Swissi
PharmaAI
AI-Accelerated Drug Discovery, Molecular Simulation, and Clinical Trial Optimisation
Transforming pharmaceutical research through AI-powered molecular design, predictive toxicology, and intelligent clinical trial optimisation. From target identification to market-ready compounds in a fraction of traditional timelines.
"The future of drug discovery is not about replacing scientists—it is about amplifying their capabilities with AI systems that can explore chemical space at unprecedented scale while maintaining rigorous safety standards."— PharmaAI Research Team
Drug development costs exceed USD 2.6 billion per approved compound with 90% failure rates in clinical trials. Average time-to-market spans 12-15 years from discovery to approval.
AI-driven drug discovery platform reducing preclinical timelines by 60%, improving hit rates through generative molecular design, and optimising clinical trial protocols for faster regulatory approval.
The Problem
Prohibitive Discovery Costs
Traditional high-throughput screening evaluates millions of compounds but yields diminishing returns. The vast majority of the 10^60 drug-like molecules remain unexplored, while synthesis and testing of each candidate costs thousands of dollars.
Clinical Trial Failures
90% of drug candidates fail in clinical trials, with Phase II and III failures accounting for the bulk of industry losses. Poor target validation, inadequate patient stratification, and unforeseen toxicity drive these failures.
Core Capabilities
End-to-end AI platform covering the complete drug discovery and development pipeline from target identification through clinical trial optimisation.
Target Identification
Multi-omics integration analysing genomics, proteomics, and metabolomics data to identify novel therapeutic targets. Network pharmacology models predict off-target effects and polypharmacology opportunities.
Generative Molecular Design
Diffusion models and reinforcement learning generate novel molecules optimised for binding affinity, ADMET properties, and synthetic accessibility. Exploration of vast chemical spaces beyond known compound libraries.
Predictive Toxicology
Deep learning models trained on millions of compound-toxicity relationships predict hepatotoxicity, cardiotoxicity, and genotoxicity before synthesis. Reducing late-stage failures through early risk identification.
Molecular Simulation at Scale
Physics-informed neural networks accelerate molecular dynamics simulations by orders of magnitude. Free energy perturbation calculations that traditionally required weeks of supercomputer time complete in hours with equivalent accuracy.
Integration with Swissi HPC infrastructure enables massive parallel screening of virtual compound libraries, evaluating millions of candidates for binding affinity and selectivity.
AI-Augmented Pipeline
Every stage of drug development enhanced by specialised AI modules working in concert.
Target Discovery
Multi-omics analysis, disease pathway mapping, druggability assessment
Hit Generation
Generative chemistry, virtual screening, fragment-based design
Lead Optimisation
ADMET prediction, selectivity profiling, synthetic route planning
Preclinical
Toxicity prediction, PK/PD modelling, formulation optimisation
Clinical Trials
Patient stratification, endpoint prediction, protocol optimisation
Clinical Trial Intelligence
AI-driven patient stratification identifies responder populations before trial initiation. Predictive models analyse biomarkers, genetic profiles, and historical data to optimise cohort selection and improve statistical power.
Adaptive trial designs continuously learn from incoming data, adjusting dosing, endpoints, and enrollment criteria in real-time to maximise probability of success while minimising patient exposure and cost.
Regulatory Compliance
Built for pharmaceutical-grade regulatory requirements from day one.
FDA & EMA Guidelines
Full compliance with ICH guidelines, FDA 21 CFR Part 11, and EMA requirements for electronic records. AI model validation protocols aligned with regulatory expectations for ML in drug development.
GxP Standards
Good Laboratory Practice (GLP), Good Clinical Practice (GCP), and Good Manufacturing Practice (GMP) integration. Audit trails and documentation meeting pharmaceutical quality standards.
AI Act Compliance
High-risk AI classification framework with explainability requirements. Transparent model architectures with interpretable decision pathways for regulatory submissions.
Training & Certification
AI-powered training platform for pharmaceutical professionals meeting regulatory certification requirements and continuous education mandates.
Regulatory Training Programs
Comprehensive training modules covering GxP compliance, FDA/EMA submission requirements, and AI-specific regulatory frameworks. Certification pathways aligned with industry standards and regulatory expectations.
- 01GMP/GLP/GCP Certification Programs
- 02AI in Drug Development Specialisation
- 03Pharmacovigilance & Safety Training
- 04Clinical Trial Management Certification
AI-Powered Learning Platform
Personalised learning paths adapting to individual knowledge gaps and career objectives. Integration with Swissi UniAI infrastructure for seamless credential management and competency tracking.
- 01Adaptive Assessment & Knowledge Mapping
- 02Simulation-Based Practical Training
- 03Continuous Professional Development Tracking
- 04Industry-Recognised Digital Credentials
EQF 5-8
Qualification Levels
From technician to doctoral-level certifications
100%
Online Delivery
Flexible learning for working professionals
40+
Training Modules
Comprehensive pharma industry curriculum
EU/CH
Recognition
Aligned with European qualification frameworks
Market Opportunity
USD 71B
AI in Pharma by 2030
Global market for AI applications in pharmaceutical research and development
60%
Timeline Reduction
Target reduction in preclinical development timelines through AI acceleration
USD 2.6B
Per Drug Cost
Current average cost to bring a single drug to market
10x
Hit Rate Improvement
Projected improvement in lead candidate identification versus traditional screening
Swissi Ecosystem Integration
PharmaAI leverages the full Swissi infrastructure stack for competitive advantage.
Agnostyca Foundation Models
Domain-specific fine-tuning of Agnostyca base models for pharmaceutical applications. Molecular language models trained on proprietary compound datasets with full regulatory compliance.
Knowledge transfer from related Swissi verticals—MedAI clinical insights, Data AG knowledge graphs, and HPC computational resources.
Swissi HPC Infrastructure
Dedicated GPU clusters for molecular dynamics simulations and model training. Secure, compliant compute environment meeting pharmaceutical industry requirements.
Elastic scaling for peak workloads during virtual screening campaigns, with guaranteed capacity and data sovereignty.
Key Differentiators
End-to-End
Platform
Complete pipeline from target discovery through clinical trials—not point solutions.
Swiss
Data Sovereignty
Pharma-grade infrastructure in Swiss jurisdiction with full regulatory compliance.
Generative
Chemistry
Novel molecule design beyond known chemical space using state-of-the-art diffusion models.
Integrated
Ecosystem
Synergies with Swissi MedAI, HPC, and Agnostyca for unmatched capability breadth.
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