Ruthenium-Based Therapeutics
Combining bench-top synthesis with quantum chemistry, molecular docking, and machine learning to design the next generation of metal-based anticancer and antimycobacterial agents.
A Two-Pronged Research Strategy
My PhD research centres on ruthenium(III) and half-sandwich Ru–arene complexes bearing Schiff-base ligands. Unlike cisplatin — the gold-standard metal-based anticancer drug — ruthenium compounds can switch oxidation states inside the cell, potentially offering improved selectivity and lower toxicity.
For every compound I synthesise, I run a parallel computational study: DFT calculations reveal the electronic structure, HOMO–LUMO gaps predict reactivity, MEP maps show where the molecule wants to bind, and molecular docking places it inside a target protein to estimate affinity.
Lab results then confirm — or challenge — what the computer predicted. This dialogue between computation and experiment is the engine of my research.
View Publications →Synthesis & Characterisation
Multistep organometallic synthesis confirmed by ¹H/¹³C NMR, FT-IR, UV-Vis, single-crystal X-ray diffraction, and CHNS elemental analysis.
Computational Analysis
DFT geometry optimisation, HOMO–LUMO, MEP surface mapping (GAUSSIAN), and molecular docking studies (AutoDock, Discovery Studio) with ADME profiling.
Biological Evaluation
Anticancer, antimycobacterial, antioxidant, and DNA-binding assays conducted with partner laboratories; results integrated into SAR analyses.
Structure–Activity Relationships
Substituent effects, coordination geometry, and electronic parameters correlated with biological potency across 10+ complexes.
Core Research Areas
Ruthenium Metallodrugs
Designing Ru(III) and half-sandwich Ru(II)–arene complexes as next-generation anticancer and antimycobacterial agents with selectivity advantages over cisplatin.
Computational Drug Design
Applying DFT, HOMO–LUMO analysis, MEP mapping, and molecular docking to rationalise reactivity and predict binding affinities.
Structure–Activity Relationships
Correlating electronic structure, ligand substitution patterns, and molecular geometry with experimental biological activity to understand structure–activity relationships across a series of ruthenium complexes.
Biological Evaluation
Collaborating on anticancer, antimycobacterial, antioxidant, and DNA-binding studies and integrating the experimental findings with computational analyses to support mechanistic interpretation.
Piano-Stool Geometry
Half-sandwich [Ru(η⁶-p-cymene)(N,O-Schiff base)Cl]⁺ complexes adopt the distinctive piano-stool geometry: the η⁶-coordinated arene acts as the “seat”, while the bidentate Schiff base N,O donors and chloride ligand form the three “legs”.
This geometry is biologically significant — it exposes the chloride leaving group to aquation inside cells, while the arene ring controls lipophilicity and cellular uptake. Varying the Schiff-base substituents tunes HOMO–LUMO gaps, binding affinities, and ultimately anticancer potency.
Coordination number
6 (half-sandwich)
η⁶ arene
p-Cymene
Chelate ligand
N,O-Schiff base
Leaving group
Cl⁻ (aquation)
The η⁶ p-cymene arene ring (top) forms the “seat”; the Schiff-base N,O donors and chloride (bottom) form the “legs” — giving the characteristic piano-stool geometry. Based on compounds reported in ChemistrySelect (2025). Drag to rotate · scroll to zoom.
Machine Learning & Python Libraries
Python-based open-source ecosystem for computational chemistry, cheminformatics, and machine learning — applied to drug discovery and metallodrug research.
RDKit
CheminformaticsIndustry-standard Python library for cheminformatics. Used for molecule parsing (SMILES/SDF), fingerprint generation (Morgan, MACCS), substructure search, and property prediction in virtual screening pipelines.
DeepChem
ML / Drug DiscoveryDeep learning library for drug discovery and quantum chemistry. Applied for graph neural networks on molecular graphs, activity prediction, and ADMET modelling of ruthenium complex candidates.
Future Research Vision: AI-Driven Virtual Screening
A five-step AI pipeline that takes a large compound library and intelligently narrows it down to the most promising metal-complex candidates — ready for bench synthesis.
Library Preparation
Parse all candidate structures with RDKit; generate 3-D shapes; convert file formats for downstream analysis.
Drug-Likeness Filter
Apply Lipinski rules — checking molecular weight, water-solubility, and drug absorption potential — to keep only viable candidates.
AI Similarity Ranking
Use machine learning to score each molecule by how similar it is to known active Ru complexes from the literature.
