Freelance – Cheminformatics & Cardiotoxicity Assessment Expert

Freelance Researcher – QSPR \& Topological Index Modelling for Cardiotoxicity Assessment

· Lead the design and execution of a QSPR and graph\-theoretical modelling framework to investigate cardiotoxicity and autonomic (HRV\-related) effects of anticancer drugs.

· Extend and adapt previously established degree\-based topological index methodologies to cardiovascular risk endpoints while maintaining methodological consistency.

· Identify structural determinants and graph\-theoretical patterns associated with cardiotoxicity, QT prolongation, autonomic imbalance, and related risks.

· Produce statistically rigorous, interpretable, and publication\-ready QSPR models and analyses.

· Master’s or Ph.D. in Chemistry, Pharmaceutical Sciences, Computational Chemistry, Cheminformatics, Mathematical Chemistry, Biostatistics, or a closely related field.

· Strong academic or applied research background in QSPR/QSAR modelling, molecular graph theory, or computational drug safety assessment.

· Demonstrated experience working with small datasets (HDLSS settings) and rigorous validation methodologies.

· Cheminformatics \& Molecular Modelling: RDKit, PaDEL\-Descriptor, ChemDraw, ChemAxon, SMILES handling, 2D/3D molecular representations

· Topological \& Graph\-Based Descriptors: Zagreb indices (M1, M2, Hyper Zagreb), Randic, ABC, GA, Harmonic, Balaban J indices, Degree\-based and edge\-partition descriptors

· Statistical \& ML Modelling: Python (NumPy, Pandas, scikit\-learn), R (stats, caret), Linear regression, MLR, ANN\-based QSPR

· Data Analysis \& Visualization: Correlation analysis (Pearson, Spearman), Heatmaps, regression diagnostics, residual plots

· 2\+ years of hands\-on experience in QSPR/QSAR modelling, cheminformatics, or molecular descriptor analysis.

· Prior work involving: Degree\-based or graph\-theoretical molecular descriptors, Regression\-based property or toxicity prediction, Cardiovascular, ADMET, or safety\-related endpoints (preferred)

· Experience preparing academically structured reports suitable for journal submission.

· Strong understanding of molecular graph theory and its application to chemical property prediction.

· Expertise in descriptor–property correlation analysis and multicollinearity diagnostics (VIF).

· Ability to work within high\-dimensional, low\-sample\-size (HDLSS) constraints using LOOCV or equivalent techniques.

· Knowledge of cardiotoxicity mechanisms, including QT prolongation, arrhythmia risk, hERG inhibition, and autonomic dysregulation (HRV).

· Strong analytical writing skills with the ability to deliver mechanistically interpretable QSPR insights.

· Construct a structured chemical dataset for selected anticancer drugs, including SMILES, physicochemical descriptors, and degree\-based topological indices.

· Compute mandatory topological indices (M1, M2, Randic, ABC, GA, Harmonic, Balaban J, Hyper Zagreb, etc.) with raw and normalized values.

· Extract, curate, and score cardiotoxicity and autonomic (HRV\-related) endpoints from literature sources.

· Perform comprehensive descriptor–endpoint correlation analysis, including Pearson, Spearman, heatmaps, and VIF checks.

· Develop and evaluate: Simple linear regression models, Multiple linear regression (MLR) models, ANN\-based QSPR models consistent with prior methodology

· Validate models using R², Q², RMSE, MAE, residual analysis, and outlier diagnostics.

· Interpret results to identify: Structural alerts for cardiotoxicity, Graph\-theoretical patterns linked to HRV suppression, Cross\-class trends among anthracyclines, TKIs, antimetabolites, and biologics

· Prepare high\-quality figures, tables, and visualizations suitable for publication.

· Deliver a 12–page, publication\-ready technical report with full methodological transparency and reproducibility.

• *Contact person:**

Gray\-95661 33822

Job Types: Part\-time, Freelance, Volunteer

Contract length: 1 month

Pay: From ₹10,000\.00 per month

Work Location: Remote