Multiplatform AI Ecosystem
for Drug Discovery

An integrated AI ecosystem designed to support early discovery decisions, strengthen rational therapeutic design, and provide clinically meaningful program translation.

Biology determines the strategy.
The multiplatfrom ecosystem delivers an objective, modality-agnostic therapeutic solution.

small molecule platform

Targeting of cryptic and
transient protein pockets

Multiparametric optimization
across 80+ endpoints

Design of novel and IP-free
compounds

peptide platform

Design of orally bioavailable
peptides

De novo generation of
functional binders

Targeting of intracellular
proteins

Proximity Inducers Platform

Development of antibody-
enabled therapeutics

Drugging of transient and
shallow interfaces

Design of molecular glues
and protein degraders

LEVEL 01

R&D Strategy and Control

Agentic AI creates the R&D project plan based on validated drug discovery strategies, monitors its execution across all platform levels, and adapts it under expert oversight.

Strategy selection: Agentic AI chooses the drug discovery strategy tailored to the scientific and clinical profile constraints.
R&D plan design: Relying on the strategy, the system creates the project plan and assembles end-to-end drug discovery workflows for each iteration.
Risk Assessment: The plan is analysed by domain experts who reveal and control all potential risks connected to the project.
Strategic adjustment: The system iteratively revises the R&D plan based on intermediate experimental results, expert insights, and resource dynamics.

01

LEVEL 02

Drug Discovery Workflows

End-to-end drug discovery workflows tailored to the therapeutic modality and target class are selected and configured according to the R&D plan.

Supported modalities: Small molecules, linear and cyclic peptides, covalent inhibitors, and various proximity-inducing agents.
Target-specific protocols: Tailored workflows for GPCRs, kinases, ion channels, enzymes, and targets involved in PPIs.
Dry + Wet lab integration: We align computational and experimental parts of the workflow by accounting for the target biology and mechanism of action.
Experimental integration: Each workflow has an embedded active learning strategy that guides experimental data generation.

02

LEVEL 03

AI Model Stack

A suite of advanced predictive and generative AI models designed to power core drug discovery tasks and enable efficient, data-driven optimization across the pipeline:

ArtiDock: A high-throughput AI-powered docking engine for both small molecules and peptides, trained on the world’s largest augmented dataset.
DeepTAG: A next-generation model for predicting protein-protein interaction interfaces without requiring structural templates — a major step beyond traditional tools like AlphaFold.
ADMET: An industry-leading ADMET prediction engine covering 80+ endpoints, incorporating consensus scoring that accounts for inter-endpoint correlations and mechanistic dependencies for superior decision-making.
OffTaRGet: A comprehensive selectivity profiling tool that combines ligand-based and structure-based prediction to assess off-target risk across closely related and mechanistically distinct targets.

03

LEVEL 04

Data Engine

A secure data management environment for collecting, engineering, and analyzing project-specific data, enabling fine-tuning of our AI models.

Public data integration: LLM-powered knowledge graph extracts and systematizes all publicly available information as the project starts.
Feature engineering: Revealing hidden structure-property relationships through the analysis of both natural and artificially generated compound descriptors
Data-driven project guidance: Uncovering hidden correlations and uncertainties in the initial data to steer the subsequent experimental rounds.
Data augmentation: AI-based data generation that enriches sparse experimental datasets to support AI models' fine-tuning.

04