LLM Applications for Cancer Drug Discovery and Oncology Clinical Trials
Collection of Manuscripts 01-20 strengths, limitations, and results based on LLM-driven oncology research by ChemicalQDevice. 50+ Abstracts and Diagrams. Industry Guidance.
ChemicalQDevice develops AI applications for oncology clinical trials and pharmaceutical research. Anthropic, Google, OpenAI, and xAI large language models (LLMs) are utilized to generate machine learning training code, clinical trial simulations, drug discovery pipelines, and multi-model research workflows.
This repository contains 2,300+ files across three primary directories spanning LLM/AI code notebooks, published manuscripts, and R&D documentation.
| Metric | Value |
|---|---|
| Jupyter Notebooks | 1,446 |
| Python Scripts | 35 |
| Published Manuscripts | 17+ (with DOIs) |
| R&D Documents | 70 |
| Research Directories | 28 |
| Repository Size | 801 MB |
LLMs-Pharmaceutical/
├── Code/ # 2,235 files — Notebooks, scripts, and research code
│ ├── Digital_Twin_PDAC/ # 10 notebooks — PDAC digital twin clinical trial proposals
│ ├── Drug Discovery/ # 290 files — 14 subdirectories: LLM, RAG, agentic, multi-model
│ │ ├── Agentic-LLM/ # 4 notebooks — Agentic LLM drug discovery workflows
│ │ ├── Multi-LLM/ # 15 notebooks — Multi-LLM comparisons, meta-analyses, reports
│ │ ├── Quad-LLM/ # 14 notebooks — Quad-LLM evaluation pipelines
│ │ ├── ProtBert/ # 1 notebook — Protein language model experiments
│ │ ├── ProtGPT2/ # 2 notebooks — Protein generation models
│ │ ├── Llama-3 RAG/ # 2 notebooks — Llama-3 retrieval-augmented generation
│ │ ├── Meta-Llama-3/ # 6 notebooks — Meta Llama-3 fine-tuning and inference
│ │ └── ... # (LLM, LLM-RAG, LMM, XLLM, pLDM, iPhone 15 Pro)
│ ├── Hugging Face/ # 15 notebooks — Llama-3 fine-tuning, 32-pipeline benchmark
│ ├── LangChain/ # 9 notebooks — Agent Supervisor, Code Assistant, RAPTOR
│ ├── Generative AI Live/ # 6 notebooks — Live coding: RAG, RAPTOR, Agent, fine-tuning
│ ├── Groq/ # 1 notebook — GroqCloud inference benchmarks (4 models)
│ ├── Open WebUI/ # — Ollama/Docker deployment studies
│ ├── Python/ # 35 scripts — Python tutorial series (Classes → Logging)
│ ├── C++/ # 47 files — C++ tutorial source files
│ ├── Apple/ # — LLM Farm: MacBook Pro, iPad Pro, iPhone 15 Pro
│ ├── PennyLane/ # 488 notebooks — QML algorithms and benchmarking
│ ├── Qiskit/ # 18 notebooks — Quantum programming and device benchmarks
│ └── ... # (QML and quantum parallel algorithm directories)
│
├── Manuscripts/ # Published research — 17+ papers with DOIs
│ ├── README.md # 67 KB — Full abstracts, citations, and DOI badges
│ └── LICENSE.md # CC BY 4.0
│
├── README.md # This file
├── LICENSE.md # Apache License 2.0
├── CHANGELOG.md # Version history
├── CONTRIBUTING.md # Contribution guidelines
├── CODE_OF_CONDUCT.md # Community standards
├── SECURITY.md # Security policy
├── SUPPORT.md # Getting help
├── CITATION.cff # Citation metadata
├── ruff.toml # Linter configuration
├── .gitignore # Git ignore rules
├── .github/ # CI workflows and templates
├── Daraxonrasib Efficient LLM Trial Simulations.pdf
├── LaTeX LLM Oncology Table Prompting Guide.pdf
└── Scaling Oncology LLM Generated Code Jan 2026.pdf
The Manuscripts/ directory contains the full abstracts, citations, and DOI links for all published work. Key publications are summarized below in reverse chronological order.
