This article and associated images are based on a poster originally authored by Olufunso O. Abosede and presented at ELRIG Drug Discovery 2026 in affiliation with Federal University Otuoke.
This poster is being hosted on this website in its raw form, without modifications. It has not undergone peer review but has been reviewed to meet AZoNetwork's editorial quality standards. The information contained is for informational purposes only and should not be considered validated by independent peer assessment.
1. Introduction
Artificial intelligence (AI) and machine learning (ML) are transforming medicinal chemistry by enabling data-driven prediction, optimization, and prioritization across the drug discovery pipeline. This work presents a two-pronged approach: (i) machine-learning models for predicting chemical reaction outcomes and (ii) machine-learning models for early toxicity assessment of aromatic compounds. Building on these results, we discuss AI's potential to accelerate the discovery and optimization of transition-metal and metal-based therapeutics.
AI enhances decision-making by analyzing complex datasets, identifying patterns and providing predictive insights, thereby reducing time, cost and uncertainty in drug development.
Integrating machine learning with medicinal chemistry, coordination chemistry, crystallography, and structure–property relationships offers a promising framework for rationally designing next-generation therapeutics, including metal-based drugs, with improved efficacy, selectivity, and safety.
Image Credit: Olufunso O. Abosede in partnership with ELRIG (UK) Ltd.
2. AI/ML methodology
2.1 Reaction yield and purity prediction
Image Credit: Olufunso O. Abosede in partnership with ELRIG (UK) Ltd.
2.2 Toxicity prediction (aromatic compounds)
Image Credit: Olufunso O. Abosede in partnership with ELRIG (UK) Ltd.
2.3 Explainable AI
- SHAP analysis
- Feature importance
- Feature interactions
- Model interpretability
3. Key results
3.1 Chemical reaction yield and purity prediction
Source: Federal University Otuoke
| Model |
R2 |
MSE |
RMSE |
MAE |
| CatBoost |
0.92 |
68.12 |
8.25 |
5.67 |
| XGBoost |
0.90 |
74.36 |
8.62 |
6.43 |
Feature importance (SHAP)
Image Credit: Olufunso O. Abosede in partnership with ELRIG (UK) Ltd.
3.2 Toxicity prediction (aromatic compounds)
Source: Federal University Otuoke
| Model |
Accuracy |
Precision |
Recall |
F1 score |
Specificity |
| Random Forest |
1.00% |
1.00% |
1.00 |
1.00 |
1.00 |
| SVM |
0.80 |
0.79 |
0.81 |
0.80 |
0.82 |
| Linear Regression |
0.62 |
0.63 |
0.60 |
0.59 |
0.61 |
Important molecular descriptors
Image Credit: Olufunso O. Abosede in partnership with ELRIG (UK) Ltd.
4. Integration with medicinal chemistry
Image Credit: Olufunso O. Abosede in partnership with ELRIG (UK) Ltd.
5. Future research direction
Image Credit: Olufunso O. Abosede in partnership with ELRIG (UK) Ltd.
6. Conclusions
- ML models (CatBoost, XGBoost, Random Forest, SVM) effectively predict chemical reaction outcomes and toxicity in aromatic compounds.
- Explainable AI (SHAP) enhances model interpretability and provides valuable chemical insights.
- These findings provide a strong foundation for AI-assisted discovery and optimization of metal-based therapeutics.
- Future work will integrate these domains into a unified medicinal-chemistry platform for the rational design of next-generation metal drugs.
About Federal University Otuoke 
The Federal University Otuoke (FUO) is one of the Federal Universities established by the Federal Government of Nigeria in February 2011 in cognisance of the need to bridge the great divide between the educationally advantaged and disadvantaged sections of the country, especially areas where universities were lacking. Cited in the serene and secure Otuoke Community, at the heart of the oil-rich Niger-Delta region of Bayelsa State, the University which is 21 kilometers south of the state capital Yenagoa, occupies an expanse of 200 hectares (494 acres).
Our Mission is the generation, dissemination, preservation, and application of knowledge. The vision of the Federal University, Otuoke (FUO) is to gain and maintain a reputation as a world-class university that challenges all its students to achieve the highest levels of intellectual and personal growth, to promote sustainable development, as well as contribute purposeful and ethical service to the nation and Mankind. We intend to achieve this through our core values captured as FUO LIKES: Learning, Integrity, Knowledge, Excellence, Service and Scholarship.
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Last Updated: Oct 27, 2026