Our broad range of expertise in bioinformatics and machine learning allows for efficient, multi-evidence identification and stratification of leading candidate targets.

• Synthetic lethality-based gene pairs identification
• Genetic landscape characterization
• Prediction of similarities in drug effects
• Differential gene expression analysis
• Translation analysis with ribosome profiling
• Functional characterisation of affected gene sets
• Literature data mining for target evidence and mechanism of action

Comprehensive computational approaches to validate the early stages of the drug discovery process in-silico.

• Computational methods for the creation of KO/KD/KI
• Computational methods for the design of disease models
• In silico prediction of siRNA acting site
• In silico prediction of sgRNA (CRISPR) acting site
• Genetic screen perturbation analysis

Ardigen’s disease model design enables preclinical studies of pharmacological properties.

• Evaluation of transcriptome profiles in preclinical models
• Multi-omic data integration from preclinical studies
• Prediction of toxicity including immunotoxicity
• AI-based scoring of whole slide biopsies

Biomarker signatures of a patient’s response to the treatment to improve the statistical power of the trial.

• Patient stratification associated with overall survival
• Prediction of response and survival
• Publication and reporting
• Discovery of novel biomarkers
• Surrogate markers for safety and efficacy

• Custom methods leveraging AI/Data Science in your challenging R&D projects
• Broad applications: Biomarker Discovery, NLP for knowledge extraction, MLOps for production and experimental AI pipelines, etc.
• Research in collaboration with renowned academic groups on the development and application of novel deep learning methods (adversarial networks, semi-supervised learning, graph-representation learning, etc.)

• High-content phenotypic screening (HCS) with superior image-based compound fingerprints
• Robust image processing aimed at improving prediction properties
• Advanced Computer Vision pipelines with diverse applications in biomedical imaging
• Automated assessment of pathological changes outperforming human experts
• Interpretability, quality control, automatic segmentation of tissues

• In-house COPTIC technology for molecule property prediction and optimization (based on cutting-edge research presented at NeurIPS 2019 and 2020)
• In-house AI-based RMAT technology for accurate prediction of compound properties

Custom solutions based on in-house PRISM technology:

• Working with any protein type
• Prediction and optimization for properties, such as binding, stability, immunogenicity, interactions, etc.
• Recommendation of changes in the amino-acid sequence with custom constraints
• Collaboration scenarios tailored to your needs (optimization iterations and/or web application for cloud computing)

• Comprehensive life science computing support
• End-to-end cloud-native systems development
• MLOps solutions for Al projects
• Security & legal compliance

• Large scale, automated production pipelines for RNA-Seq, DNA-Seq, single cell, etc.
• Robust research oriented pipelines
• Pipeline benchmarking and optimization
• High quality and reproducible pipelines

• Data architecture consulting
• Data ingestion & engineering
• Big data analytics
• Bioinformatics workflows engineering

• Data capture & storage systems
• Harmonization & FAIRification pipelines
• Reporting & dashboard solutions
• Self-service data marts