Molecular Docking
Virtually place the top candidates inside the target protein using AutoDock and score how tightly each one binds (binding energy ΔG).
DFT Validation
Run quantum chemistry calculations on the shortlisted hits to verify their electronic structure and HOMO–LUMO gap. Best candidates go to the lab.
How AI Narrows Thousands to a Handful
Compound Library
All candidate Ru–Schiff-base structures
Drug-Like Filter
Pass molecular weight & lipophilicity rules
AI Similarity Ranking
Most similar to known active Ru complexes
Molecular Docking
Strongest predicted protein binding (ΔG)
DFT Validation
HOMO–LUMO & electronic structure verified
Ready for Synthesis
Shortlisted for the wet lab
Each stage filters smarter — only the most promising candidates reach the bench
Multi-Agent AI for Computational Drug Design
During my PhD, designing a Ru complex, running DFT in GAUSSIAN, docking in AutoDock, and extracting SAR insights was done manually — one compound at a time. Here is how specialised AI agents could automate that exact same workflow.
Chemistry Researcher
Sets the goal: Ru scaffold · substituent library · target protein (e.g. PARP-1 / HSA)
Orchestrator
Receives the research goal, breaks it into tasks, and assigns each to a specialised agent in sequence
Design Agent
RDKit · DeepChemEnumerates new Ru–Schiff-base candidates and scores predicted biological activity using ML models
↓ Candidate library
DFT Agent
GAUSSIANRuns B3LYP/LANL2DZ calculations automatically, extracts HOMO–LUMO gap and MEP surface data
↓ Electronic descriptors
Docking Agent
AutoDock · Discovery StudioDocks each candidate into the target protein binding pocket and returns predicted binding affinity (ΔG)
↓ Binding affinity scores
SAR Agent
Statistical AnalysisCorrelates electronic structure with binding affinity across the full series and ranks the best leads
↓ Ranked lead compounds
Shortlisted Lead Compounds
Top-ranked Ru complexes — filtered by HOMO–LUMO gap, binding ΔG, and ADME — passed directly to bench synthesis, saving weeks of manual iteration
10–100× Faster Iteration
Parallel DFT jobs and automated docking replace sequential manual steps, compressing weeks into hours.
Automated SAR Analysis
The SAR Agent correlates electronic descriptors with binding affinity across the full series — no spreadsheet required.
Intelligent Lead Prioritisation
Agents rank candidates by HOMO–LUMO gap, docking ΔG, and ADME filters before a single milligram is synthesised.
Closed-Loop Design
Experimental results from the bench feed back into the Design Agent, continuously refining the next generation of candidates.
Research Workflow
Molecular Design
Design Ru(III) and half-sandwich Ru(II)–arene Schiff-base complexes based on coordination chemistry principles and literature-guided ligand selection.
Synthesis
Multistep organometallic synthesis of Ru(III) and half-sandwich Ru(II)–arene Schiff-base complexes.
Characterisation
¹H/¹³C NMR · FT-IR · UV-Vis · Single-crystal X-ray (where applicable) · CHNS elemental analysis.
Computational Analysis
DFT geometry optimisation, HOMO–LUMO, MEP maps, and molecular docking in GAUSSIAN / AutoDock.
Biological Evaluation
Anticancer, antimycobacterial, antioxidant assays; DNA binding kinetics via fluorescence.
Structure–Activity Interpretation
SAR analysis correlates substituent effects with potency to guide the next synthetic cycle.
Key Publications
New Ru(III) 2,6-Bis(2-Benzimidazolyl)Pyridine Complexes Bearing p-Sub-Benzyl Thiosemicarbazones Schiff Base: Synthesis, Characterization, DNA Binding and Anti-cancer Activity
Ru(III) complexes with 2,6-bis(2-benzimidazolyl)pyridine and p-substituted benzyl thiosemicarbazone Schiff bases; DNA binding, anticancer activity, and full DFT characterisation.
Read paper ↗Design, Synthesis, and Biological Insights of Half-Sandwich Ruthenium–Arene Schiff Base Complexes: Molecular Docking and DFT
Half-sandwich η⁶-arene Ru(II) complexes with Schiff-base ligands; full structure-based computational analysis and anticancer/antimycobacterial biological evaluation.
Read paper ↗Design, Synthesis, Theoretical, Spectroscopic and Molecular Docking Studies of Ruthenium and Zinc Complexes and their Antimycobacterial Study
Ruthenium and zinc Schiff-base complexes with complete DFT, spectroscopic characterisation, molecular docking, and antimycobacterial evaluation.
Read paper ↗