Physics-based, fast and bias-free

Quantitative Complexity Theory (QCT) is the foundation of Artificial Intuition. It quantifies the complexity of molecules producing an Atomic Participation Factors spectrum, mapping how information is encoded. The key output is a ranking of atoms in terms of their information footprint and impact on molecular dynamics. A similar spectrum is produced for proteins, indicating which amino acids drive dynamics and structural robustness.

Training-free, universal applicability across novel targets, QCT delivers prioritization insights without requiring training data, enabling rapid deployment across novel targets and data-sparse therapeutic areas including orphan and rare diseases.

Beyond Machine Learning.

QCT's deterministic, training-free, highly scalable approach enables independent Lead Optimization guidance while seamlessly augmenting both medicinal chemistry expertise and AI/ML-driven active learning. Fast, physics-based and bias-free.

QCT, in combination with Molecular Dynamics, pinpoints atoms driving biological activity via Participation Factor analysis. This improves Compound Prioritization, providing better guidance about which chemical modifications are likely to succeed, reducing time and resouces spent on low-potential compounds.

Physics-based guidance for rational lead optimization

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Methodology

• Run a Molecular Dynamics Simulation of lead compound in water (200ns).

• Process resulting PDB file with QCT.

• QCT quantifies the molecule’s dynamic complexity and its breakdown into Atomic Participation Factors.

• The largest Participation Factors drive the molecule’s dynamics and constitute the “complexity hotspots” and potential pharmacophores. Moreover, these hotspots are the drivers of biological function.

Publications and Articles

“Artificial Intuition and accelerating the process of antimicrobial drug discovery” Computers in Biology and Medicine, 188 (2025) 109833, DOI: Artificial Intuition and accelerating the process of antimicrobial drug discovery - ScienceDirect article download

“Protein Folding – Quantification of Protein Complexity, Robustness and Amino Acid Participation”, Biomedical Journal of Scientific and Technical Research, Vol. 50, Issue 2. DOI: 10.26717/BJSTR.2023.50.007914, article download

“Complexity Quantification and Comparison of Two Commercially Available Anti-Coagulants”, Biomedical Journal of Scientific and Technical Research, Vol. 49, Issue 2. DOI: 10.26717/BJSTR.2023.49.007765, article download

“Quantitative Complexity Theory (QCT) in Integrative Analysis of Cardiovascular Hemodynamic Response to Posture Change”, Life 2023, 13(3), 632; https://doi.org/10.3390/life13030632, article download

“Quantitative Complexity Theory Used in the Prediction of Head-Up Tilt Testing Outcome”, Cardiology Research and Practice, Volume 2021, Article ID 8882498, Download

"Quantitative Complexity Theory (QCT) in Integrative Analysis of Hemodynamic Response to Posture Change”

Poster, Download

“Quantitative Complexity Theory (QCT) in Prediction of Head-up Tilt Testing Outcome”

Poster, Download

"Real-time calculation of system-level complexity during trauma/haemorrhage: can we do it?"

American Heart Association Annual Meeting, 2011, Download

"Changes in Systems-level Complexity Precede Deterioration in Traditional Vital Signs in Hypoxic Cardiac Arrest"

USAISR Poster, Download

"Baseline Heart Rate Variability Predicts Clinical Events in Heart Failure Patients Implanted with Cardiac Resynchronization Therapy: Validation by Means of Related Complexity Index."

Annals of Noninvasive Electro-Cardiology, October 2010, Vol. 15, No.4, HRV Indices in Heart Failure. Download

"Analysis of ECG by means of Complexity Index and Association with Clinical Response to Cardiac Resynchronization Therapy",

Journal of Cardiovascular Disease, Vol. 2, No. 2, April 2014, Download

"Electrocardiogram predicts response to cardiac resynchronization therapy assessment by means of complexity index"

Journal of Critical Care, ICCAI 2013, Download