AI-Powered Drug Discovery

Accelerating Cures for
Rare Diseases

Siloam Therapeutics harnesses the power of Agentic AI to discover and develop breakthrough therapies for patients with rare diseases who have no other options.

Explore drug.AI View Pipeline
drug.AI Agentic Platform
2+ Active Programs
Rare Disease Focus
About Us

Redefining What's Possible in Drug Discovery

At Siloam Therapeutics, we believe every patient deserves access to effective treatment — even when they have a rare disease that affects only a small population. Our mission is to leverage the transformative power of artificial intelligence to accelerate discovery of safe, effective therapies for underserved patients.

🧬

AI-First Approach

Our Agentic AI platform automates and accelerates every step of the drug discovery process, from target identification to lead optimization.

🎯

Rare Disease Focus

We dedicate our technology and expertise to diseases where patients have few or no treatment options, driven by genuine medical need.

⚡

Speed & Precision

By combining generative AI with computational chemistry, we compress traditional drug discovery timelines from years to months.

Our Platform

Introducing drug.AI

drug.AI is our proprietary Agentic AI platform — an autonomous, multi-agent system designed to revolutionize drug discovery. It operates as a continuously learning scientific intelligence that reasons, plans, and executes complex discovery workflows with unprecedented speed and accuracy.

⬡

drug.AI

Agentic AI Core Technology Platform

Multi-Agent Generative AI Graph Neural Networks GraphRAG
01 Generative AI

Agentic AI — De Novo Small Molecule Drug Discovery

Our De Novo Small Molecule discovery engine uses a published multi-agent AI framework to autonomously design novel drug candidates from first principles. The system integrates generative molecular design, multi-objective optimization, and AI-driven ADMET prediction to generate highly optimized small molecule candidates against specific disease targets.

  • Generative molecular scaffold design
  • Multi-objective property optimization
  • AI-driven ADMET prediction
  • Autonomous iterative lead refinement
  • In silico binding affinity simulation
Disease Target
→
AI Design
→
Optimization
→
Lead Candidate
02 Graph AI

Agentic AI — Drug Target Identification

Our Drug Target Identification engine combines Graph Neural Networks (GNN), Agentic AI reasoning, and GraphRAG technology to systematically map and rank the underlying genes, proteins, and biological pathways most relevant to a given rare disease — surfacing actionable drug targets with high therapeutic potential.

  • Graph Neural Network (GNN) modeling
  • GraphRAG knowledge retrieval
  • Multi-omics data integration
  • Pathway enrichment & network analysis
  • Target druggability scoring
Disease Data
→
Graph AI
→
Network Analysis
→
Target ID
R&D Pipeline

Advancing Treatments for Rare Diseases

Our pipeline focuses on rare diseases with significant unmet medical need, leveraging drug.AI to accelerate the path from in silico discovery to the clinic.

Program Indication Modality In Silico In Vitro / In Vivo IND-Enabling Phase I
ST-001
HER2+ Rare HER2-Driven Cancer
Small Molecule
Completed
In Progress
ST-002
CF Cystic Fibrosis
Small Molecule
In Progress
In Progress
Completed
In Progress
Planned
ST-001

HER2+ Rare Cancer

✓
In Silico Discovery

Completed — Novel lead compounds identified using drug.AI platform

◉
In Vivo / In Vitro Validation

In Progress — Biological validation of AI-generated lead candidates

ST-002

Cystic Fibrosis

◉
In Silico Discovery

In Progress — Agentic AI designing novel CFTR-modulating candidates

◉
In Vivo / In Vitro Validation

In Progress — Parallel experimental validation ongoing

Our Team

Leadership Team

Our founders bring together deep expertise in AI, computational biology, and regulatory science to build the next generation of drug discovery.

Dr. Dony Ang

Dr. Dony Ang

CEO / CTO / Co-founder

Dony Ang, Ph.D. is the CEO/CTO/cofounder of Siloam Therapeutics. A veteran data scientist with over two decades of experience across major technology companies from Yahoo! to Google and recently with Accenture, he specializes in generative AI, reinforcement learning, and large-scale data systems applied to molecular design. He earned his Ph.D. in Computational Data Science from Chapman University.

Generative AI Reinforcement Learning Molecular Design
Linna Makmura

Linna Makmura

COO / Co-founder

Linna Makmura is the Chief Operations Officer and Chief Regulatory Officer at Siloam Therapeutics. A seasoned research compliance and operations professional with 20+ years of experience streamlining complex research-regulatory ecosystems, she brings a founder's mindset — data-driven, systems-oriented, and relentlessly committed to reducing friction. She ensures the speed of AI-driven drug discovery is matched by a rigorous "compliance-by-design" framework, allowing innovations to transition safely from digital models to human clinical trials.

Regulatory Affairs Operations Compliance by Design
Get In Touch

Partner With Us

Whether you're a patient advocacy group, pharmaceutical partner, or investor interested in the future of AI-driven rare disease therapeutics — we'd love to hear from you.

📧 info@siloamtherapeutics.com
🔬 Scientific & Research Inquiries
🤝 Partnership & Collaboration Opportunities
💼 Investor Relations