| Date | Title | DOI |
|---|---|---|
| Dec 2025 | Code Generation Competition: 16 Proprietary vs. Open-Source LLMs & Iterative Learning Based on FDA FAERS | 10.5281/zenodo.18029100 |
| Nov 2025 | AI Peer Review Acceleration of LLM-Generated Glioblastoma Clinical Trial Patient Matching ML | 10.5281/zenodo.17774560 |
| Nov 2025 | LLM-Generated Glioblastoma Drug Synergy Machine Learning | 10.5281/zenodo.17614396 |
| Oct 2025 | End-to-End Oncology Clinical Trial LLM Efficiency For Industry Adoption | 10.5281/zenodo.17451709 |
| Sep 2025 | Accelerating FDA Compliance via AI Digital Twin Pancreatic Cancer Simulation | 10.5281/zenodo.17239510 |
| Aug 2025 | QSP Metastatic Pancreatic Cancer AI Clinical Trial Simulation | 10.5281/zenodo.17001137 |
| Jul 2025 | ChatGPT 100,000 Patient 24-Month In Silico Phase III Clinical Trial | 10.5281/zenodo.16415815 |
| Jun 2025 | End-to-End PDAC Digital Twin Clinical Trial Proposals | 10.5281/zenodo.15735068 |
| May 2025 | 10 Year Glioblastoma Clinical Trial Meta-Analyses by Autonomous AI | 10.5281/zenodo.15549831 |
| Apr 2025 | AI Revolution Toward the Cure of Lung Adenocarcinoma | 10.5281/zenodo.15278152 |
| Mar 2025 | Autonomous LLM Agent and Scalable Reasoning LLM for Cancer Drug Cost Solutions | 10.5281/zenodo.15072843 |
| Feb 2025 | Cost Containment of Global mAb Drugs and Cancer Clinical Trials via LLM Reasoning | 10.5281/zenodo.14968404 |
| Jan 2025 | Clinical Decision Support Based on Bevacizumab Cancer Trials | 10.5281/zenodo.14968162 |
| Date | Title | DOI |
|---|---|---|
| Dec 2024 | Cancer vs. Conversational Artificial Intelligence | 10.1101/2024.12.28.630597 |
| Nov 2024 | mAb Bioprocess Engineering In-Context Table Forecasts | 10.26434/chemrxiv-2024-jzbj0 |
| Oct 2024 | Monoclonal Antibody Bioprocess Engineering Using Conversational AI | 10.26434/chemrxiv-2024-3m7m1 |
| Oct 2024 | Paclitaxel Biosynthesis AI Breakthrough | 10.26434/chemrxiv-2024-pqjd3 |
Full abstracts and citation details are available in
Manuscripts/README.md.
Code/Digital_Twin_PDAC/
├── Notebooks/
│ ├── Python_01_RP01.ipynb ─── Research proposal report 1
│ ├── Python_02_RP02.ipynb ─── Research proposal report 2
│ ├── Python_03–05_RP*.ipynb ─── Continued proposal reports
│ ├── Python_06_RP06.ipynb ─── Extended proposal analysis (5.4 MB)
│ ├── Python_07_Pro5Viz.ipynb ─── 5-proposal visualizations
│ ├── Python_08_Pro5Tab.ipynb ─── 5-proposal tabular data
│ ├── Python_09_ProOps4.ipynb ─── Opus 4 Extended proposal
│ └── Python_10_ProSon4.ipynb ─── Sonnet 4 Extended proposal
└── Images/
├── Proposals/
└── Reports/
10 notebooks implementing a multi-AI pipeline for pancreatic ductal adenocarcinoma (PDAC) digital twin clinical trial proposals. Seven AI model combinations (o3re, g25p, son4, grk3, o3pr, ops4, o3ch) generated 40 meta-analyses, 6 reports, and 5 trial proposals scored up to 9.60/10.
Code/Drug Discovery/
├── Agentic-LLM/ # 4 notebooks — Agentic LLM workflows
├── Multi-LLM/ # 15 notebooks — Multi-model comparisons and meta-analyses
│ ├── Images/
│ ├── Notebooks/
│ ├── Reports/
│ └── Meta-Analyses/
├── Quad-LLM/ # 14 notebooks — Four-model evaluation pipelines
│ ├── Images/
│ └── Notebooks/
├── ProtBert/ # 1 notebook — Protein language model
├── ProtGPT2/ # 2 notebooks — Protein generation
├── Meta-Llama-3/ # 6 notebooks — Llama-3 fine-tuning
├── Llama-3 RAG/ # 2 notebooks — Retrieval-augmented generation
├── LLM/ # LLM approach documentation
├── LLM-RAG/ # Drug synthesis RAG
├── LMM/ # Large multimodal models
├── XLLM/ # Precision medicine
├── pLDM/ # 2 notebooks — Protein latent diffusion
└── iPhone 15 Pro/ # On-device inference
290 files across 14 subdirectories covering LLM, agentic, RAG, multi-model, and protein language model approaches to drug discovery. Key studies include Groq inference benchmarks (~1000 tokens/sec), Hugging Face 32-pipeline evaluations, and LangChain agent workflows.
