# Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks

arXiv: https://arxiv.org/abs/2404.08019 · HTML: https://mlsanity.com/paper/2404.08019/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [AI Feynman: a Physics-Inspired Method for Symbolic Regression](https://mlsanity.com/paper/1905.11481/) (2019)
- [Universal Differential Equations for Scientific Machine Learning](https://mlsanity.com/paper/2001.04385/) (2020)
- [B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data](https://mlsanity.com/paper/2003.06097/) (2020)
- [A PINN Approach to Symbolic Differential Operator Discovery with Sparse Data](https://mlsanity.com/paper/2212.04630/) (2022)

## Built on by (most cited first, 6 of 6)

- [From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning](https://mlsanity.com/paper/2410.13228/) (2024)
- [CMINNs: Compartment Model Informed Neural Networks -- Unlocking Drug Dynamics](https://mlsanity.com/paper/2409.12998/) (2024)
- [A Unified Framework for Simultaneous Parameter and Function Discovery in Differential Equations](https://mlsanity.com/paper/2505.16996/) (2025)
- [Modeling Adoptive Cell Therapy in Bladder Cancer from Sparse Biological Data using PINNs](https://mlsanity.com/paper/2510.13431/) (2025)
- [Learning Model Parameter Dynamics in a Combination Therapy for Bladder Cancer from Sparse Biological Data](https://mlsanity.com/paper/2512.15706/) (2025)
- [Physics-Informed Neural Networks for Chemotherapy Pharmacokinetics: Benchmarking the Clinical Estimator and Exposing Parameter Identifiability](https://mlsanity.com/paper/2606.12658/) (2026)

## Abstract (arXiv)

Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However, significant manual distillation of the literature is needed in order to construct a QSP model. Parameters may need to be fit, and simplifying assumptions of the model need to be made. In this work, we apply Universal Physics-Informed Neural Networks (UPINNs) to learn unknown components of various differential equations that model chemotherapy pharmacodynamics. We learn three commonly employed chemotherapeutic drug actions (log-kill, Norton-Simon, and E_max) from synthetic data. Then, we use the UPINN method to fit the parameters for several synthetic datasets simultaneously. Finally, we learn the net proliferation rate in a model of doxorubicin (a chemotherapeutic) pharmacodynamics. As these are only toy examples, we highlight the usefulness of UPINNs in learning unknown terms in pharmacodynamic and pharmacokinetic models.