Advanced MaterialRuthenium Chemistry

Dr. Farha Arshi

Synthetic, Computational & AI-Driven Chemist

Accelerating advanced materials discovery through molecular design and artificial intelligence

Computational ChemistryAI Assisted MaterialsDFT ModellingMolecular Docking
Dr. Farha Arshi — Synthetic & Computational Chemist
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University of Lucknow, India

PhD, Chemistry · NAAC A++

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Years Research

About the Doctoral Research

From Bench to Bits — A Two-Pronged Approach

Imagine designing a molecular key that fits a cancer-related protein target, then using computational chemistry to understand why it behaves the way it does.

During my PhD, I synthesised ten novel ruthenium complexes and investigated their electronic structure, reactivity, and protein-binding behaviour using Density Functional Theory (DFT) and molecular docking. By integrating experimental synthesis with computational analysis, I correlated structural features with chemical and biological properties, providing mechanistic insights that guided the interpretation of experimental results.

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Research Approach

  • 01Synthesise Ru(III) & half-sandwich Ru(II)–arene Schiff-base complexes
  • 02Confirm structures by NMR, X-ray diffraction, FT-IR & UV-Vis
  • 03Model electronic properties via DFT (GAUSSIAN)
  • 04Predict binding via molecular docking (AutoDock / Discovery Studio)
  • 05Establish SAR from combined computational & biological data
Focus Areas

Doctoral Research Domains

Ruthenium Chemistry

Designing Ru(III) and half-sandwich Ru(II)–arene complexes as next-generation anticancer and antimycobacterial agents with selectivity advantages over cisplatin.

Organometallic SynthesisNMR / X-ray DiffractionDNA Binding StudiesAnticancer Assays

Computational Drug Design

Applying DFT, HOMO–LUMO analysis, MEP mapping, and molecular docking to rationalise reactivity and predict binding affinities.

GAUSSIAN (DFT)AutoDock / Discovery StudioHOMO–LUMO & MEPADME Prediction

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.

SAR AnalysisElectronic Descriptor AnalysisSubstituent Effect StudiesComputational–Experimental Correlation

Biological Evaluation

Collaborating on anticancer, antimycobacterial, antioxidant, and DNA-binding studies and integrating the experimental findings with computational analyses to support mechanistic interpretation.

MTT Anticancer AssayMIC AntimycobacterialDPPH AntioxidantDNA Binding Fluorescence
Scientific Process

Doctoral Research Workflow

01

Molecular Design

Design Ru(III) and half-sandwich Ru(II)–arene Schiff-base complexes based on coordination chemistry principles and literature-guided ligand selection.

02

Synthesis

Multistep organometallic synthesis of Ru(III) and half-sandwich Ru(II)–arene Schiff-base complexes.

03

Characterisation

¹H/¹³C NMR · FT-IR · UV-Vis · Single-crystal X-ray (where applicable) · CHNS elemental analysis.

04

Computational Analysis

DFT geometry optimisation, HOMO–LUMO, MEP maps, and molecular docking in GAUSSIAN / AutoDock.

05

Biological Evaluation

Anticancer, antimycobacterial, antioxidant assays; DNA binding kinetics via fluorescence.

06

Structure–Activity Interpretation

SAR analysis correlates substituent effects with potency to guide the next synthetic cycle.

From Prediction to Discovery

AI-Assisted Materials Discovery Workflow

01

Computational Library Design

Generate virtual libraries of metal–ligand systems and functional nanomaterials using cheminformatics and Python-based computational workflows.

02

Quantum Chemical Modelling

Perform DFT calculations to obtain electronic descriptors including HOMO–LUMO energies, electrostatic potential surfaces, and global reactivity descriptors.

03

AI-Assisted Materials Screening

Apply machine learning models to predict structure–property relationships, rank candidates, and identify the most promising systems.

04

Experimental Validation

Synthesize and characterize the highest-ranked candidates to verify computational predictions using spectroscopic and structural techniques.

05

Structure–Property Analysis

Correlate computational descriptors with experimental performance to understand the relationship between electronic structure and material functionality.

06

Iterative Materials Discovery

Incorporate experimental results into computational models to continuously improve prediction accuracy and guide the next generation of functional materials.

Designing next-generation functional materials through chemistry, computation, and artificial intelligence.

Dr. Farha Arshi

Synthetic & Computational Chemist