Code/Hugging Face/ # 15 notebooks — 32-pipeline benchmark, Llama-3 fine-tuning
Code/LangChain/ # 9 notebooks — Agent Supervisor, Code Assistant, RAPTOR
Code/Generative AI Live/ # 6 notebooks — Live demos: RAG, RAPTOR, Agent, fine-tuning
Code/Groq/ # 1 notebook — GroqCloud 4-model inference comparison
Code/Open WebUI/ # — Ollama/Docker GenAI deployment
Earlier work focused on quantum-inspired machine learning (QiML) using PennyLane and Qiskit frameworks. This represents the largest portion of notebooks by count (600+) and covers algorithm prototyping, parallel quantum architectures, tensor networks, and device benchmarking.
Code/PennyLane/ # 488 notebooks
Code/Qiskit/ # 18 notebooks
Code/All PennyLane QML Demos/ # 26 notebooks
Code/All Qiskit ML Demos/ # 13 notebooks
Code/All Qiskit, PennyLane QML Nov 23/ # 54 notebooks
Code/Efficiency Metrics for Parallel QML Algorithms/
Code/Parallel Quantum Algorithms torch.nn Seq, Mod/
Code/PyTorch, Keras, PL Parallel Quantum Algorithms/
Code/QML Parameters for Breakthrough Parallel Algorithms/
Code/Quantum Parallel Architectures Progression/
Code/Tensor Network vs FC Controllability/
Code/Tensor Network vs FC Explainability/
Code/Tensor Network vs Fully Connected Layer/
Code/Tensor Networks for Generative AI/
2023 ──── Quantum ML foundations (PennyLane, Qiskit, parallel algorithms)
│
2024 ──── LLM pivot: drug discovery, spectrometric analysis, bioprocess engineering
│ ├── Multi-LLM and Quad-LLM evaluation frameworks
│ ├── Protein language models (ProtBert, ProtGPT2)
│ ├── Hugging Face 32-pipeline benchmark
│ └── LangChain agents and RAG pipelines
│
2025 ──── Clinical trial scale-up and regulatory workflows
│ ├── Digital Twin PDAC (7 AI models, 40 meta-analyses)
│ ├── 100K patient in silico Phase III trials
│ ├── QSP pancreatic cancer simulations (250 ODEs/patient)
│ ├── End-to-end oncology trial LLM efficiency (52-73% faster, 24-50% less cost)
│ ├── Glioblastoma drug synergy ML (accuracy 0.9804)
│ ├── AI peer review workflows
│ └── 16-LLM code generation competition on FDA FAERS
│
2026 ──── Scaling and methodology
├── Scaling Oncology LLM Generated Code
├── Daraxonrasib Efficient LLM Trial Simulations
└── LaTeX LLM Oncology Table Prompting Guide
- Trial time reduction: 52–73% faster with LLM-assisted workflows (Oct 2025)
- Trial cost reduction: 24–50% less expensive; 99.6–99.9997% cost reduction vs. in-person trials
- Per-patient cost: $3.6 (in silico) vs. $59,500 (traditional) — a 16,528x difference
- Drug synergy prediction: 0.9804 accuracy, 0.9705 Macro-F1 on glioblastoma drug pairs (Nov 2025)
- In silico scale: 100,000 patients, 24-month, 5-arm Phase III triplicate (Jul 2025)
- AI experiment speed: Core experiments completed in 3.1–5.4 hours (May 2025)
- Proposal scores: Up to 9.60/10 for PDAC digital twin trial proposals (Jun 2025)
- Code and documentation: Apache License 2.0
- Manuscripts: CC BY 4.0
@misc{kawchak2026llmspharmaceutical,
author = {Kawchak, Kevin},
title = {LLMs-Pharmaceutical: LLM Applications for Cancer Drug Discovery and Oncology Clinical Trials},
year = {2026},
publisher = {GitHub},
url = {https://github.com/kevinkawchak/LLMs-Pharmaceutical},
doi = {10.5281/zenodo.13273141}
}Kevin Kawchak — CEO, ChemicalQDevice, San Diego, CA kevink@chemicalqdevice.com