1. Introduction and Motivation Quantitative Structure–Activity Relationship (QSAR) modeling represents one of the earliest and most enduring attempts to formalize the relationship between chemical structure and biological or physicochemical activity. At its core, QSAR is founded on a deceptively simple premise: that measurable properties derived from molecular structure encode information relevant to how a compound behaves in a given experimental or biological context. Despite its long history, QSAR remains highly relevant in contemporary computational chemistry, cheminformatics, and early-stage drug discovery, particularly as a baseline framework against which more complex machine-learning approaches are evaluated. However, while the conceptual foundations of QSAR are widely taught, the practical construction of a QSAR pipeline that is methodologically sound, reproducible, and diagnostically transparent is far less frequently demonstrated in a complete and auditable manner. Many publi...
This article is a reflective and technical account of how my understanding and practical usage of Python , R , RStudio , and bioinformatics workflows has evolved over time. It is written deliberately without hype. The goal is not to exaggerate proficiency, but to document growth, limitations, scale of data handled, and a statistically reasoned trajectory of where my computational capacity is realistically headed over the next three years. 1. Starting Point: Computational Literacy, Not Expertise My initial engagement with programming languages was not as a formally trained computer scientist, but as a biomedical researcher responding to data pressure. Early usage of R and Python was functional and problem-driven: plotting figures, running basic statistics, reshaping tables, and automating repetitive tasks. I did not begin with algorithmic depth; I began with necessity. At this stage, my interaction with code was characterized by: Script reuse with modification Heavy reli...