IktosNEW

Iktos is a specialized French deep-tech company developing artificial intelligence solutions for drug discovery and lead optimization. The technology combines generative AI models with advanced retrosynthesis planning to drastically accelerate the design of novel small molecules in the pharmaceutical and biotech industries.

The company's core platforms include Makya for structure-based and ligand-based de novo drug design, alongside Spock for automated synthesis planning. By integrating physicochemical property predictions with synthetic accessibility scoring, Iktos enables medicinal chemists to generate highly promising drug candidates with reduced synthetic complexity.

In addition to software platforms, Iktos offers integrations with automated laboratory robotics to close the Design-Make-Test-Analyze (DMTA) cycle in pharmaceutical research. The tool is tailored for professional labs, medicinal chemists, and R&D departments in global pharmaceutical enterprise environments.

• Generative De Novo Drug Design (Makya) — Uses deep learning algorithms to generate novel molecular structures tailored to specific biological targets. The platform simultaneously optimizes multiple parameters such as binding affinity, selectivity, solubility, and toxicity profiles.
• AI-Driven Retrosynthesis Planning (Spock) — Analyzes target molecules and suggests feasible multi-step synthetic routes based on verified chemical reaction data. This significantly reduces time spent on reaction route planning in the lab.
• DMTA Cycle Integration — Connects AI design pipelines directly with experimental lab data and robotic synthesis hardware. This enables rapid feedback loops and continuous model improvement based on actual test results.

Medicinal chemists, computational chemists, drug discovery researchers, and R&D teams in pharmaceutical and biotechnology organizations.

A pharmaceutical firm aims to discover a novel lead compound for a recently validated target protein. Using Makya, medicinal chemists generate novel molecular candidates optimized for target binding and selectivity. Spock then evaluates synthetic accessibility and generates actionable synthesis pathways. Consequently, the research team minimizes trial-and-error iterations in the wet lab and shortens lead optimization timelines substantially.

• Medicinal chemists, computational chemists, drug discovery researchers, and R&D teams in pharmaceutical and biotechnology organizations.
• A pharmaceutical firm aims to discover a novel lead compound for a recently validated target protein.
• Generative De Novo Drug Design (Makya): Uses deep learning algorithms to generate novel molecular structures tailored to specific biological targets. The platform simultaneously optimizes multiple parameters such as binding affinity, selectivity, solubility, and toxicity profiles.
• AI-Driven Retrosynthesis Planning (Spock): Analyzes target molecules and suggests feasible multi-step synthetic routes based on verified chemical reaction data. This significantly reduces time spent on reaction route planning in the lab.
• DMTA Cycle Integration: Connects AI design pipelines directly with experimental lab data and robotic synthesis hardware. This enables rapid feedback loops and continuous model improvement based on actual test results.

Not suitable for general-purpose AI tasks like text or image generation, nor for individual users or casual hobbyists lacking professional life science infrastructure.

• SYNTHIA (Merck KGaA) — Paid · HQ: Darmstadt, Germany · Unclear