Led by Dr. Fidelis Ndombera, PhD

Science Meets
Intelligence

Bridging the gap between biochemical rigor and artificial intelligence to solve Africa's most complex health challenges.

115+
Citations
15+
Publications
High
Impact Factor
54
Countries

Trusted Collaboration Network

U
University of Nairobi
K
Kenyatta University
D
DeepMind Health
M
Matawis AI
P
PawaEye AI
E
EXA Research
U
University of Nairobi
K
Kenyatta University
D
DeepMind Health
M
Matawis AI
P
PawaEye AI
E
EXA Research
U
University of Nairobi
K
Kenyatta University
D
DeepMind Health
M
Matawis AI
P
PawaEye AI
E
EXA Research

Critical Research Vectors

Targeting the intersection of infectious pathology, genetic predisposition, and environmental factors in African populations.

Infectious Diseases

AI-driven optimization for Malaria & TB diagnostics. Leveraging data to predict outbreaks and resistance patterns.

  • Pathogen Identification
  • Resistance Prediction

Genetic Genomics

Precision medicine for Sickle Cell & Thalassemia. Mapping African genomic diversity to tailor effective therapies.

  • Personalized Medicine
  • Gene Editing Targets

Oncology

Novel glycoside agents for lung and cervical cancer. Reducing toxicity while enhancing bioavailability.

  • Drug Discovery
  • Clinical Protocols
Published Works

Scientific Contributions

View on Google Scholar
Pharmacokinetic, physicochemical and medicinal properties of n-glycoside anti-cancer agent more potent than 2-deoxy-d-glucose in lung cancer cells
Featured Publication
2019Journal of Pharmacy and Pharmacology

Pharmacokinetic, physicochemical and medicinal properties of n-glycoside anti-cancer agent more potent than 2-deoxy-d-glucose in lung cancer cells

This groundbreaking study presents a novel n-glycoside anti-cancer agent that demonstrates superior efficacy compared to 2-deoxy-d-glucose in lung cancer cell lines. Our research reveals significant improvements in pharmacokinetic properties and medicinal potential.

Molecular BioSystems40 Citations

A clickable glutathione approach for identification of protein glutathionylation in response to glucose metabolism

We developed an innovative clickable glutathione approach that enables precise identification of protein glutathionylation in cellular responses to glucose metabolism.

Protein ChemistryMetabolism
View Citation
Bioorganic & Medicinal Chemistry Letters10 Citations

Carbohydrate-based inducers of cellular stress for targeting cancer cells

This research introduces novel carbohydrate-based compounds that selectively induce cellular stress in cancer cells while sparing healthy tissue.

Cancer TherapyCarbohydrate Chemistry
View Citation
Pure and Applied Chemistry8 Citations

Anti-cancer agents and reactive oxygen species modulators that target cancer cell metabolism

This comprehensive review examines the role of reactive oxygen species modulators in cancer therapy, focusing on agents that specifically target altered metabolic pathways.

Cancer MetabolismROS Modulation
View Citation
Journal of Bioinformatics and Computational Genomics4 Citations

Revisiting cheminformatics and mechanisms of action of chloroquine and hydroxychloroquine in targeting COVID-19

During the COVID-19 pandemic, this timely research provided critical analysis of chloroquine and hydroxychloroquine mechanisms through advanced cheminformatics approaches.

COVID-19Drug Repurposing
View Citation
Cancer Causes & Control2 Citations

The role of infections in the causation of cancer in Kenya

This important epidemiological study examines the relationship between infectious diseases and cancer development in Kenya, providing crucial data for understanding cancer etiology.

Cancer EpidemiologyInfectious Disease
View Citation
Pawanax Intelligence

Accelerating
Drug Discovery

Our AI pipeline reduces lead optimization time from years to months. By training on diverse African datasets, we identify molecules that major global pharma overlooks.

⚛️

Molecular Modeling

3D structural prediction of n-glycosides

🧪

ADMET Prediction

AI-driven toxicity and metabolic profiling

🧬

Clinical Simulation

Virtual patient trials with genetic diversity

PROCESSING: MOLECULE_ID_992CONFIDENCE: 98.4%
500B+Parameters Analyzed

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