Aktiver/in-silico-experiment-agent

🧬 Aktiver In-Silico Lab Experiment Agent

Precision Therapeutics | Verified Reasoning | Real-Time Simulation

The Aktiver In-Silico Lab Experiment Agent is a personalized medicine engine that tailors treatment simulation to each patient’s phenotype and molecular context. Built to support clinician decision-making, it replaces static treatment assumptions with real-time, patient-specific experimentation grounded in curated biomedical tools.

💡 What is In Silico Experimentation?

“In silico” refers to computational experimentation—using simulations to model drug-target interactions, treatment effects, and biological outcomes. This agentic system evaluates therapies across pharmacokinetics, contraindications, comorbidities, and target relevance before any in vivo trial or intervention, with high potential to replace in vivo experiments.

🧠 Built on Verified Medical Knowledge

The agent draws from 190+ Clinical Laboratory Tools, which integrates:

• FDA-approved drugs
• Target-pathway-disease insights from Open Targets
• Clinical annotations via the Human Phenotype Ontology

It dynamically assembles toolchains based on patient queries, performing reasoning that is both personalized and evidence-aligned.

🚀 Required model to build agent

Aktiver CoreTX Agent https://huggingface.co/Aktiver/CoreTX-Agent

Highlights:

• ✅ 90% accuracy on open-ended drug reasoning tasks
• 🚀 Surpasses GPT-4o by 25%%
• 💡 Outperforms DeepSeek-R1 (671B) in structured tool-use tasks
• ⚖️ <0.01 variance between brand/generic/description references
• 🧠 +50% reasoning accuracy over baseline tool-LLMs

🛠️ Training Architecture: ToolRAG Reasoning Agent

• Base model: gte-Qwen2-1.5B-instruct
• Lab Equipment model fine-tuned on >150k reasoning traces + 250k lab tool calls
• Uses multiple negatives ranking loss for tool-context alignment
• Dual-stage bootstrapping loop improves tool selection over time

This creates an iteratively improving LLM-tool ecosystem: every generated reasoning trace improves the next.

🧪 Use Cases

• Simulate patient-specific drug regimens
• Predict adverse reactions before real-world exposure
• Explore repurposing options for rare or complex diseases
• Match molecular targets to treatment candidates for oncology, Alzheimer’s, lupus, and many more advanced diseases

🧭 Why This Matters

In silico methods are transforming medicine—from whole-cell simulation to AI-guided treatment design. The Aktiver Lab Experiment Agent brings together:

• Real-time reasoning
• Verified biomedical tools
• Transparent, step-by-step justification
• Personalized outputs aligned with FDA, Open Targets, and clinical ontologies

This is the future of adaptive, evidence-grounded therapeutic design.

This method enables the reasoning trace to more accurately mirror real-world scenarios by having the model agent directly retrieve laboratory tools. The process is repeated in cycles, progressively enhancing both the structure of experimental clinical workflows and the precision of reasoning traces derived from the RDF Knowledge Graph.

License: Proprietary
Maintainer: Aktiver
Source Data and Ontologies: openFDA, Open Targets, Human Phenotype Ontology