company specializing in deep learning applications for medicinal chemistry and drug design. As of 2026, Iktos has solidified its position as a top-tier provider of generative modeling solutions for de novo design (Makya) and AI-powered retrosynthesis (Spaya). Their technical architecture leverages advanced Deep Generative Models, including Reinforcement Learning and Variational Autoencoders, to navigate vast chemical spaces while optimizing for multiple parameters simultaneously (MPO). Unlike traditional docking-based virtual screening, Iktos focuses on generating synthetically accessible molecules that meet specific ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles. The 2026 market position of Iktos is defined by its 'Iktos Robotics' integration—a closed-loop platform that combines generative software with automated chemical synthesis hardware. This synergy addresses the primary bottleneck in drug discovery: the cycle time between molecular design and physical testing. Their platform is utilized by global pharmaceutical giants and mid-sized biotech firms to reduce the 'Design-Make-Test-Analyze' cycle from months to weeks, utilizing proprietary algorithms that ensure high synthetic feasibility and structural diversity.
Iktos is a leading French AI company specializing in deep learning applications for medicinal chemistry and drug design.
Explore all tools that specialize in retrosynthesis planning. This domain focus ensures Iktos delivers optimized results for this specific requirement.
Uses Reinforcement Learning to optimize molecular structures against custom scoring functions.
A template-based and template-free hybrid AI for predicting chemical reaction pathways.
Weighted scoring system for balancing conflicting properties like solubility vs. potency.
Incorporation of 3D protein structure information into the generative reward function.
Algorithms designed to replace a core molecular scaffold while maintaining peripheral interactions.
Models that improve iteratively as new experimental data is uploaded into the system.
Direct communication between generative software and liquid-handling robots.
Initial project configuration and target definition in the Makya platform.
Uploading existing project data or reference ligands in SMILES or SDF format.
Definition of Multi-Parametric Optimization (MPO) criteria including potency and ADMET thresholds.
Configuration of the generative engine parameters (exploration vs. exploitation).
Selection of molecular building blocks and allowed chemical reactions for synthetic constraints.
Execution of the generative cycle to produce thousands of novel molecular candidates.
Automated retrosynthetic analysis using the Spaya engine to verify synthetic accessibility.
Clustering and filtering of results based on structural diversity and pharmacophore alignment.
Selection of top-tier candidates for physical synthesis or further docking simulations.
Exporting finalized routes and molecular structures to Electronic Lab Notebooks (ELN).
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"Highly regarded in the pharmaceutical industry for the synthetic feasibility of its generated molecules, though users note a steep learning curve for non-computational chemists."
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