Technology and Capabilities

Iktos is the only company offering state-of-the-art, ready-to-use generative modelling solutions with synthetic accessibility information for successful drug discovery projects. 

At Iktos, we have developed an innovative technology platform for drug design and discovery. The Generative AI is taking off and a myriad of deep generative models reported in the scientific literature. Incorporated in 2016, Iktos has pioneered Generative AI approach and stands at the forefront of AI-based generative chemistry by combining its internal R&D and a continuous benchmarking effort.  Our technology has delivered value in 50 plus real-world research collaborations with some of the most eminent pharma companies globally.

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Generative models relying on deep neural networks have recently emerged as promising tools to overcome multi-parametric optimisation (MPO) challenges and to propose AI-designed optimal molecules that match your target product profile (TPP) in a relatively short time frame. 

Complementary to your design efforts, Iktos generative AI technology provides new, unbiased insights on the project, based on an optimal exploitation of the information hidden in your data. From hit discovery to lead optimisation, our technology integrates ligand and structure-based approaches to design new molecules solving your project objectives. Our proprietary algorithms excel at multi-parametric optimisation (MPO). 

  • If you are struggling to make the consensus on all the objectives of a lead optimisation project (10 and more), Iktos will help you design new structures, close to your chemical series, that optimise all your endpoints. 
  • If you want to discover new hits based on 2D, 3D signature similarity (e.g. pharmacophores, shape or charges) or crystallography data, Iktos is developing proprietary methods to design virtual hits with new scaffolds, for any given blueprint. 

Technology Platform

Generative Design

We have developed Makya, a fully automated SaaS platform for de novo drug design that enables rapid identification of molecules that satisfy multiple drug-like criteria to expedite drug discovery and development. Makya is suitable for both ligand-based and structure-based virtual lead design and allows multi-parametric optimisation (MPO) of potential pre-clinical drug candidates.

Makya has three main applications:

  • Hit / New scaffold generation
  • Lead generation
  • Lead optimisation

Synthetic Planning

Spaya is Iktos’s computer-aided synthetic planning technology that uses data-derived AI models to discover and prioritise synthetic routes. The underlying AI engine has been trained on several millions of reactions and Iktos continues to train it with more reaction data, thereby enhancing its performance.

Spaya performs an exhaustive analysis of all possible synthetic routes toward a given compound and ranks them according to various criteria, each route ending with commercially available building blocks.

Expertise and Capabilities

At Iktos, we have a brilliant and talented team in computer science, chemoinformatics, molecular modelling and medicinal chemistry, supported by a strong team in IT and software engineering. We have developed a unique set of capabilities in the field of AI/ML technology for drug discovery.

Objective functions

We have introduced fitness functions to guide the molecular generators in exploring the vast chemical space and to produce optimally designed molecules in line with project needs. The fitness functions are predictors, specifically built from the collaborator’s project’s data set: molecules, measured bioactivity data points, activity, selectivity, ADMET…In addition,  we also have implemented fitness functions to maximise druggability, synthetic accessibility (spaya.ai), and similarity to collaborators initial data set. The molecules generated from Iktos technology platform can be optimised simultaneously on multiple parameters in line with the success criteria.

Top notch predictive models

Iktos molecule generation technology is only as good as the predictors it uses to travel in the chemical space. Therefore, the  quality of the predictors is key. We have developed ways to construct high-performance predictors and to select the optimal ones, automatically and in very short time frame, by benchmarking and optimizing both machine learning algorithms and molecular descriptors (1D, 2D and 3D representations of the molecules, and even data driven representations like latent space for instance).

Molecular structures generation metrics

Our monitoring tools enable real-time supervision of the process and visualisation of the results as they are generated:

  • Speed of convergence to the desired objective
  • Molecular diversity of the generated structures
  • Similarity to the initial data set
  • Other Multi-parametric objectives (MPO)

A state-of-the-art big data infrastructure

Iktos has developed its own big data platform that enables parallelisation of computation on GPU and CPU on demand, and can easily be scaled up to meet any computational needs. All Iktos technologies can easily be deployed on collaborators virtual private cloud (VPC) either on AWS, Azure or Google if needed.