Mohin Sapara

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...

In 2025, my interaction with artificial intelligence transitioned from occasional consultation to sustained intellectual collaboration. This post documents that trajectory using structured metrics, annotated summaries, and reflective analysis, treating AI usage as a measurable component of scientific skill development rather than a casual productivity aid. 1. Temporal Scope and Engagement Intensity Metric Scientifically Interpretable Value Annotation Calendar window January–December 2025 Continuous annual engagement, not episodic usage Total active days Multi-month distributed activity Indicates integration into routine research workflow Session depth High (20–30+ conversational turns/session) Reflects iterative hypothesis refinement rather than query–response use Cumulative active time Equivalent to several full working weeks Comparable to time invested in a structured training module Interpretation: Engagement patterns resemble supervised intelle...